\n",
+ "
\n",
+ " "
+ ],
+ "text/plain": [
+ " id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 "
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Visualize preprocessing result:\n",
+ "parse_string = lambda x: [string[1:-1] for string in x[1:-1].split(', ')]\n",
+ "alltrain_texts.entities = alltrain_texts.entities.apply(parse_string)\n",
+ "alltrain_texts.text_split = alltrain_texts.text_split.apply(parse_string)\n",
+ "\n",
+ "alltrain_texts.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jZ-g9VB3UxuM"
+ },
+ "outputs": [],
+ "source": [
+ "# Same for testing:\n",
+ "test_texts.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DRFI_KLMUxuN"
+ },
+ "source": [
+ "# Preparing cross validation\n",
+ "\n",
+ "Generate proper folds so that the essays we use in each fold have roughly the same number of discourse types overall.\n",
+ "Only compute if we don't have the file in directory already."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "IOkXJ5UTUxuN"
+ },
+ "outputs": [],
+ "source": [
+ "if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \n",
+ " # Transform categorical labels to dummy variables. Group by id. Sum over dummy. \n",
+ " dfx = pd.get_dummies(df_alltrain, columns=[\"discourse_type\"]).groupby([\"id\"], as_index=False).sum()\n",
+ "\n",
+ " # Generate name for the dummy columns\n",
+ " dummy_cols = [c for c in dfx.columns if c.startswith(\"discourse_type_\") or c == \"id\" and c != \"discourse_type_num\"]\n",
+ " # dfx is now only the dataset with dummy columns selected: don't need to pass the data to do the splits\n",
+ " dfx = dfx[dummy_cols]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "a1AuTst8UxuN"
+ },
+ "outputs": [],
+ "source": [
+ "if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \n",
+ " # Generate cross validation object\n",
+ " mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+ "\n",
+ " # Extract labels\n",
+ " labels = [c for c in dfx.columns if c != \"id\"]\n",
+ " dfx_labels = dfx[labels]\n",
+ "\n",
+ " # Dummy kfold assignment\n",
+ " dfx[\"kfold\"] = -1\n",
+ "\n",
+ " # Split\n",
+ " for fold, (trn_, val_) in enumerate(mskf.split(dfx, dfx_labels)):\n",
+ " print(len(trn_), len(val_))\n",
+ " \n",
+ " # Change the value of the kfold column at the validation index to the value of the fold\n",
+ " # This will tell us when to use the current entry in the validation set\n",
+ " dfx.loc[val_, \"kfold\"] = fold\n",
+ "\n",
+ " # merge back to original dataframe\n",
+ " alltrain_texts = alltrain_texts.merge(dfx[[\"id\", \"kfold\"]], on=\"id\", how=\"left\")\n",
+ " print(alltrain_texts.kfold.value_counts())\n",
+ "\n",
+ " # Save so next time we import it directly\n",
+ " alltrain_texts.to_csv(f\"{HyperParameters.data_dir}/train_folds.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "K7fcRANPUxuO"
+ },
+ "source": [
+ "# Model and Dataset classes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7gDGkTBtUxuO"
+ },
+ "source": [
+ "### Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "APIkM7YwUxuO"
+ },
+ "outputs": [],
+ "source": [
+ "# need help with this\n",
+ "class FeedbackPrizeDataset(Dataset):\n",
+ " def __init__(self, dataframe, tokenizer, max_len, has_labels):\n",
+ " self.len = len(dataframe)\n",
+ " self.data = dataframe\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_len = max_len\n",
+ " self.has_labels = has_labels\n",
+ " \n",
+ " def __getitem__(self, index):\n",
+ " text = self.data.text[index]\n",
+ " encoding = self.tokenizer(\n",
+ " text.split(),\n",
+ " is_split_into_words = True,\n",
+ " padding = 'max_length',\n",
+ " truncation = True,\n",
+ " max_length = self.max_len\n",
+ " )\n",
+ " word_ids = encoding.word_ids()\n",
+ "\n",
+ " # targets\n",
+ " if self.has_labels:\n",
+ " word_labels = self.data.entities[index]\n",
+ " prev_word_idx = None\n",
+ " labels_ids = []\n",
+ " for word_idx in word_ids:\n",
+ " if word_idx is None:\n",
+ " labels_ids.append(IGNORE_INDEX)\n",
+ " elif word_idx != prev_word_idx:\n",
+ " labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\n",
+ " else:\n",
+ " if HyperParameters.label_subtokens:\n",
+ " labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\n",
+ " else:\n",
+ " labels_ids.append(IGNORE_INDEX)\n",
+ " prev_word_idx = word_idx\n",
+ " encoding['labels'] = labels_ids\n",
+ " # convert to torch.tensor\n",
+ " item = {k: torch.as_tensor(v) for k, v in encoding.items()}\n",
+ " word_ids2 = [w if w is not None else NON_LABEL for w in word_ids]\n",
+ " item['word_ids'] = torch.as_tensor(word_ids2)\n",
+ " return item\n",
+ "\n",
+ " def __len__(self):\n",
+ " return self.len"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eE6pC1LdUxuP"
+ },
+ "source": [
+ "### Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "X9UQQRt2UxuP"
+ },
+ "outputs": [],
+ "source": [
+ "class FeedbackModel(nn.Module):\n",
+ " def __init__(self):\n",
+ " super(FeedbackModel, self).__init__()\n",
+ " \n",
+ " # init config of transformer model of choice:\n",
+ " # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\n",
+ " model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\n",
+ " self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\n",
+ " \n",
+ " # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\n",
+ " self.model_config = model_config\n",
+ " self.dropout1 = nn.Dropout(0.1)\n",
+ " self.dropout2 = nn.Dropout(0.2)\n",
+ " self.dropout3 = nn.Dropout(0.3)\n",
+ " self.dropout4 = nn.Dropout(0.4)\n",
+ " self.dropout5 = nn.Dropout(0.5)\n",
+ " self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\n",
+ " \n",
+ " def forward(self, input_ids, mask):\n",
+ " x = self.backbone(input_ids, mask)\n",
+ " logits1 = self.head(self.dropout1(x[0]))\n",
+ " logits2 = self.head(self.dropout2(x[0]))\n",
+ " logits3 = self.head(self.dropout3(x[0]))\n",
+ " logits4 = self.head(self.dropout4(x[0]))\n",
+ " logits5 = self.head(self.dropout5(x[0]))\n",
+ " logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\n",
+ " return logits"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "RslNIsyMUxuP"
+ },
+ "outputs": [],
+ "source": [
+ "def build_model_tokenizer():\n",
+ " tokenizer = AutoTokenizer.from_pretrained(HyperParameters.model_name, add_prefix_space = True)\n",
+ " model = FeedbackModel()\n",
+ " return model, tokenizer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PriptyGpUxuP"
+ },
+ "source": [
+ "### Utilities"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CmRQM_TPUxuQ"
+ },
+ "source": [
+ "What does this do?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "_CJQ0r-yUxuQ"
+ },
+ "outputs": [],
+ "source": [
+ "# Need help with this: used in training to transform raw logits to labels needed\n",
+ "def active_logits(raw_logits, word_ids):\n",
+ " word_ids = word_ids.view(-1)\n",
+ " active_mask = word_ids.unsqueeze(1).expand(word_ids.shape[0], HyperParameters.num_labels)\n",
+ " active_mask = active_mask != NON_LABEL\n",
+ " active_logits = raw_logits.view(-1, HyperParameters.num_labels)\n",
+ " active_logits = torch.masked_select(active_logits, active_mask) # return 1dTensor\n",
+ " active_logits = active_logits.view(-1, HyperParameters.num_labels) \n",
+ " return active_logits\n",
+ "\n",
+ "def active_labels(labels):\n",
+ " active_mask = labels.view(-1) != IGNORE_INDEX\n",
+ " active_labels = torch.masked_select(labels.view(-1), active_mask)\n",
+ " return active_labels\n",
+ "\n",
+ "def active_preds_prob(active_logits):\n",
+ " active_preds = torch.argmax(active_logits, axis = 1)\n",
+ " active_preds_prob, _ = torch.max(active_logits, axis = 1)\n",
+ " return active_preds, active_preds_prob"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YkokQcdsUxuQ"
+ },
+ "source": [
+ "F1 scoring functions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "SfhEUmMcUxuQ"
+ },
+ "outputs": [],
+ "source": [
+ "def calculate_overlap(set_pred, set_gt):\n",
+ " \"\"\"\n",
+ " Calculates if the overlap between prediction and\n",
+ " ground truth is enough fora potential True positive\n",
+ " \"\"\"\n",
+ " # Length of each and intersection\n",
+ " try:\n",
+ " len_gt = len(set_gt)\n",
+ " len_pred = len(set_pred)\n",
+ " inter = len(set_gt & set_pred)\n",
+ " overlap_1 = inter / len_gt\n",
+ " overlap_2 = inter/ len_pred\n",
+ " return overlap_1 >= 0.5 and overlap_2 >= 0.5\n",
+ " except: # at least one of the input is NaN\n",
+ " return False\n",
+ "\n",
+ "def score_feedback_comp_micro(pred_df, gt_df, discourse_type):\n",
+ " \"\"\"\n",
+ " A function that scores for the kaggle\n",
+ " Student Writing Competition\n",
+ " \n",
+ " Uses the steps in the evaluation page here:\n",
+ " https://www.kaggle.com/c/feedback-prize-2021/overview/evaluation\n",
+ " \"\"\"\n",
+ " gt_df = gt_df.loc[gt_df['discourse_type'] == discourse_type, \n",
+ " ['id', 'predictionstring']].reset_index(drop=True)\n",
+ " pred_df = pred_df.loc[pred_df['class'] == discourse_type,\n",
+ " ['id', 'predictionstring']].reset_index(drop=True)\n",
+ " pred_df['pred_id'] = pred_df.index\n",
+ " gt_df['gt_id'] = gt_df.index\n",
+ " pred_df['predictionstring'] = [set(pred.split(' ')) for pred in pred_df['predictionstring']]\n",
+ " gt_df['predictionstring'] = [set(pred.split(' ')) for pred in gt_df['predictionstring']]\n",
+ " \n",
+ " # Step 1. all ground truths and predictions for a given class are compared.\n",
+ " joined = pred_df.merge(gt_df,\n",
+ " left_on='id',\n",
+ " right_on='id',\n",
+ " how='outer',\n",
+ " suffixes=('_pred','_gt')\n",
+ " )\n",
+ " overlaps = [calculate_overlap(*args) for args in zip(joined.predictionstring_pred, \n",
+ " joined.predictionstring_gt)]\n",
+ " \n",
+ " # 2. If the overlap between the ground truth and prediction is >= 0.5, \n",
+ " # and the overlap between the prediction and the ground truth >= 0.5,\n",
+ " # the prediction is a match and considered a true positive.\n",
+ " # If multiple matches exist, the match with the highest pair of overlaps is taken.\n",
+ " # we don't need to compute the match to compute the score\n",
+ " TP = joined.loc[overlaps]['gt_id'].nunique()\n",
+ "\n",
+ " # 3. Any unmatched ground truths are false negatives\n",
+ " # and any unmatched predictions are false positives.\n",
+ " TPandFP = len(pred_df)\n",
+ " TPandFN = len(gt_df)\n",
+ " \n",
+ " #calc microf1\n",
+ " my_f1_score = 2*TP / (TPandFP + TPandFN)\n",
+ " return my_f1_score\n",
+ "\n",
+ "def score_feedback_comp(pred_df, gt_df, return_class_scores=False):\n",
+ " \"\"\"\n",
+ " Final helper function for model evaluation.\n",
+ " \n",
+ " Args:\n",
+ " pred_df (pandas.DataFrame): dataframe containing model predictions. Needs to have columns: ['id','class','predictionstring']\n",
+ " gt_df (pandas.DataFrame): dataframe of ground truth used for model training\n",
+ " return_class_scores (bool): Boolean indicating if we want to return the F1 score for each predicted class.\n",
+ " \n",
+ " Returns:\n",
+ " f1 (float): F1 score of the model\n",
+ " (optional) class_scores (dict): Dictionary of per-class F1 score\n",
+ " \"\"\"\n",
+ " class_scores = {}\n",
+ " for discourse_type in gt_df.discourse_type.unique():\n",
+ " class_score = score_feedback_comp_micro(pred_df, gt_df, discourse_type)\n",
+ " class_scores[discourse_type] = class_score\n",
+ " f1 = np.mean([v for v in class_scores.values()])\n",
+ " if return_class_scores:\n",
+ " return f1, class_scores\n",
+ " return f1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IIQdqtzoUxuR"
+ },
+ "source": [
+ "# Training and validation functions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7GrUCjcbUxuR"
+ },
+ "source": [
+ "### Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WSeGDvufUxuR"
+ },
+ "outputs": [],
+ "source": [
+ "def train_fn(model, train_data_loader, optimizer, epoch, criterion):\n",
+ " model.train()\n",
+ " train_loss = 0\n",
+ " train_accuracy = 0\n",
+ " stream = tqdm(train_data_loader)\n",
+ " # Init gradscaler to ensure everything works smoothly on cuda\n",
+ " scaler = GradScaler()\n",
+ "\n",
+ " for batch_idx, batch in enumerate(stream, start = 1):\n",
+ " ids = batch['input_ids'].to(device, dtype = torch.long)\n",
+ " mask = batch['attention_mask'].to(device, dtype = torch.long)\n",
+ " raw_labels = batch['labels'].to(device, dtype = torch.long)\n",
+ " word_ids = batch['word_ids'].to(device, dtype = torch.long)\n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # Calculate output with autocast for cuda support\n",
+ " with autocast():\n",
+ " raw_logits = model(input_ids = ids, mask = mask)\n",
+ " \n",
+ " logits = active_logits(raw_logits, word_ids)\n",
+ " labels = active_labels(raw_labels)\n",
+ " sf_logits = torch.softmax(logits, dim=-1)\n",
+ " preds, preds_prob = active_preds_prob(sf_logits)\n",
+ " train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\n",
+ " criterion = nn.CrossEntropyLoss()\n",
+ " loss = criterion(logits, labels)\n",
+ "\n",
+ " scaler.scale(loss).backward()\n",
+ " scaler.step(optimizer)\n",
+ " scaler.update()\n",
+ " train_loss += loss.item()\n",
+ " \n",
+ " if batch_idx % HyperParameters.verbose_steps == 0:\n",
+ " loss_step = train_loss / batch_idx\n",
+ " print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\n",
+ " \n",
+ " epoch_loss = train_loss / batch_idx\n",
+ " epoch_accuracy = train_accuracy / batch_idx \n",
+ " # Cleanup\n",
+ " del train_data_loader, raw_logits, logits, raw_labels, preds, labels\n",
+ " torch.cuda.empty_cache()\n",
+ " gc.collect()\n",
+ " \n",
+ " print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\n",
+ " print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fRR3l8npUxuR"
+ },
+ "source": [
+ "### Validate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Mj0dSxx0UxuR"
+ },
+ "outputs": [],
+ "source": [
+ "def valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion):\n",
+ " oof, valid_loss, valid_acc = get_preds_onefold(model, df_val, dl_val, criterion, valid_flg=True)\n",
+ " f1score =[]\n",
+ " # classes = oof['class'].unique()\n",
+ " classes = ['Lead', 'Position', 'Claim','Counterclaim', 'Rebuttal','Evidence','Concluding Statement']\n",
+ " print(f\"Validation F1 scores\")\n",
+ "\n",
+ " for c in classes:\n",
+ " pred_df = oof.loc[oof['class'] == c].copy()\n",
+ " gt_df = df_val_eval.loc[df_val_eval['discourse_type'] == c].copy()\n",
+ " f1 = score_feedback_comp(pred_df, gt_df)\n",
+ " print(f' * {c:<10}: {f1:4f}')\n",
+ " f1score.append(f1)\n",
+ " f1avg = np.mean(f1score)\n",
+ " print(f'Overall Validation avg F1: {f1avg:.4f} val_loss:{valid_loss:.4f} val_accuracy:{valid_acc:.4f}')\n",
+ " return valid_loss, oof"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xSZ3iq8dUxuS"
+ },
+ "source": [
+ "### Infer on validation data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3qtgxPTlUxuS"
+ },
+ "outputs": [],
+ "source": [
+ "def inference(model, data_loader, criterion, valid_flg):\n",
+ " stream = tqdm(data_loader)\n",
+ " model.eval()\n",
+ " \n",
+ " valid_loss = 0\n",
+ " valid_accuracy = 0\n",
+ " all_logits = None\n",
+ " for batch_idx, batch in enumerate(stream, start = 1):\n",
+ " ids = batch['input_ids'].to(device, dtype = torch.long)\n",
+ " mask = batch['attention_mask'].to(device, dtype = torch.long)\n",
+ " with torch.no_grad():\n",
+ " raw_logits = model(input_ids=ids, mask = mask)\n",
+ " del ids, mask\n",
+ " \n",
+ " word_ids = batch['word_ids'].to(device, dtype = torch.long)\n",
+ " logits = active_logits(raw_logits, word_ids)\n",
+ " sf_logits = torch.softmax(logits, dim= -1)\n",
+ " sf_raw_logits = torch.softmax(raw_logits, dim=-1)\n",
+ " if valid_flg: \n",
+ " raw_labels = batch['labels'].to(device, dtype = torch.long)\n",
+ " labels = active_labels(raw_labels)\n",
+ " preds, preds_prob = active_preds_prob(sf_logits)\n",
+ " valid_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\n",
+ " loss = criterion(logits, labels)\n",
+ " valid_loss += loss.item()\n",
+ " \n",
+ " if batch_idx == 1:\n",
+ " all_logits = sf_raw_logits.cpu().numpy()\n",
+ " else:\n",
+ " all_logits = np.append(all_logits, sf_raw_logits.cpu().numpy(), axis=0)\n",
+ "\n",
+ " \n",
+ " if valid_flg: \n",
+ " epoch_loss = valid_loss / batch_idx\n",
+ " epoch_accuracy = valid_accuracy / batch_idx\n",
+ " else:\n",
+ " epoch_loss, epoch_accuracy = 0, 0\n",
+ " return all_logits, epoch_loss, epoch_accuracy\n",
+ "\n",
+ "\n",
+ "def preds_class_prob(all_logits, data_loader):\n",
+ " print(\"predict target class and its probabilty\")\n",
+ " final_predictions = []\n",
+ " final_predictions_score = []\n",
+ " stream = tqdm(data_loader)\n",
+ " len_sample = all_logits.shape[0]\n",
+ "\n",
+ " for batch_idx, batch in enumerate(stream, start=0):\n",
+ " for minibatch_idx in range(HyperParameters.valid_batch_size):\n",
+ " sample_idx = int(batch_idx * HyperParameters.valid_batch_size + minibatch_idx)\n",
+ " if sample_idx > len_sample - 1 : break\n",
+ " word_ids = batch['word_ids'][minibatch_idx].numpy()\n",
+ " predictions =[]\n",
+ " predictions_prob = []\n",
+ " pred_class_id = np.argmax(all_logits[sample_idx], axis=1)\n",
+ " pred_score = np.max(all_logits[sample_idx], axis=1)\n",
+ " pred_class_labels = [IDS_TO_LABELS[i] for i in pred_class_id]\n",
+ " prev_word_idx = -1\n",
+ " for idx, word_idx in enumerate(word_ids):\n",
+ " if word_idx == -1:\n",
+ " pass\n",
+ " elif word_idx != prev_word_idx:\n",
+ " predictions.append(pred_class_labels[idx])\n",
+ " predictions_prob.append(pred_score[idx])\n",
+ " prev_word_idx = word_idx\n",
+ " final_predictions.append(predictions)\n",
+ " final_predictions_score.append(predictions_prob)\n",
+ " return final_predictions, final_predictions_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "E5dGJEzDUxuS"
+ },
+ "outputs": [],
+ "source": [
+ "def get_preds_onefold(model, df, dl, criterion, valid_flg):\n",
+ " logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\n",
+ " all_preds, all_preds_prob = preds_class_prob(logits, dl)\n",
+ " df_pred = post_process_pred(df, all_preds, all_preds_prob)\n",
+ " return df_pred, valid_loss, valid_acc\n",
+ "\n",
+ "def get_preds_folds(model, df, dl, criterion, valid_flg=False):\n",
+ " for i_fold in range(HyperParameters.n_fold):\n",
+ " model_filename = os.path.join(HyperParameters.model_dir, f\"{HyperParameters.model_savename}_{i_fold}.bin\")\n",
+ " print(f\"{model_filename} inference\")\n",
+ " model = model.to(device)\n",
+ " model.load_state_dict(torch.load(model_filename))\n",
+ " logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\n",
+ " if i_fold == 0:\n",
+ " avg_pred_logits = logits\n",
+ " else:\n",
+ " avg_pred_logits += logits\n",
+ " avg_pred_logits /= HyperParameters.n_fold\n",
+ " all_preds, all_preds_prob = preds_class_prob(avg_pred_logits, dl)\n",
+ " df_pred = post_process_pred(df, all_preds, all_preds_prob)\n",
+ " return df_pred\n",
+ "\n",
+ "def post_process_pred(df, all_preds, all_preds_prob):\n",
+ " final_preds = []\n",
+ " for i in range(len(df)):\n",
+ " idx = df.id.values[i]\n",
+ " pred = all_preds[i]\n",
+ " pred_prob = all_preds_prob[i]\n",
+ " j = 0\n",
+ " while j < len(pred):\n",
+ " cls = pred[j]\n",
+ " if cls == 'O': j += 1\n",
+ " else: cls = cls.replace('B', 'I')\n",
+ " end = j + 1\n",
+ " while end < len(pred) and pred[end] == cls:\n",
+ " end += 1\n",
+ " if cls != 'O' and cls !='':\n",
+ " avg_score = np.mean(pred_prob[j:end])\n",
+ " if end - j > MIN_THRESH[cls] and avg_score > PROB_THRESH[cls]:\n",
+ " final_preds.append((idx, cls.replace('I-', ''), ' '.join(map(str, list(range(j, end))))))\n",
+ " j = end\n",
+ " df_pred = pd.DataFrame(final_preds)\n",
+ " df_pred.columns = ['id', 'class', 'predictionstring']\n",
+ " return df_pred"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NAmJyKnHUxuS"
+ },
+ "source": [
+ "# Finally getting some action"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7dHGvjpPUxuS"
+ },
+ "outputs": [],
+ "source": [
+ "oof = pd.DataFrame()\n",
+ "\n",
+ "for i_fold in range(HyperParameters.n_fold):\n",
+ " print(f'=== fold{i_fold} training ===')\n",
+ " model, tokenizer = build_model_tokenizer()\n",
+ " model = model.to(device)\n",
+ " optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\n",
+ " \n",
+ " df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\n",
+ " ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\n",
+ " df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\n",
+ " val_idlist = df_val['id'].unique().tolist()\n",
+ " df_val_eval = df_alltrain.query('id==@val_idlist').reset_index(drop=True)\n",
+ " ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\n",
+ " dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\n",
+ " dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\n",
+ "\n",
+ " best_val_loss = np.inf\n",
+ " criterion = nn.CrossEntropyLoss()\n",
+ "\n",
+ " for epoch in range(1, HyperParameters.n_epoch + 1):\n",
+ " train_fn(model, dl_train, optimizer, epoch, criterion)\n",
+ " valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\n",
+ " if valid_loss < best_val_loss:\n",
+ " best_val_loss = valid_loss\n",
+ " _oof_fold_best = _oof\n",
+ " _oof_fold_best[\"kfold\"] = i_fold\n",
+ " model_filename = f'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin'\n",
+ " \n",
+ " # Saving the boy\n",
+ " torch.save(model.state_dict(), model_filename)\n",
+ " print(f'{model_filename} saved')\n",
+ "\n",
+ " oof = pd.concat([oof, _oof_fold_best])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hlmZeM47UxuT"
+ },
+ "outputs": [],
+ "source": [
+ "oof.to_csv(f'{HyperParameters.output_dir}/oof_{HyperParameters.name}.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aO8hcaQ8UxuT"
+ },
+ "source": [
+ "Looking at performance:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "VOcFtSUhUxuT"
+ },
+ "outputs": [],
+ "source": [
+ "if HyperParameters.is_debug:\n",
+ " idlist = alltrain_texts['id'].unique().tolist()\n",
+ " df_train = df_alltrain.query('id==@idlist')\n",
+ "else:\n",
+ " df_train = df_alltrain.copy()\n",
+ "print(f'overall cv score: {score_feedback_comp(df_train, oof)}')"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Longformer.ipynb",
+ "provenance": []
+ },
+ "interpreter": {
+ "hash": "be336ab25ba919cf0a65f4be83b938febf47f529e9f75dfb16359f541885e1c6"
+ },
+ "kernelspec": {
+ "display_name": "Python 3.8.12 ('machinevision')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/experiments/federico/model_performance.ipynb b/experiments/federico/model_performance.ipynb
new file mode 100644
index 0000000..9febf1d
--- /dev/null
+++ b/experiments/federico/model_performance.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Performance Metrics\n",
+ "\n",
+ "This notebook includes a set of utility functions (stolen from kaggle), to calculate performance metrics for the model.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# from: https://www.kaggle.com/cpmpml/faster-metric-computation \n",
+ "\n",
+ "def calculate_overlap(set_pred, set_gt):\n",
+ " \"\"\"\n",
+ " Calculates if the overlap between prediction and\n",
+ " ground truth is enough fora potential True positive\n",
+ " \"\"\"\n",
+ " # Length of each and intersection\n",
+ " try:\n",
+ " len_gt = len(set_gt)\n",
+ " len_pred = len(set_pred)\n",
+ " inter = len(set_gt & set_pred)\n",
+ " overlap_1 = inter / len_gt\n",
+ " overlap_2 = inter/ len_pred\n",
+ " return overlap_1 >= 0.5 and overlap_2 >= 0.5\n",
+ " except: # at least one of the input is NaN\n",
+ " return False\n",
+ "\n",
+ "def score_feedback_comp_micro(pred_df, gt_df, discourse_type):\n",
+ " \"\"\"\n",
+ " A function that scores for the kaggle\n",
+ " Student Writing Competition\n",
+ " \n",
+ " Uses the steps in the evaluation page here:\n",
+ " https://www.kaggle.com/c/feedback-prize-2021/overview/evaluation\n",
+ " \"\"\"\n",
+ " gt_df = gt_df.loc[gt_df['discourse_type'] == discourse_type, \n",
+ " ['id', 'predictionstring']].reset_index(drop=True)\n",
+ " pred_df = pred_df.loc[pred_df['class'] == discourse_type,\n",
+ " ['id', 'predictionstring']].reset_index(drop=True)\n",
+ " pred_df['pred_id'] = pred_df.index\n",
+ " gt_df['gt_id'] = gt_df.index\n",
+ " pred_df['predictionstring'] = [set(pred.split(' ')) for pred in pred_df['predictionstring']]\n",
+ " gt_df['predictionstring'] = [set(pred.split(' ')) for pred in gt_df['predictionstring']]\n",
+ " \n",
+ " # Step 1. all ground truths and predictions for a given class are compared.\n",
+ " joined = pred_df.merge(gt_df,\n",
+ " left_on='id',\n",
+ " right_on='id',\n",
+ " how='outer',\n",
+ " suffixes=('_pred','_gt')\n",
+ " )\n",
+ " overlaps = [calculate_overlap(*args) for args in zip(joined.predictionstring_pred, \n",
+ " joined.predictionstring_gt)]\n",
+ " \n",
+ " # 2. If the overlap between the ground truth and prediction is >= 0.5, \n",
+ " # and the overlap between the prediction and the ground truth >= 0.5,\n",
+ " # the prediction is a match and considered a true positive.\n",
+ " # If multiple matches exist, the match with the highest pair of overlaps is taken.\n",
+ " # we don't need to compute the match to compute the score\n",
+ " TP = joined.loc[overlaps]['gt_id'].nunique()\n",
+ "\n",
+ " # 3. Any unmatched ground truths are false negatives\n",
+ " # and any unmatched predictions are false positives.\n",
+ " TPandFP = len(pred_df)\n",
+ " TPandFN = len(gt_df)\n",
+ " \n",
+ " #calc microf1\n",
+ " my_f1_score = 2*TP / (TPandFP + TPandFN)\n",
+ " return my_f1_score\n",
+ "\n",
+ "def score_feedback_comp(pred_df, gt_df, return_class_scores=False):\n",
+ " \"\"\"\n",
+ " Helper function for model evaluation.\n",
+ " \n",
+ " Args:\n",
+ " pred_df (pandas.DataFrame): dataframe containing model predictions. Needs to have columns: ['id','class','predictionstring']\n",
+ " gt_df (pandas.DataFrame): dataframe of ground truth used for model training\n",
+ " return_class_scores (bool): Boolean indicating if we want to return the F1 score for each predicted class.\n",
+ " \n",
+ " Returns:\n",
+ " f1 (float): F1 score of the model\n",
+ " (optional) class_scores (dict): Dictionary of per-class F1 score\n",
+ " \"\"\"\n",
+ " class_scores = {}\n",
+ " for discourse_type in gt_df.discourse_type.unique():\n",
+ " class_score = score_feedback_comp_micro(pred_df, gt_df, discourse_type)\n",
+ " class_scores[discourse_type] = class_score\n",
+ " f1 = np.mean([v for v in class_scores.values()])\n",
+ " if return_class_scores:\n",
+ " return f1, class_scores\n",
+ " return f1\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.0"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Importing the data and scoring \n",
+ "train_df = pd.read_csv('./data/train.csv')\n",
+ "pred_df = train_df[['id','discourse_type','predictionstring']].copy()\n",
+ "pred_df.columns = ['id','class','predictionstring']\n",
+ "\n",
+ "# Here scoring the train data onto itself\n",
+ "score_feedback_comp(pred_df, train_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Here scoring a sample of it\n",
+ "pred_df2 = pred_df.sample(frac=0.7).reset_index(drop=True)\n",
+ "score_feedback_comp(pred_df2, train_df)"
+ ]
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "be336ab25ba919cf0a65f4be83b938febf47f529e9f75dfb16359f541885e1c6"
+ },
+ "kernelspec": {
+ "display_name": "Python 3.8.12 ('machinevision')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/experiments/federico/requirements.txt b/experiments/federico/requirements.txt
new file mode 100644
index 0000000..e69de29
diff --git a/experiments/federico/stratified_k_folds.ipynb b/experiments/federico/stratified_k_folds.ipynb
new file mode 100644
index 0000000..cbc47e9
--- /dev/null
+++ b/experiments/federico/stratified_k_folds.ipynb
@@ -0,0 +1,189 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48526e00",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Note: this notebook is a commented version of a notebook shared on kaggle!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "56d03522",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-01-11T08:12:04.265417Z",
+ "iopub.status.busy": "2022-01-11T08:12:04.264529Z",
+ "iopub.status.idle": "2022-01-11T08:12:05.284426Z",
+ "shell.execute_reply": "2022-01-11T08:12:05.284875Z",
+ "shell.execute_reply.started": "2022-01-11T08:10:56.674544Z"
+ },
+ "papermill": {
+ "duration": 1.029031,
+ "end_time": "2022-01-11T08:12:05.285054",
+ "exception": false,
+ "start_time": "2022-01-11T08:12:04.256023",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "from sklearn.model_selection import KFold\n",
+ "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "80efa5b7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# installation: pip install iterative-stratification"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a4cd849f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-01-11T08:12:05.300983Z",
+ "iopub.status.busy": "2022-01-11T08:12:05.300287Z",
+ "iopub.status.idle": "2022-01-11T08:12:09.902357Z",
+ "shell.execute_reply": "2022-01-11T08:12:09.901403Z",
+ "shell.execute_reply.started": "2022-01-11T08:11:26.601473Z"
+ },
+ "papermill": {
+ "duration": 4.613868,
+ "end_time": "2022-01-11T08:12:09.902499",
+ "exception": false,
+ "start_time": "2022-01-11T08:12:05.288631",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# read data\n",
+ "df = pd.read_csv(\"/Users/fededagos/Dropbox/NLP project/data/train.csv\")\n",
+ "\n",
+ "# Transform categorical labels to dummy variables. Group by id. Sum over dummy. \n",
+ "dfx = pd.get_dummies(df, columns=[\"discourse_type\"]).groupby([\"id\"], as_index=False).sum()\n",
+ "\n",
+ "# Generate name for the dummy columns\n",
+ "dummy_cols = [c for c in dfx.columns if c.startswith(\"discourse_type_\") or c == \"id\" and c != \"discourse_type_num\"]\n",
+ "# dfx is now only the dataset with dummy columns selected: don't need to pass the data to do the splits\n",
+ "dfx = dfx[dummy_cols]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0fe70e2c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# At this point dfx is a dataframe with ids, counting how many discourse types are present in that id!\n",
+ "# (Each id is a file (15594))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "ab16653b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "12477 3117\n",
+ "12474 3120\n",
+ "12475 3119\n",
+ "12475 3119\n",
+ "12475 3119\n",
+ "0 28997\n",
+ "2 28968\n",
+ "3 28904\n",
+ "1 28737\n",
+ "4 28687\n",
+ "Name: kfold, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Generate cross validation object\n",
+ "mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+ "\n",
+ "# Extract labels\n",
+ "labels = [c for c in dfx.columns if c != \"id\"]\n",
+ "dfx_labels = dfx[labels]\n",
+ "\n",
+ "# Dummy kfold assignment\n",
+ "dfx[\"kfold\"] = -1\n",
+ "\n",
+ "# Split\n",
+ "for fold, (trn_, val_) in enumerate(mskf.split(dfx, dfx_labels)):\n",
+ " print(len(trn_), len(val_))\n",
+ " \n",
+ " # Change the value of the kfold column at the validation index to the value of the fold\n",
+ " # This will tell us when to use the current entry in the validation set\n",
+ " dfx.loc[val_, \"kfold\"] = fold\n",
+ "\n",
+ "# merge back to original dataframe\n",
+ "df = df.merge(dfx[[\"id\", \"kfold\"]], on=\"id\", how=\"left\")\n",
+ "print(df.kfold.value_counts())\n",
+ "df.to_csv(\"train_folds.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "83959ccc",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 25.017425,
+ "end_time": "2022-01-11T08:12:10.615418",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "__notebook__.ipynb",
+ "output_path": "__notebook__.ipynb",
+ "parameters": {},
+ "start_time": "2022-01-11T08:11:45.597993",
+ "version": "2.3.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/qingyu/Adversarial_Attacks.ipynb b/experiments/qingyu/Adversarial_Attacks.ipynb
new file mode 100644
index 0000000..f71878e
--- /dev/null
+++ b/experiments/qingyu/Adversarial_Attacks.ipynb
@@ -0,0 +1,201 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "05312ca4",
+ "metadata": {},
+ "source": [
+ "# Adversarial Attacks Function \n",
+ "## from https://github.com/QData/TextAttack"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "10c7e510",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install tensorflow_text\n",
+ "#!textattack list attack-recipes\n",
+ "#!pip install textattack\n",
+ "from textattack.augmentation import EmbeddingAugmenter,EasyDataAugmenter,WordNetAugmenter,CharSwapAugmenter,CheckListAugmenter,CLAREAugmenter\n",
+ "import time\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "dd07b4c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['I do not understand transformesr']"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "t = \"I do not understand transformers\"\n",
+ "augmenter = CharSwapAugmenter()\n",
+ "augmenter.augment(t)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "301ad56e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['What I not do understand, I can not create.',\n",
+ " 'What I do not understand, I non can not create.']"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "augmenter = EasyDataAugmenter(pct_words_to_swap=0.2, transformations_per_example=2)\n",
+ "s = 'What I do not understand, I can not create.'\n",
+ "augmenter.augment(s)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "8b7201cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# - WordNet synonym replacement\n",
+ "# - Randomly replace words with their synonyms.\n",
+ "# - Word deletion\n",
+ "# - Randomly remove words from the sentence.\n",
+ "# - Word order swaps\n",
+ "# - Randomly swap the position of words in the sentence.\n",
+ "# - Random synonym insertion\n",
+ "# - Insert a random synonym of a random word at a random location.\n",
+ "\n",
+ "\n",
+ "\n",
+ "def create_attacks(df,att_type,**kwargs):\n",
+ " \n",
+ " def easy_data(text):\n",
+ " augmenter = EasyDataAugmenter(kwargs['pct_swap'], kwargs['num_trans'])\n",
+ " return augmenter.augment(text)[0]\n",
+ " \n",
+ " def syno(text):\n",
+ " augmenter = WordNetAugmenter()\n",
+ " return augmenter.augment(text)[0]\n",
+ " \n",
+ " if att_type == \"easy_data\":\n",
+ " df.text = df.text.apply(easy_data)\n",
+ " \n",
+ " elif att_type == \"replace_synonym\":\n",
+ " df.text = df.text.apply(syno)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 134,
+ "id": "acbcdd2b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.6966230869293213\n"
+ ]
+ }
+ ],
+ "source": [
+ "s = time.time()\n",
+ "create_attacks(df_combined.iloc[:5],att_type = \"replace_synonym\")\n",
+ "print(time.time()-s)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc126457",
+ "metadata": {},
+ "source": [
+ "# Prediction_String"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 193,
+ "id": "07669733",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_predStr(df):\n",
+ " assert all(item in list(df) for item in ['label', 'text', 'doc_id']), \"Please use a dataframe with correct columns\"\n",
+ " prediction_strings = []\n",
+ " start_id = 1\n",
+ " prev_doc = df.iloc[0].doc_id\n",
+ " for (label, text, doc_id) in df[['label', 'text', 'doc_id']].itertuples(index=False):\n",
+ " if doc_id != prev_doc:\n",
+ " prev_doc = doc_id\n",
+ " start_id = 0\n",
+ " text_split = text.split()\n",
+ " end_id = start_id + len(text_split)\n",
+ " prediction_strings.append(\n",
+ " [num for num in range(start_id, end_id)]\n",
+ " )\n",
+ " start_id = end_id \n",
+ " df['predictionString'] = prediction_strings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1fddf06d",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "83e54b82",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/qingyu/Baseline.ipynb b/experiments/qingyu/Baseline.ipynb
new file mode 100644
index 0000000..891e65a
--- /dev/null
+++ b/experiments/qingyu/Baseline.ipynb
@@ -0,0 +1,1444 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "id": "b3543ffe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install fastai"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "id": "1470af93",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "fastai version: 2.5.5\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- public imports\n",
+ "\n",
+ "import os\n",
+ "from fastai.text import *\n",
+ "from fastai.text.all import *\n",
+ "import nltk\n",
+ "import spacy\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import fastai\n",
+ "import torch\n",
+ "from tqdm.notebook import tqdm\n",
+ "from sklearn.metrics import classification_report\n",
+ "tqdm.pandas()\n",
+ "print(f\"fastai version: {fastai.__version__}\")\n",
+ "if torch.cuda.is_available():\n",
+ " print(f\"GPU which is used : {torch.cuda.get_device_name(0)}\")\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b7fa0326",
+ "metadata": {},
+ "source": [
+ "# Import Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "id": "5ed43e67",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "base_path = '/Users/feng/downloads'\n",
+ "path = os.path.join(base_path, 'ArgumentAnnotatedEssays-2.0')\n",
+ "text_file_dir = os.path.join(path, 'brat-project-final')\n",
+ "\n",
+ "texts = []\n",
+ "annotated_texts = []\n",
+ "for file in os.listdir(text_file_dir):\n",
+ " essay_num, file_extension = file.split('.')\n",
+ " if file_extension == 'ann':\n",
+ " with open(os.path.join(text_file_dir, file), 'r') as f:\n",
+ " df_temp = pd.read_csv(f, delimiter='\\t', header=None, names=['label_type', 'label', 'text'])\n",
+ " df_temp[['label', 'label_comp1', 'label_comp2']] = df_temp.label.str.split(expand=True)\n",
+ " df_temp['doc_id'] = essay_num\n",
+ " annotated_texts.append(df_temp)\n",
+ " elif file_extension == 'txt':\n",
+ " with open(os.path.join(text_file_dir, file), 'r') as f:\n",
+ " texts.append((essay_num, f.read()))\n",
+ " else:\n",
+ " continue\n",
+ "\n",
+ "df_texts = pd.DataFrame.from_records(texts, columns={'doc_id', 'text'})\n",
+ "df_annotated = pd.concat(annotated_texts)\n",
+ "assert sorted(df_annotated.doc_id.unique()) == sorted(df_texts.doc_id)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "id": "a5ebb2a3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ "\n",
+ " \n",
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+ "
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+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n", + " | id | \n", + "text | \n", + "text_split | \n", + "entities | \n", + "kfold | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "3321A3E87AD3 | \n", + "I do agree that some students would benefit fr... | \n", + "[I, do, agree, that, some, students, would, be... | \n", + "[B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... | \n", + "2 | \n", + "
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+ "\n",
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " label_type label text \\\n",
+ "0 T1 MajorClaim alternative means of transportation and intern... \n",
+ "1 T2 MajorClaim alternative forms of transport and internation... \n",
+ "2 T3 Claim some people claim the convenience of automobile \n",
+ "4 T4 Claim it is crucial to alter automobiles to the othe... \n",
+ "6 T5 Premise an increase in the number of automobiles bring... \n",
+ "\n",
+ " label_comp1 label_comp2 doc_id \n",
+ "0 358 464 essay031 \n",
+ "1 2393 2565 essay031 \n",
+ "2 2325 2372 essay031 \n",
+ "4 822 933 essay031 \n",
+ "6 561 638 essay031 "
+ ]
+ },
+ "execution_count": 140,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ids_argument_segment = df_annotated.label_type.str.startswith('T')\n",
+ "\n",
+ "df_arguments = df_annotated[ids_argument_segment]\n",
+ "\n",
+ "df_arguments.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "id": "8c3f1485",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "label_type object\n",
+ "label object\n",
+ "text object\n",
+ "span_start int64\n",
+ "span_end int64\n",
+ "doc_id object\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 141,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_arguments = df_arguments.rename(columns={'label_comp1':'span_start', 'label_comp2':'span_end'}).astype(\n",
+ " {'span_start': int, 'span_end':int}\n",
+ ")\n",
+ "df_arguments.dtypes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "id": "e4840b26",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "murdering criminals is therefore immoral and hard to accept\n",
+ "\"murdering\" criminals is therefore immoral and hard to accept\n",
+ "\n",
+ "Click is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\"Click\" is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\n",
+ "Blood diamond, an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\"Blood diamond\", an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | label_type | \n", + "label | \n", + "text | \n", + "label_comp1 | \n", + "label_comp2 | \n", + "doc_id | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "T1 | \n", + "MajorClaim | \n", + "alternative means of transportation and intern... | \n", + "358 | \n", + "464 | \n", + "essay031 | \n", + "
| 1 | \n", + "T2 | \n", + "MajorClaim | \n", + "alternative forms of transport and internation... | \n", + "2393 | \n", + "2565 | \n", + "essay031 | \n", + "
| 2 | \n", + "T3 | \n", + "Claim | \n", + "some people claim the convenience of automobile | \n", + "2325 | \n", + "2372 | \n", + "essay031 | \n", + "
| 4 | \n", + "T4 | \n", + "Claim | \n", + "it is crucial to alter automobiles to the othe... | \n", + "822 | \n", + "933 | \n", + "essay031 | \n", + "
| 6 | \n", + "T5 | \n", + "Premise | \n", + "an increase in the number of automobiles bring... | \n", + "561 | \n", + "638 | \n", + "essay031 | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " label_type label text \\\n",
+ "0 O Other Should students be taught to compete or to coo... \n",
+ "1 O Other .\\nFirst of all, \n",
+ "2 O Other . \n",
+ "3 O Other . \n",
+ "4 O Other . \n",
+ "... ... ... ... \n",
+ "6486 O Other . \n",
+ "6487 O Other , so \n",
+ "6488 O Other . Secondly, \n",
+ "6489 O Other . \\nIn conclusion, \n",
+ "6490 O Other . \n",
+ "\n",
+ " span_start span_end doc_id \n",
+ "0 0 503 essay001 \n",
+ "1 575 591 essay001 \n",
+ "2 714 716 essay001 \n",
+ "3 851 853 essay001 \n",
+ "4 1086 1088 essay001 \n",
+ "... ... ... ... \n",
+ "6486 1339 1341 essay402 \n",
+ "6487 1388 1393 essay402 \n",
+ "6488 1436 1448 essay402 \n",
+ "6489 1525 1543 essay402 \n",
+ "6490 1620 1621 essay402 \n",
+ "\n",
+ "[6491 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 142,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "records = []\n",
+ "df_arguments = df_arguments.sort_values(['doc_id', 'span_start', 'span_end'])\n",
+ "for (doc_id, text) in df_texts.sort_values('doc_id').itertuples(index=False):\n",
+ " df_argument = df_arguments[df_arguments.doc_id == doc_id]\n",
+ " prev_span = 0\n",
+ " for i, (text_segment, span_start, span_end) in enumerate(df_argument[['text', 'span_start', 'span_end']].itertuples(index=False)):\n",
+ " try:\n",
+ " assert text_segment == text[span_start: span_end]\n",
+ " except Exception as e:\n",
+ " print(f'{text_segment}\\n{text[span_start: span_end]}\\n')\n",
+ " # all the exception were manually checked. These are because of qutoe chars, this is a hot fix.!!!! TODO\n",
+ " df_arguments['text'][df_arguments.text == text_segment] = text[span_start: span_end]\n",
+ " records.append(('O', 'Other', text[prev_span: span_start], prev_span, span_start, doc_id))\n",
+ " prev_span = span_end\n",
+ " records.append(('O', 'Other', text[prev_span:], prev_span, len(text), doc_id))\n",
+ "df_other = pd.DataFrame.from_records(records, columns=df_arguments.columns)\n",
+ "df_other"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "id": "bd37a854",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_combined = pd.concat([df_other, df_arguments])\n",
+ "df_combined = df_combined.sort_values(['doc_id', 'span_start', 'span_end']).reset_index(drop=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "id": "d8e146a4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "\n",
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " label_type label \\\n",
+ "0 O Other \n",
+ "1 T1 MajorClaim \n",
+ "2 O Other \n",
+ "3 T3 Claim \n",
+ "4 O Other \n",
+ "... ... ... \n",
+ "12575 O Other \n",
+ "12576 T14 Premise \n",
+ "12577 O Other \n",
+ "12578 T2 MajorClaim \n",
+ "12579 O Other \n",
+ "\n",
+ " text \\\n",
+ "0 Should students be taught to compete or to cooperate?\\n\\nIt is always said that competition can effectively promote the development of economy. In order to survive in the competition, companies continue to improve their products and service, and as a result, the whole society prospers. However, when we discuss the issue of competition or cooperation, what we are concerned about is not the whole society, but the development of an individual's whole life. From this point of view, I firmly believe that \n",
+ "1 we should attach more importance to cooperation during primary education \n",
+ "2 .\\nFirst of all, \n",
+ "3 through cooperation, children can learn about interpersonal skills which are significant in the future life of all students \n",
+ "4 . \n",
+ "... ... \n",
+ "12575 . Secondly, \n",
+ "12576 playing sport makes children getting healthy and become children's well-being \n",
+ "12577 . \\nIn conclusion, \n",
+ "12578 both studying hard at school and playing sports can improve children's future \n",
+ "12579 . \n",
+ "\n",
+ " span_start span_end doc_id \n",
+ "0 0 503 essay001 \n",
+ "1 503 575 essay001 \n",
+ "2 575 591 essay001 \n",
+ "3 591 714 essay001 \n",
+ "4 714 716 essay001 \n",
+ "... ... ... ... \n",
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+ "12577 1525 1543 essay402 \n",
+ "12578 1543 1620 essay402 \n",
+ "12579 1620 1621 essay402 \n",
+ "\n",
+ "[12580 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 148,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_combined"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "id": "3c3afe91",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Other': 0, 'MajorClaim': 1, 'Claim': 2, 'Premise': 3}"
+ ]
+ },
+ "execution_count": 150,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ID2CLASS = dict(enumerate(df_combined['label'].unique().tolist()))\n",
+ "CLASS2ID = {v: k for k, v in ID2CLASS.items()}\n",
+ "# print(ID2CLASS)\n",
+ "CLASS2ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "id": "893e1d58",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def convert_label(label):\n",
+ " return CLASS2ID[label]\n",
+ "\n",
+ "df_combined['label'] = df_combined['label'].apply(convert_label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "id": "aab6ae45",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 247\n",
+ "3 146\n",
+ "2 64\n",
+ "1 43\n",
+ "Name: label, dtype: int64"
+ ]
+ },
+ "execution_count": 153,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_sample = df_combined.sample(n=500, random_state=2)\n",
+ "df_sample['label'].value_counts()\n",
+ "df_sample = df_sample[['text','label']]\n",
+ "df_sample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "id": "b60b578f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
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+ "| \n", + " | label_type | \n", + "label | \n", + "text | \n", + "span_start | \n", + "span_end | \n", + "doc_id | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "O | \n", + "Other | \n", + "Should students be taught to compete or to cooperate?\\n\\nIt is always said that competition can effectively promote the development of economy. In order to survive in the competition, companies continue to improve their products and service, and as a result, the whole society prospers. However, when we discuss the issue of competition or cooperation, what we are concerned about is not the whole society, but the development of an individual's whole life. From this point of view, I firmly believe that | \n", + "0 | \n", + "503 | \n", + "essay001 | \n", + "
| 1 | \n", + "T1 | \n", + "MajorClaim | \n", + "we should attach more importance to cooperation during primary education | \n", + "503 | \n", + "575 | \n", + "essay001 | \n", + "
| 2 | \n", + "O | \n", + "Other | \n", + ".\\nFirst of all, | \n", + "575 | \n", + "591 | \n", + "essay001 | \n", + "
| 3 | \n", + "T3 | \n", + "Claim | \n", + "through cooperation, children can learn about interpersonal skills which are significant in the future life of all students | \n", + "591 | \n", + "714 | \n", + "essay001 | \n", + "
| 4 | \n", + "O | \n", + "Other | \n", + ". | \n", + "714 | \n", + "716 | \n", + "essay001 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 12575 | \n", + "O | \n", + "Other | \n", + ". Secondly, | \n", + "1436 | \n", + "1448 | \n", + "essay402 | \n", + "
| 12576 | \n", + "T14 | \n", + "Premise | \n", + "playing sport makes children getting healthy and become children's well-being | \n", + "1448 | \n", + "1525 | \n", + "essay402 | \n", + "
| 12577 | \n", + "O | \n", + "Other | \n", + ". \\nIn conclusion, | \n", + "1525 | \n", + "1543 | \n", + "essay402 | \n", + "
| 12578 | \n", + "T2 | \n", + "MajorClaim | \n", + "both studying hard at school and playing sports can improve children's future | \n", + "1543 | \n", + "1620 | \n", + "essay402 | \n", + "
| 12579 | \n", + "O | \n", + "Other | \n", + ". | \n", + "1620 | \n", + "1621 | \n", + "essay402 | \n", + "
12580 rows × 6 columns
\n", + "| \n", + " | text | \n", + "text_ | \n", + "
|---|---|---|
| 0 | \n", + "xxbos games on their phone xxunk a xxunk xxunk on xxunk , xxunk to a large problem that they xxunk xxunk to their xxunk xxbos there is a fact that one can improve health xxunk with xxunk physical xxunk : xxunk , xxunk by xxunk we xxunk physical xxunk to better xxbos teachers think that foreign language most likely xxunk to xxunk for children who are xxunk to start learning xxbos . | \n", + "games on their phone xxunk a xxunk xxunk on xxunk , xxunk to a large problem that they xxunk xxunk to their xxunk xxbos there is a fact that one can improve health xxunk with xxunk physical xxunk : xxunk , xxunk by xxunk we xxunk physical xxunk to better xxbos teachers think that foreign language most likely xxunk to xxunk for children who are xxunk to start learning xxbos . xxmaj | \n", + "
| 1 | \n", + "significant issue in our xxunk xxbos they xxunk have an xxunk of xxunk different xxunk of life with the help of a xxunk xxbos xxunk can not always be xxunk in the real world xxbos students are to develop their xxunk skills by learning xxunk - related subjects , rather than playing on xxunk or xxunk a xxunk xxbos to be xxunk , we all xxunk to xxunk the xxunk we might | \n", + "issue in our xxunk xxbos they xxunk have an xxunk of xxunk different xxunk of life with the help of a xxunk xxbos xxunk can not always be xxunk in the real world xxbos students are to develop their xxunk skills by learning xxunk - related subjects , rather than playing on xxunk or xxunk a xxunk xxbos to be xxunk , we all xxunk to xxunk the xxunk we might xxunk | \n", + "
| 2 | \n", + "xxunk xxbos public transportation helps to solve the xxunk xxunk problems xxbos . xxmaj as a result , xxbos each of them has its own benefits xxbos . \\n xxmaj xxunk , at some point , xxbos xxmaj no one can xxunk the importance to xxunk health xxunk in school xxunk , especially at xxunk level , in the xxunk of raising people 's xxunk xxbos . xxmaj moreover , xxbos the | \n", + "xxbos public transportation helps to solve the xxunk xxunk problems xxbos . xxmaj as a result , xxbos each of them has its own benefits xxbos . \\n xxmaj xxunk , at some point , xxbos xxmaj no one can xxunk the importance to xxunk health xxunk in school xxunk , especially at xxunk level , in the xxunk of raising people 's xxunk xxbos . xxmaj moreover , xxbos the best | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.lr_find()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 173,
+ "id": "705e6e39",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/feng/opt/anaconda3/lib/python3.8/site-packages/torch/cuda/amp/autocast_mode.py:114: UserWarning: torch.cuda.amp.autocast only affects CUDA ops, but CUDA is not available. Disabling.\n",
+ "/Users/feng/opt/anaconda3/lib/python3.8/site-packages/torch/cuda/amp/grad_scaler.py:115: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n",
+ " warnings.warn(\"torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\")\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
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+ ]
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+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
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+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.fit_one_cycle(5, 1e-2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 174,
+ "id": "e4e2c7a3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/feng/opt/anaconda3/lib/python3.8/site-packages/torch/cuda/amp/autocast_mode.py:114: UserWarning: torch.cuda.amp.autocast only affects CUDA ops, but CUDA is not available. Disabling.\n",
+ "/Users/feng/opt/anaconda3/lib/python3.8/site-packages/torch/cuda/amp/grad_scaler.py:115: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\n",
+ " warnings.warn(\"torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling.\")\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
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+ "metadata": {},
+ "output_type": "display_data"
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+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "learn.unfreeze()\n",
+ "learn.fit_one_cycle(5, 1e-2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 175,
+ "id": "c3530d18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn.save_encoder('/Users/feng/downloads/ArgumentAnnotatedEssays-2.0/finetuned')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 176,
+ "id": "84f51f9b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/feng/opt/anaconda3/lib/python3.8/site-packages/numpy/core/_asarray.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " return array(a, dtype, copy=False, order=order)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dls_clas = TextDataLoaders.from_df(df_sample, path=path, text_col='text', label_col='label',text_vocab=dls_lm.vocab)\n",
+ "dls_clas.show_batch(max_n=3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 177,
+ "id": "5c0d56ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "metrics=[accuracy,F1Score(average='macro')]\n",
+ "learn = text_classifier_learner(dls_clas, AWD_LSTM,drop_mult=0.2, metrics=metrics, path=path, wd=0.1, model_dir=\"/tmp/model/\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 178,
+ "id": "11c86021",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = learn.load_encoder('/Users/feng/downloads/ArgumentAnnotatedEssays-2.0/finetuned')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 187,
+ "id": "e67ca373",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test = df_combined.sample(n=30, random_state=42)\n",
+ "df_test = df_test[['text','label','doc_id']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 195,
+ "id": "b55715c6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "get_predStr(df_test)\n",
+ "df_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 196,
+ "id": "08623e15",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict(txt):\n",
+ " with learn.no_bar(), learn.no_logging():\n",
+ " return int(learn.predict(txt)[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 208,
+ "id": "cc7b774a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test['predictions'] = df_test[\"text\"].progress_apply(lambda x: predict(x))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "350a904a",
+ "metadata": {},
+ "source": [
+ "# Adversarial Attacks Function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7a53da6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install tensorflow_text\n",
+ "#!textattack list attack-recipes\n",
+ "#!pip install textattack\n",
+ "from textattack.augmentation import EmbeddingAugmenter,EasyDataAugmenter,WordNetAugmenter,CharSwapAugmenter,CheckListAugmenter,CLAREAugmenter\n",
+ "import time\n",
+ "t = df_combined.iloc[0].text\n",
+ "augmenter = CharSwapAugmenter()\n",
+ "augmenter.augment(t)\n",
+ "\n",
+ "# - WordNet synonym replacement\n",
+ "# - Randomly replace words with their synonyms.\n",
+ "# - Word deletion\n",
+ "# - Randomly remove words from the sentence.\n",
+ "# - Word order swaps\n",
+ "# - Randomly swap the position of words in the sentence.\n",
+ "# - Random synonym insertion\n",
+ "# - Insert a random synonym of a random word at a random location.\n",
+ "augmenter = EasyDataAugmenter(pct_words_to_swap=0.2, transformations_per_example=1)\n",
+ "s = 'What I do not understand, I can not create.'\n",
+ "augmenter.augment(s)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 133,
+ "id": "d4b71c51",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_attacks(df,att_type,**kwargs):\n",
+ " \n",
+ " def easy_data(text):\n",
+ " augmenter = EasyDataAugmenter(kwargs['pct_swap'], kwargs['num_trans'])\n",
+ " return augmenter.augment(text)[0]\n",
+ " def syno(text):\n",
+ " augmenter = WordNetAugmenter()\n",
+ " return augmenter.augment(text)[0]\n",
+ " \n",
+ " if att_type == \"easy_data\":\n",
+ " df.text = df.text.apply(easy_data)\n",
+ " \n",
+ " elif att_type == \"replace_synonym\":\n",
+ " df.text = df.text.apply(syno)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 134,
+ "id": "3d37abf8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.6966230869293213\n"
+ ]
+ }
+ ],
+ "source": [
+ "s = time.time()\n",
+ "create_attacks(df_combined.iloc[:5],att_type = \"replace_synonym\")\n",
+ "print(time.time()-s)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "154371bc",
+ "metadata": {},
+ "source": [
+ "# Prediction_String"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 193,
+ "id": "11a02cf9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_predStr(df):\n",
+ " assert all(item in list(df) for item in ['label', 'text', 'doc_id']), \"Please use a dataframe with correct columns\"\n",
+ " prediction_strings = []\n",
+ " start_id = 1\n",
+ " prev_doc = df.iloc[0].doc_id\n",
+ " for (label, text, doc_id) in df[['label', 'text', 'doc_id']].itertuples(index=False):\n",
+ " if doc_id != prev_doc:\n",
+ " prev_doc = doc_id\n",
+ " start_id = 1\n",
+ " text_split = text.split()\n",
+ " end_id = start_id + len(text_split)\n",
+ " prediction_strings.append(\n",
+ " [num for num in range(start_id, end_id)]\n",
+ " )\n",
+ " start_id = end_id \n",
+ " df['predictionString'] = prediction_strings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "81682769",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12712e0a",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/qingyu/LSTM.ipynb b/experiments/qingyu/LSTM.ipynb
new file mode 100644
index 0000000..a14eb44
--- /dev/null
+++ b/experiments/qingyu/LSTM.ipynb
@@ -0,0 +1,570 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "id": "d8dab58e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# -- public imports\n",
+ "\n",
+ "import os\n",
+ "import nltk\n",
+ "import spacy\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "import keras\n",
+ "from keras import preprocessing\n",
+ "from keras.preprocessing.sequence import pad_sequences\n",
+ "\n",
+ "import torch.nn as nn\n",
+ "import torch\n",
+ "import torch.optim as optim\n",
+ "import torch.nn.functional as F\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "id": "ce83a197",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = pd.read_json(\"/Users/feng/downloads/data/bio/TUDarmstadt_postprocessed.json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "id": "d7ee3c4e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = data.iloc[:10]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 162,
+ "id": "40214708",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "biggest sentence has 422 words\n"
+ ]
+ }
+ ],
+ "source": [
+ "largest_len = max(len(s.split()) for s in list(df.text))\n",
+ "print(f\"biggest sentence has {largest_len} words\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "id": "1382a192",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "output_labels = ['O', 'B-MajorClaim', 'I-MajorClaim', 'B-Claim', 'I-Claim', 'B-Premise','I-Premise']\n",
+ "\n",
+ "labels_to_ids = {v:k for k,v in enumerate(output_labels)}\n",
+ "ids_to_labels = {k:v for k,v in enumerate(output_labels)}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 213,
+ "id": "9eea5c35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "word_to_ids = {\"UNK\":0}\n",
+ "for sentence in list(df.text):\n",
+ " for word in sentence.split():\n",
+ " if word not in word_to_ids.keys():\n",
+ " word_to_ids[word] = len(word_to_ids)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 170,
+ "id": "b226534a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ":15: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)\n",
+ " label = torch.tensor(labels,dtype=torch.long)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1\n",
+ "Epoch 2\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Training\n",
+ "acc_list = []\n",
+ "loss_list = []\n",
+ "epochs = 3\n",
+ "running_loss = 0\n",
+ "\n",
+ "for epoch in range(epochs):\n",
+ " print(f\"Epoch {epoch}\")\n",
+ " acc = 0\n",
+ " loss = 0\n",
+ " i = 0\n",
+ " for idx,(sentences,labels) in enumerate(generate_batch(X_tr,y_tr)):\n",
+ " optimizer.zero_grad()\n",
+ " sentence = torch.tensor(sentences,dtype=torch.long)\n",
+ " label = torch.tensor(labels,dtype=torch.long)\n",
+ " tag_scores = model(sentence)\n",
+ " loss = loss_function(tag_scores.view(-1,7),label.view(-1))\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " \n",
+ " argmaxed = torch.argmax(tag_scores,dim=-1)\n",
+ " mask = label>-1\n",
+ " relevant = argmaxed[mask]\n",
+ " acc = ((relevant==label[mask]).sum()/relevant.shape[0]).item()\n",
+ " loss = loss.item()\n",
+ " acc_list.append(acc)\n",
+ " loss_list.append(loss)\n",
+ " running_loss = 0.99*running_loss+0.01*loss\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "34089147",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/qingyu/baseline_from_pipeline.ipynb b/experiments/qingyu/baseline_from_pipeline.ipynb
new file mode 100644
index 0000000..a46152b
--- /dev/null
+++ b/experiments/qingyu/baseline_from_pipeline.ipynb
@@ -0,0 +1 @@
+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Potential Modifications:\n**other HuggingFace backbones \nother heads designs \nother architecture tricks like using hidden layer activations \nother pre and post processing \nquestion and answer instead of NER \nother learning schedules, optimizers, etc \nsliding window training \nadjust attention of LongFormer \nmore folds \ndata augmentation \nexternal data **","metadata":{}},{"cell_type":"markdown","source":"### TURN OFF INTERNET\nIn code competitions, we must turn internet off to make a submission. This starter notebook shows how to download any transformer of your choice to a Kaggle dataset","metadata":{}},{"cell_type":"markdown","source":"# Import","metadata":{}},{"cell_type":"code","source":"import json\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport os\n\nfrom collections import defaultdict\nfrom torch.utils.data import Dataset, DataLoader\nimport pdb\nimport torch\nfrom torch import cuda\n\nfrom transformers import BigBirdTokenizer, AutoTokenizer, AutoModelForTokenClassification, AutoConfig\nimport pandas as pd\nfrom torch.utils.data import DataLoader\nimport torch\n","metadata":{"execution":{"iopub.status.busy":"2022-03-12T10:16:20.712825Z","iopub.execute_input":"2022-03-12T10:16:20.714274Z","iopub.status.idle":"2022-03-12T10:16:21.227208Z","shell.execute_reply.started":"2022-03-12T10:16:20.714195Z","shell.execute_reply":"2022-03-12T10:16:21.226212Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Configuration","metadata":{}},{"cell_type":"code","source":"# import os\n# # DECLARE HOW MANY GPUS YOU WISH TO USE. \n# # KAGGLE ONLY HAS 1, BUT OFFLINE, YOU CAN USE MORE\n# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\" #0,1,2,3 for four gpu\n\n# # VERSION FOR SAVING MODEL WEIGHTS\n# VER=26\n\n# # IF VARIABLE IS NONE, THEN NOTEBOOK COMPUTES TOKENS\n# # OTHERWISE NOTEBOOK LOADS TOKENS FROM PATH\n# LOAD_TOKENS_FROM = '../input/py-bigbird-v26'\n\n# # IF VARIABLE IS NONE, THEN NOTEBOOK TRAINS A NEW MODEL\n# # OTHERWISE IT LOADS YOUR PREVIOUSLY TRAINED MODEL\n# LOAD_MODEL_FROM = '../input/py-bigbird-v26'\n\n# # IF FOLLOWING IS NONE, THEN NOTEBOOK \n# # USES INTERNET AND DOWNLOADS HUGGINGFACE \n# # CONFIG, TOKENIZER, AND MODEL\n# DOWNLOADED_MODEL_PATH = '../input/py-bigbird-v26' \n\n# if DOWNLOADED_MODEL_PATH is None:\n# DOWNLOADED_MODEL_PATH = 'model' \n# MODEL_NAME = 'google/bigbird-roberta-base'","metadata":{"execution":{"iopub.status.busy":"2022-03-12T10:26:25.232119Z","iopub.execute_input":"2022-03-12T10:26:25.23313Z","iopub.status.idle":"2022-03-12T10:26:25.240402Z","shell.execute_reply.started":"2022-03-12T10:26:25.233085Z","shell.execute_reply":"2022-03-12T10:26:25.238894Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_checkpoints = \"allenai/longformer-base-4096\"","metadata":{"execution":{"iopub.status.busy":"2022-03-12T10:32:46.803964Z","iopub.execute_input":"2022-03-12T10:32:46.804444Z","iopub.status.idle":"2022-03-12T10:32:46.809504Z","shell.execute_reply.started":"2022-03-12T10:32:46.804408Z","shell.execute_reply":"2022-03-12T10:32:46.8087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/feedback-prize-2021/train.csv')","metadata":{"execution":{"iopub.status.busy":"2022-03-12T09:48:21.692452Z","iopub.execute_input":"2022-03-12T09:48:21.69302Z","iopub.status.idle":"2022-03-12T09:48:23.407924Z","shell.execute_reply.started":"2022-03-12T09:48:21.692978Z","shell.execute_reply":"2022-03-12T09:48:23.406932Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_names, train_texts = [], []\nfor f in list(os.listdir('../input/feedback-prize-2021/train')):\n test_names.append(f.replace('.txt', ''))\n train_texts.append(open('../input/feedback-prize-2021/train/' + f, 'r').read())\n \ndoc_df = pd.DataFrame({'id': test_names, 'text': train_texts})\ndoc_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T09:48:23.409426Z","iopub.execute_input":"2022-03-12T09:48:23.409694Z","iopub.status.idle":"2022-03-12T09:49:15.833087Z","shell.execute_reply.started":"2022-03-12T09:48:23.409659Z","shell.execute_reply":"2022-03-12T09:49:15.831961Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Create Dataset: Convert Train Text to NER Labels","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"entities = []\nfor index,row in doc_df.iterrows():\n length_text = row['text'].split().__len__()\n ent = [\"O\" for i in range(length_text)]\n\n for idx,r in train_df[train_df['id'] == row['id']].iterrows():\n \n pred_idx = r['predictionstring'].split()\n ent[int(pred_idx[0])] = f\"B-{r['discourse_type']}\"\n\n for i in pred_idx[1:]:\n ent[int(i)] = f\"I-{r['discourse_type']}\"\n \n\n entities.append(ent)\n\n","metadata":{"execution":{"iopub.status.busy":"2022-03-12T09:49:15.835399Z","iopub.execute_input":"2022-03-12T09:49:15.83569Z","iopub.status.idle":"2022-03-12T09:56:25.118453Z","shell.execute_reply.started":"2022-03-12T09:49:15.835656Z","shell.execute_reply":"2022-03-12T09:56:25.117209Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"doc_df['elements'] = entities\ndoc_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T09:56:25.120794Z","iopub.execute_input":"2022-03-12T09:56:25.121254Z","iopub.status.idle":"2022-03-12T09:56:25.157038Z","shell.execute_reply.started":"2022-03-12T09:56:25.121217Z","shell.execute_reply":"2022-03-12T09:56:25.155755Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"output_labels = ['O', 'B-Lead', 'I-Lead', 'B-Position', 'I-Position', 'B-Claim', 'I-Claim', 'B-Counterclaim', 'I-Counterclaim', \n 'B-Rebuttal', 'I-Rebuttal', 'B-Evidence', 'I-Evidence', 'B-Concluding Statement', 'I-Concluding Statement']\n\nlabels_to_ids = {v:k for k,v in enumerate(output_labels)}\nids_to_labels = {k:v for k,v in enumerate(output_labels)}\n\ndoc_df['labels'] = doc_df['elements'].apply(lambda x: [labels_to_ids[i] for i in x])\n","metadata":{"execution":{"iopub.status.busy":"2022-03-12T09:56:25.158585Z","iopub.execute_input":"2022-03-12T09:56:25.158862Z","iopub.status.idle":"2022-03-12T09:56:26.163234Z","shell.execute_reply.started":"2022-03-12T09:56:25.15882Z","shell.execute_reply":"2022-03-12T09:56:26.162085Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"doc_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-12T09:56:26.164947Z","iopub.execute_input":"2022-03-12T09:56:26.165319Z","iopub.status.idle":"2022-03-12T09:56:26.193993Z","shell.execute_reply.started":"2022-03-12T09:56:26.16527Z","shell.execute_reply":"2022-03-12T09:56:26.192939Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Check MAX LENGTH (we use 1024 wide tokens since the majority of text is less than 1024 tokens.)","metadata":{}},{"cell_type":"code","source":"### Check MAX LENGTH (we use 1024 wide tokens since the majority of text is less than 1024 tokens.)\n# def calc_len(text):\n# ids = tokenizer(text)['input_ids']\n# return len(ids)\n\n# tokenizer = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-large')\n# doc_df['length'] = doc_df['text'].apply(calc_len)\n# do_df.describe()","metadata":{"execution":{"iopub.status.busy":"2022-03-08T00:03:16.796049Z","iopub.execute_input":"2022-03-08T00:03:16.796302Z","iopub.status.idle":"2022-03-08T00:03:19.449605Z","shell.execute_reply.started":"2022-03-08T00:03:16.796244Z","shell.execute_reply":"2022-03-08T00:03:19.448955Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Experiement with first top 3 training data\ntr_df = doc_df.iloc[:3]","metadata":{"execution":{"iopub.status.busy":"2022-03-12T09:56:26.197075Z","iopub.execute_input":"2022-03-12T09:56:26.197535Z","iopub.status.idle":"2022-03-12T09:56:26.207794Z","shell.execute_reply.started":"2022-03-12T09:56:26.197477Z","shell.execute_reply":"2022-03-12T09:56:26.206959Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tokenizer = AutoTokenizer.from_pretrained(model_checkpoints,add_prefix_space=True)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T10:33:47.213909Z","iopub.execute_input":"2022-03-12T10:33:47.214253Z","iopub.status.idle":"2022-03-12T10:34:05.586526Z","shell.execute_reply.started":"2022-03-12T10:33:47.214221Z","shell.execute_reply":"2022-03-12T10:34:05.585333Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# DataLoader\n### outputs: tokens and attention. \nDuring training it also provides labels.\nDuring inference it also provides word ids to help convert token predictions into word predictions.","metadata":{}},{"cell_type":"code","source":"# IGNORE_INDEX = -100\n# NON_LABEL = -1\n# DO WE LABEL ALL SUBTOKENS???\n\nLABEL_ALL_SUBTOKENS = True\n\nclass dataset(Dataset):\n def __init__(self, dataframe, tokenizer, max_len, get_wids):\n self.len = len(dataframe)\n self.data = dataframe\n self.tokenizer = tokenizer\n self.max_len = max_len\n self.get_wids = get_wids # for validation\n\n def __getitem__(self, index):\n # GET TEXT AND WORD LABELS \n text = self.data.text[index] \n word_labels = self.data.elements[index] if not self.get_wids else None\n\n # TOKENIZE TEXT\n encoding = self.tokenizer(text.split(),\n is_split_into_words=True,\n #return_offsets_mapping=True, \n padding='max_length', \n truncation=True, \n max_length=self.max_len)\n word_ids = encoding.word_ids() \n \n # CREATE TARGETS\n if not self.get_wids:\n previous_word_idx = None\n label_ids = []\n for word_idx in word_ids: \n if word_idx is None:\n label_ids.append(-100)\n elif word_idx != previous_word_idx: \n label_ids.append( labels_to_ids[word_labels[word_idx]] )\n else:\n if LABEL_ALL_SUBTOKENS:\n label_ids.append( labels_to_ids[word_labels[word_idx]] )\n else:\n label_ids.append(-100)\n previous_word_idx = word_idx\n encoding['labels'] = label_ids\n\n # CONVERT TO TORCH TENSORS\n item = {key: torch.as_tensor(val) for key, val in encoding.items()}\n if self.get_wids: \n word_ids2 = [w if w is not None else -1 for w in word_ids]\n item['wids'] = torch.as_tensor(word_ids2)\n \n return item\n\n def __len__(self):\n return self.len","metadata":{"execution":{"iopub.status.busy":"2022-03-12T10:50:32.408909Z","iopub.execute_input":"2022-03-12T10:50:32.409199Z","iopub.status.idle":"2022-03-12T10:50:32.4247Z","shell.execute_reply.started":"2022-03-12T10:50:32.409171Z","shell.execute_reply":"2022-03-12T10:50:32.423717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# CHOOSE VALIDATION INDEXES (that match my TF notebook)\nIDS = tr_df.id.unique()\nprint('There are',len(IDS),'train texts. We will split 90% 10% for validation.')\n\n# TRAIN VALID SPLIT 90% 10%\nnp.random.seed(42)\ntrain_idx = np.random.choice(np.arange(len(IDS)),int(0.9*len(IDS)),replace=False)\nvalid_idx = np.setdiff1d(np.arange(len(IDS)),train_idx)\nnp.random.seed(None)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T10:51:13.383022Z","iopub.execute_input":"2022-03-12T10:51:13.383383Z","iopub.status.idle":"2022-03-12T10:51:13.397224Z","shell.execute_reply.started":"2022-03-12T10:51:13.383346Z","shell.execute_reply":"2022-03-12T10:51:13.396332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# CREATE TRAIN SUBSET AND VALID SUBSET\ndata = tr_df[['id','text', 'elements']]\ntrain_dataset = data.loc[data['id'].isin(IDS[train_idx]),['text', 'elements']].reset_index(drop=True)\ntest_dataset = data.loc[data['id'].isin(IDS[valid_idx])].reset_index(drop=True)\n\nprint(\"FULL Dataset: {}\".format(data.shape))\nprint(\"TRAIN Dataset: {}\".format(train_dataset.shape))\nprint(\"TEST Dataset: {}\".format(test_dataset.shape))\n\ntraining_set = dataset(train_dataset, tokenizer, 1024, False)\ntesting_set = dataset(test_dataset, tokenizer, 1024, True)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T10:54:23.559678Z","iopub.execute_input":"2022-03-12T10:54:23.560129Z","iopub.status.idle":"2022-03-12T10:54:23.578185Z","shell.execute_reply.started":"2022-03-12T10:54:23.560088Z","shell.execute_reply":"2022-03-12T10:54:23.577367Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# TRAIN DATASET AND VALID DATASET\ntrain_params = {'batch_size': 3,\n 'shuffle': True,\n 'num_workers': 2,\n 'pin_memory':True\n }\n\ntest_params = {'batch_size': 3,\n 'shuffle': False,\n 'num_workers': 2,\n 'pin_memory':True\n }\n\ntraining_loader = DataLoader(training_set, **train_params)\ntesting_loader = DataLoader(testing_set, **test_params)\n\n# TEST DATASET\n# test_texts_set = dataset(test_texts, tokenizer, config['max_length'], True)\n# test_texts_loader = DataLoader(test_texts_set, **test_params)","metadata":{"execution":{"iopub.status.busy":"2022-03-12T10:54:26.089514Z","iopub.execute_input":"2022-03-12T10:54:26.090509Z","iopub.status.idle":"2022-03-12T10:54:26.118945Z","shell.execute_reply.started":"2022-03-12T10:54:26.090452Z","shell.execute_reply":"2022-03-12T10:54:26.11775Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/experiments/qingyu/generate_adAttacks.ipynb b/experiments/qingyu/generate_adAttacks.ipynb
new file mode 100644
index 0000000..399d020
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+ }
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "m5Rb4l8CKdPM",
+ "outputId": "fb71cc4e-7513-4f2d-8c25-c6552b2eaef7"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting git+https://github.com/namiyousef/colab-utils.git\n",
+ " Cloning https://github.com/namiyousef/colab-utils.git to /tmp/pip-req-build-n7563p22\n",
+ " Running command git clone -q https://github.com/namiyousef/colab-utils.git /tmp/pip-req-build-n7563p22\n",
+ "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from colab-dev-tools==0.0.7) (1.10.0+cu111)\n",
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from colab-dev-tools==0.0.7) (1.21.5)\n",
+ "Requirement already satisfied: nvidia-ml-py3 in /usr/local/lib/python3.7/dist-packages (from colab-dev-tools==0.0.7) (7.352.0)\n",
+ "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->colab-dev-tools==0.0.7) (4.1.1)\n",
+ "Building wheels for collected packages: colab-dev-tools\n",
+ " Building wheel for colab-dev-tools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for colab-dev-tools: filename=colab_dev_tools-0.0.7-py3-none-any.whl size=3654 sha256=82edc88c1bf452b5401647d79864a699488c701b2b3d2796a34b9ba99eecc33b\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-7r0t3gt3/wheels/1c/35/c0/364531e4ff0f0fe0f3296c80f1ee668b03ae6c6c378c5a44bf\n",
+ "Successfully built colab-dev-tools\n",
+ "Installing collected packages: colab-dev-tools\n",
+ "Successfully installed colab-dev-tools-0.0.7\n",
+ "Google Drive import successful.\n",
+ "CUDA device detected. Using GPU...\n",
+ "Mounted at /content/drive\n",
+ "Google Drive mount successful.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- env setup\n",
+ "import os\n",
+ "import gc\n",
+ "\n",
+ "!python3.7 -m pip install git+https://github.com/namiyousef/colab-utils.git\n",
+ "from colabtools.utils import get_gpu_utilization, mount_drive, install_private_library\n",
+ "\n",
+ "drive_path = mount_drive()\n",
+ "project_path = os.path.join(drive_path, 'COMP0087/data/core')\n",
+ "#development_dir = os.path.join(drive_path, 'argument-mining/argminer')\n",
+ "\n",
+ "install_private_library(os.path.join(drive_path, 'github_config.json'), 'argument-mining')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# import pkg_resources\n",
+ "# version = pkg_resources.require(\"argminer\")[0].version"
+ ],
+ "metadata": {
+ "id": "GRvwmT19z9xP"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from argminer.data import ArgumentMiningDataset, TUDarmstadtProcessor, PersuadeProcessor, DataProcessor, create_labels_doc_level, df_from_text_files\n",
+ "from argminer.evaluation import inference\n",
+ "import time\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gB_N8vYBhsWR",
+ "outputId": "c85007a7-7f0f-43ef-db0b-73545c850cf1"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Import of ArgMiner successful\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install nlpaug"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vew-rlL0Z45X",
+ "outputId": "d332e739-a744-4195-d3e3-83595cbc0b02"
+ },
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting nlpaug\n",
+ " Downloading nlpaug-1.1.10-py3-none-any.whl (410 kB)\n",
+ "\u001b[K |████████████████████████████████| 410 kB 4.3 MB/s \n",
+ "\u001b[?25hRequirement already satisfied: numpy>=1.16.2 in /usr/local/lib/python3.7/dist-packages (from nlpaug) (1.21.5)\n",
+ "Requirement already satisfied: pandas>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from nlpaug) (1.3.5)\n",
+ "Requirement already satisfied: requests>=2.22.0 in /usr/local/lib/python3.7/dist-packages (from nlpaug) (2.23.0)\n",
+ "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.2.0->nlpaug) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.2.0->nlpaug) (2018.9)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.2.0->nlpaug) (1.15.0)\n",
+ "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->nlpaug) (3.0.4)\n",
+ "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->nlpaug) (2.10)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->nlpaug) (2021.10.8)\n",
+ "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->nlpaug) (1.24.3)\n",
+ "Installing collected packages: nlpaug\n",
+ "Successfully installed nlpaug-1.1.10\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import nlpaug.augmenter.char as nac\n",
+ "import nlpaug.augmenter.word as naw\n",
+ "import nlpaug.augmenter.sentence as nas\n",
+ "\n",
+ "import nltk\n",
+ "nltk.download('averaged_perceptron_tagger')\n",
+ "nltk.download('wordnet')\n",
+ "nltk.download('omw-1.4')"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "RQWnMpdLYl9P",
+ "outputId": "8b6a5197-cfcb-423b-c21c-e34b32953a27"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+ "[nltk_data] /root/nltk_data...\n",
+ "[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.\n",
+ "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
+ "[nltk_data] Unzipping corpora/wordnet.zip.\n",
+ "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
+ "[nltk_data] Unzipping corpora/omw-1.4.zip.\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 4
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "text = [\"From this point of view, I firmly believe that we should attach more importance to cooperation during primary education.\"]"
+ ],
+ "metadata": {
+ "id": "qOAGncjPaXni"
+ },
+ "execution_count": 7,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "id": "dAWCA0_rLHQg",
+ "outputId": "da0e6025-4e30-4756-bab6-7099f4b4f2b6"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'From this point of view, I firmly believe that we should attach more importance to cooperation during primary education.'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 33
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def filler_augment(text, fillers=None):\n",
+ " \"\"\"Augments the beginning of text with a phrase taken from a list of pre-defined filler phrases.\n",
+ " The filler phrases are taken from the TUDarmstadt annotation guidelines, with the addition of some\n",
+ " other common fillers used in english.\"\"\"\n",
+ " if len(text) <= 1:\n",
+ " return text\n",
+ " if fillers is None:\n",
+ " fillers = [\n",
+ " \"According to the previous fact, \",\n",
+ " \"As can be seen, \",\n",
+ " \"For example, \",\n",
+ " \"Another important point which contributes to my argument is that \",\n",
+ " \"I agree to this view that \",\n",
+ " \"In this context, \",\n",
+ " \"At the end of the day, \",\n",
+ " ]\n",
+ " random_idx = np.random.choice(len(fillers))\n",
+ " filler = fillers[random_idx]\n",
+ " aug_text = filler + text[0].lower() + text[1:]\n",
+ " \n",
+ " return aug_text"
+ ],
+ "metadata": {
+ "id": "635_0MrpEe62"
+ },
+ "execution_count": 6,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "s = time.time()\n",
+ "text = filler_augment(text)\n",
+ "print(time.time()-s)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Vz8ESgObEgrv",
+ "outputId": "507a64e3-8e6b-44c6-93b8-8d4456c0f291"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "0.0056934356689453125\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#Synonym\n",
+ "\n",
+ "aug = naw.SynonymAug()\n",
+ "augmented_text = aug.augment(text)\n"
+ ],
+ "metadata": {
+ "id": "-I5J0E2kaJ_m"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#Random spelling mistakes\n",
+ "aug = naw.SpellingAug()"
+ ],
+ "metadata": {
+ "id": "Lyl3UYB3veQc"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#Random word insertion /substitution\n",
+ "aug = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action=\"insert\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 177,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "13ORocTJq53u",
+ "outputId": "706da3d6-21e6-47ea-f181-1ee09d71e4db"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading: 0%| | 0.00/28.0 [00:00"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "for strategy in ['bio','io','bieo']:\n",
+ " for aug_name, aug_func in {'synonym':naw.SynonymAug().augment,'spellingError':naw.SpellingAug().augment,\n",
+ " \"antonym\":naw.AntonymAug().augment, 'keywordChange':naw.ReservedAug(reserved_tokens=reserved_tokens).augment}.items():\n",
+ " processor = PersuadeProcessor(path_persuade).from_json(status='preprocessed')\n",
+ " processor = processor.process(strategy=strategy,test_size=0.3, processors=[aug_func],split=test).postprocess()\n",
+ " save_path = path_persuade+f'/{test}/{strategy}/{aug_name}'\n",
+ " if not os.path.exists(save_path):\n",
+ " os.makedirs(save_path)\n",
+ " print(f\"{save_path} is created\")\n",
+ " processor.save_json(save_path)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Y1qIYh0CjAmG",
+ "outputId": "759daa16-c30c-4267-82f6-851e663a4cd7"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: UserWarning: Getting data for split=test with params {'test_size': 0.3}\n",
+ " \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "drive/MyDrive/augmented_dataset/test/bio/synonym is created\n",
+ "drive/MyDrive/augmented_dataset/test/bio/spellingError is created\n",
+ "drive/MyDrive/augmented_dataset/test/bio/antonym is created\n",
+ "drive/MyDrive/augmented_dataset/test/bio/keywordChange is created\n",
+ "drive/MyDrive/augmented_dataset/test/io/synonym is created\n",
+ "drive/MyDrive/augmented_dataset/test/io/spellingError is created\n",
+ "drive/MyDrive/augmented_dataset/test/io/antonym is created\n",
+ "drive/MyDrive/augmented_dataset/test/io/keywordChange is created\n",
+ "drive/MyDrive/augmented_dataset/test/bieo/synonym is created\n",
+ "drive/MyDrive/augmented_dataset/test/bieo/spellingError is created\n",
+ "drive/MyDrive/augmented_dataset/test/bieo/antonym is created\n",
+ "drive/MyDrive/augmented_dataset/test/bieo/keywordChange is created\n",
+ "1203.1301529407501\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "for strategy in ['bio','io','bieo']:\n",
+ " for aug_name, aug_func in {'custom_fillers':filler_augment}.items():\n",
+ " processor = PersuadeProcessor(path_persuade).from_json(status='preprocessed')\n",
+ " processor = processor.process(strategy=strategy,test_size=0.3, processors=[aug_func],split=test).postprocess()\n",
+ " save_path = path_persuade+f'/{test}/{strategy}/{aug_name}'\n",
+ " if not os.path.exists(save_path):\n",
+ " os.makedirs(save_path)\n",
+ " print(f\"{save_path} is created\")\n",
+ " processor.save_json(save_path)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7wfd7EJDE5X8",
+ "outputId": "8dd60141-ef66-4b00-ee78-434099cc5697"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: Getting data for split=test with params {'test_size': 0.3}\n",
+ " after removing the cwd from sys.path.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "drive/MyDrive/augmented_dataset/test/bio/custom_fillers is created\n",
+ "drive/MyDrive/augmented_dataset/test/io/custom_fillers is created\n",
+ "drive/MyDrive/augmented_dataset/test/bieo/custom_fillers is created\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "for strategy in ['bio','io','bieo']:\n",
+ " for aug_name, aug_func in {'synonym':naw.SynonymAug().augment,'spellingError':naw.SpellingAug().augment,\n",
+ " \"antonym\":naw.AntonymAug().augment, 'keywordChange':naw.ReservedAug(reserved_tokens=reserved_tokens).augment,\n",
+ " 'custom_fillers':filler_augment}.items():\n",
+ " processor = TUDarmstadtProcessor(path_tu).from_json(status='preprocessed')\n",
+ " processor = processor.process(strategy=strategy,test_size=0.3, processors=[aug_func],split=test).postprocess()\n",
+ " save_path = path_tu+f'/{test}/{strategy}/{aug_name}'\n",
+ " if not os.path.exists(save_path):\n",
+ " os.makedirs(save_path)\n",
+ " print(f\"{save_path} is created\")\n",
+ " processor.save_json(save_path)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "NiTHxeVE47_9",
+ "outputId": "556b45be-b2d8-46a2-9096-f969040b80b1"
+ },
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: UserWarning: Getting data for split=test with params {'test_size': 0.3}\n",
+ " \n"
+ ]
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/experiments/qingyu/python_files/__init__.py b/experiments/qingyu/python_files/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/experiments/qingyu/python_files/create_dataset.py b/experiments/qingyu/python_files/create_dataset.py
new file mode 100644
index 0000000..aa739f1
--- /dev/null
+++ b/experiments/qingyu/python_files/create_dataset.py
@@ -0,0 +1,54 @@
+import json
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import os
+from collections import defaultdict
+from torch.utils.data import Dataset, DataLoader
+import pdb
+import torch
+from torch import cuda
+import time
+print('Yousef test')
+s = time.time()
+BASE_PATH = '../data/kaggle/feedback-prize-2021/'
+path_from_base = lambda x: os.path.join(BASE_PATH, x)
+train_df = pd.read_csv(path_from_base('train.csv'))
+test_names, train_texts = [], []
+for f in list(os.listdir(path_from_base('train'))):
+ test_names.append(f.replace('.txt', ''))
+ train_texts.append(open(path_from_base(f'train/{f}'), 'r').read())
+
+doc_df = pd.DataFrame({'id': test_names, 'text': train_texts})
+#Create entities for each document
+entities = []
+for index,row in doc_df.iterrows():
+ length_text = row['text'].split().__len__()
+ ent = ["O" for i in range(length_text)]
+
+ for idx,r in train_df[train_df['id'] == row['id']].iterrows():
+
+ pred_idx = r['predictionstring'].split()
+ ent[int(pred_idx[0])] = f"B-{r['discourse_type']}"
+
+ for i in pred_idx[1:]:
+ ent[int(i)] = f"I-{r['discourse_type']}"
+
+
+ entities.append(ent)
+
+doc_df['elements'] = entities
+
+#Match the labels to entities
+output_labels = ['O', 'B-Lead', 'I-Lead', 'B-Position', 'I-Position', 'B-Claim', 'I-Claim', 'B-Counterclaim', 'I-Counterclaim',
+ 'B-Rebuttal', 'I-Rebuttal', 'B-Evidence', 'I-Evidence', 'B-Concluding Statement', 'I-Concluding Statement']
+
+labels_to_ids = {v:k for k,v in enumerate(output_labels)}
+ids_to_labels = {k:v for k,v in enumerate(output_labels)}
+
+doc_df['labels'] = doc_df['elements'].apply(lambda x: [labels_to_ids[i] for i in x])
+print(doc_df.head())
+for i, (text, label) in doc_df[['text', 'labels']].iterrows():
+ assert len(label) == len(text.split())
+print(time.time() - s)
\ No newline at end of file
diff --git a/experiments/qingyu/python_files/dataprocess_dev_new.py b/experiments/qingyu/python_files/dataprocess_dev_new.py
new file mode 100644
index 0000000..31dba05
--- /dev/null
+++ b/experiments/qingyu/python_files/dataprocess_dev_new.py
@@ -0,0 +1,170 @@
+# -- public imports
+import os
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+import pandas as pd
+from torch.utils.data import DataLoader
+import torch
+from pandas.testing import assert_frame_equal
+import time
+import warnings
+warnings.filterwarnings("ignore")
+# -- private imports
+##from colabtools.utils import move_to_device
+
+# -- dev imports
+#%load_ext autoreload
+#%autoreload 2
+
+from argminer.data import ArgumentMiningDataset, TUDarmstadtProcessor, PersuadeProcessor, DataProcessor, create_labels_doc_level, df_from_text_files
+from argminer.evaluation import inference
+
+
+#NEW VERSION
+
+class PersuadeProcessor(DataProcessor):
+ def __init__(self,path=''):
+ super().__init__(path)
+
+ def _preprocess(self):
+ path_to_text_dir = os.path.join(self.path, 'train')
+ path_to_ground_truth = os.path.join(self.path, 'train.csv')
+
+ df_ground_truth = pd.read_csv(path_to_ground_truth)
+ df_texts = df_from_text_files(path_to_text_dir)
+ df_ground_truth = df_ground_truth.sort_values(['id', 'discourse_start', 'discourse_end'])
+ df_ground_truth = df_ground_truth.drop(columns=['discourse_id','discourse_start','discourse_end','discourse_type_num'])
+
+ ##Rename columns
+ df_ground_truth = df_ground_truth.rename(columns={'discourse_type':'label','predictionstring':'predictionString',
+ 'discourse_text':'text','id':'doc_id'})
+ df_texts = df_texts.rename(columns={'text':'doc_text','id':'doc_id'})
+
+ df_texts['text_split'] = df_texts.doc_text.str.split()
+ df_texts['range'] = df_texts['text_split'].apply(lambda x: list(range(len(x))))
+ df_texts['start_id'] = df_texts['range'].apply(lambda x: x[0])
+ df_texts['end_id'] = df_texts['range'].apply(lambda x: x[-1])
+ df_texts = df_texts.drop(columns=['text_split','range'])
+
+
+ df = df_ground_truth.groupby('doc_id').agg({
+ 'text':lambda x: ' '.join(x),
+ 'predictionString': lambda x: ' '.join(x),
+
+ }).reset_index()
+
+ df = df.merge(df_texts)
+
+ df['predictionString'] = df.predictionString.apply(lambda x: [int(num) for num in x.split()])
+ df['pred_str_start_id'] = df.predictionString.apply(lambda x: x[0])
+ df['pred_str_end_id'] = df.predictionString.apply(lambda x: x[-1])
+
+ new_df_end = pd.DataFrame()
+ new_df_start = pd.DataFrame()
+ for row in df.sort_values('doc_id').itertuples(index=False):
+ if row.end_id != row.pred_str_end_id:
+ s = row.doc_text.split()[row.pred_str_end_id+1:]
+ new_string = ' '.join(s)
+ new_predsStr = list(range(row.pred_str_end_id+1,row.end_id+1))
+ new_predsStr = " ".join([str(i) for i in new_predsStr])
+ new_type = 'Other'
+ new_id = row.doc_id
+ new_row = {'doc_id': new_id, 'text':new_string ,'label':'Other','predictionString':new_predsStr}
+ new_df_end = new_df_end.append(new_row,ignore_index=True)
+
+ if row.start_id != row.pred_str_start_id:
+ s = row.doc_text.split()[:row.pred_str_start_id]
+ new_string = ' '.join(s)
+ new_predsStr = list(range(row.start_id,row.pred_str_start_id))
+ new_predsStr = " ".join([str(i) for i in new_predsStr])
+ new_type = 'Other'
+ new_id = row.doc_id
+ new_row = {'doc_id': new_id, 'text':new_string ,'label':'Other','predictionString':new_predsStr}
+ new_df_start = new_df_start.append(new_row,ignore_index=True)
+
+
+ df_combined = df_ground_truth.append(new_df_end,ignore_index=True)
+ df_combined = new_df_start.append(df_combined,ignore_index=True)
+
+
+
+ ##KEEP THE FOLLOWING OR NOT???
+ df_combined.predictionString = df_combined.predictionString.apply(lambda x: [int(num) for num in x.split()])
+ df_combined['start'] = df_combined.predictionString.apply(lambda x: x[0])
+ df_combined['end'] = df_combined.predictionString.apply(lambda x: x[-1])
+ df_combined= df_combined.sort_values(['doc_id', 'start', 'end'])
+
+
+ self.dataframe = df_combined
+ self.status = 'preprocessed'
+ return self
+
+ def _process(self, strategy, processors=[]):
+ # processes data to standardised format, adds any extra cleaning steps
+ assert strategy in {'io', 'bio', 'bieo'} # for now
+
+ df = self.dataframe.copy()
+
+ for processor in processors:
+ df['text'] = df['text'].apply(processor)
+
+
+ # add labelling strategy
+ label_strat = dict(
+ add_end='e' in strategy,
+ add_beg='b' in strategy
+ )
+
+ df['label'] = df[['label', 'predictionString']].apply(
+ lambda x: _generate_entity_labels(
+ len(x['predictionString']), x['label'], **label_strat
+ ), axis=1
+ )
+
+ self.dataframe = df
+ self.status = 'processed'
+
+
+ return self
+
+ def _postprocess(self):
+ df_post = self.dataframe.copy()
+ df_post['predictionString'] = df_post['predictionString'].apply(
+ lambda x: ' '.join([str(item) for item in x])
+ )
+
+ df_post['label'] = df_post['label'].apply(
+ lambda x: ' '.join([str(item) for item in x])
+ )
+ df_post = df_post.groupby('doc_id').agg({
+ 'text':lambda x: ' '.join(x),
+ 'predictionString': lambda x: ' '.join(x),
+ 'label': lambda x: ' '.join(x)
+ })
+ df_post['label'] = df_post['label'].str.split()
+ df_post['predictionString'] = df_post['predictionString'].str.split().apply(lambda x: [int(x) for x in x])
+
+ df_post = df_post.reset_index()
+
+
+ self.dataframe = df_post
+ self.status = 'postprocessed'
+
+ return self
+
+
+def _generate_entity_labels(length, label, add_end=False, add_beg=True):
+ """
+ For cases where argument segment is only 1 word long, beginning given preference over end
+ """
+ labels = [f'I-{label}'] if label != 'Other' else ['O']
+ labels *= length
+
+ if add_end:
+ if label != 'Other':
+ labels[-1] = f'E-{label}'
+
+ if add_beg:
+ if label != 'Other':
+ labels[0] = f'B-{label}'
+
+ return labels
diff --git a/experiments/qingyu/requirements.txt b/experiments/qingyu/requirements.txt
new file mode 100644
index 0000000..e69de29
diff --git a/experiments/yousef/Baseline_dev.ipynb b/experiments/yousef/Baseline_dev.ipynb
new file mode 100644
index 0000000..5bbe5e0
--- /dev/null
+++ b/experiments/yousef/Baseline_dev.ipynb
@@ -0,0 +1,35352 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e350b7f0",
+ "metadata": {},
+ "source": [
+ "# Baseline_dev"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "56ae4770",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_2992/3695259575.py:18: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+ " df.text = df.text.str.replace('[^\\w\\s]','')\n"
+ ]
+ }
+ ],
+ "source": [
+ "# public imports\n",
+ "import torch\n",
+ "from torch import nn\n",
+ "from torch import optim\n",
+ "\n",
+ "# private imports\n",
+ "import gensim\n",
+ "\n",
+ "gensim_model = gensim.models.KeyedVectors.load_word2vec_format(\n",
+ " '../../data/UCL/embeddings/GoogleNews-vectors-negative300.bin', binary=True\n",
+ ")\n",
+ "\n",
+ "embedding_map = gensim_model.key_to_index\n",
+ "\n",
+ "path = '../../data/bio/Persuade_postprocessed.json'\n",
+ "import pandas as pd\n",
+ "df = pd.read_json(path)\n",
+ "df.text = df.text.str.replace('[^\\w\\s]','')\n",
+ "vocab = set(' '.join(df.text.values).split())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "df7be8ef",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "differentation\n",
+ "licenes\n",
+ "stired\n",
+ "disavenges\n",
+ "tecnonlgy\n",
+ "dangres\n",
+ "timeonce\n",
+ "swending\n",
+ "Frontantalis\n",
+ "efficatly\n",
+ "greatests\n",
+ "saidyes\n",
+ "diverles\n",
+ "obsticales\n",
+ "spreding\n",
+ "fortutious\n",
+ "assocciated\n",
+ "completeley\n",
+ "worklike\n",
+ "liveabul\n",
+ "simulationg\n",
+ "travewled\n",
+ "diasater\n",
+ "criticised\n",
+ "arugued\n",
+ "apppear\n",
+ "studants\n",
+ "plesure\n",
+ "publicransport\n",
+ "comprimseThe\n",
+ "innnovative\n",
+ "secientisit\n",
+ "towlering\n",
+ "sseat\n",
+ "possile\n",
+ "collenting\n",
+ "emtoins\n",
+ "clubunited\n",
+ "getiing\n",
+ "collaberate\n",
+ "iventions\n",
+ "Electral\n",
+ "escpially\n",
+ "crators\n",
+ "depper\n",
+ "indivisual\n",
+ "Idoh\n",
+ "proxmity\n",
+ "35000\n",
+ "lettign\n",
+ "billsclothes\n",
+ "sleepinduced\n",
+ "prussure\n",
+ "mowfuntion\n",
+ "frocery\n",
+ "humanliy\n",
+ "alwats\n",
+ "eclectronics\n",
+ "imparing\n",
+ "expadition\n",
+ "fathersand\n",
+ "Additionallystudents\n",
+ "biggersized\n",
+ "orbiculariaris\n",
+ "Secondlystudents\n",
+ "attentiveand\n",
+ "nessacary\n",
+ "anachroism\n",
+ "transreagional\n",
+ "overexagerating\n",
+ "revolutionairy\n",
+ "terain\n",
+ "impaBence\n",
+ "Engneers\n",
+ "expericance\n",
+ "associted\n",
+ "emotioanl\n",
+ "professer\n",
+ "drecrease\n",
+ "flawwed\n",
+ "leglisation\n",
+ "maunufacturers\n",
+ "civilzation\n",
+ "curiusity\n",
+ "rocksgasses\n",
+ "chaets\n",
+ "5050\n",
+ "Clus\n",
+ "seechs\n",
+ "dilemas\n",
+ "srtaying\n",
+ "personplaceand\n",
+ "anchent\n",
+ "expiditions\n",
+ "studet\n",
+ "suming\n",
+ "Colombins\n",
+ "planetother\n",
+ "statesAll\n",
+ "pplace\n",
+ "approriatins\n",
+ "emotionallyand\n",
+ "learnedthey\n",
+ "canididatesor\n",
+ "p12\n",
+ "yse\n",
+ "alogorithms\n",
+ "lleagal\n",
+ "Orleansetc\n",
+ "cuasing\n",
+ "phonomanon\n",
+ "navagational\n",
+ "suvice\n",
+ "smilly\n",
+ "ncsl\n",
+ "brease\n",
+ "cutepretty\n",
+ "cuases\n",
+ "gavinen\n",
+ "runnung\n",
+ "schoking\n",
+ "aporate\n",
+ "atmosheric\n",
+ "instrustor\n",
+ "disaterous\n",
+ "tex5ng\n",
+ "twenteyfour\n",
+ "imageded\n",
+ "oppinon\n",
+ "unindended\n",
+ "gmaes\n",
+ "foccus\n",
+ "ninetyfour\n",
+ "Rosenthalsays\n",
+ "efficeint\n",
+ "goveners\n",
+ "equivellent\n",
+ "malte\n",
+ "studiying\n",
+ "yourfacial\n",
+ "happning\n",
+ "cautiosly\n",
+ "SkylinesHonda\n",
+ "conclusionTeachers\n",
+ "imprestion\n",
+ "saidIm\n",
+ "betwwen\n",
+ "taketherefore\n",
+ "bannned\n",
+ "driverassisting\n",
+ "improive\n",
+ "newrecognition\n",
+ "attentionso\n",
+ "complteing\n",
+ "conrollers\n",
+ "driverspassengersand\n",
+ "1112\n",
+ "63\n",
+ "dependes\n",
+ "leasat\n",
+ "driviing\n",
+ "keping\n",
+ "mountfunction\n",
+ "biast\n",
+ "gasonline\n",
+ "yearson\n",
+ "homelessdoesnt\n",
+ "practise\n",
+ "comotional\n",
+ "belevie\n",
+ "satarted\n",
+ "resaerch\n",
+ "soby\n",
+ "oressure\n",
+ "feelling\n",
+ "proabley\n",
+ "pixle\n",
+ "saer\n",
+ "consculsion\n",
+ "vioces\n",
+ "gooten\n",
+ "perons\n",
+ "conclusionReduced\n",
+ "factthese\n",
+ "planningAll\n",
+ "nottable\n",
+ "understans\n",
+ "wathc\n",
+ "guidelinefree\n",
+ "workespecially\n",
+ "Bradfort\n",
+ "expertes\n",
+ "itÃ\n",
+ "pritty\n",
+ "eruptiones\n",
+ "placeand\n",
+ "decideing\n",
+ "polluston\n",
+ "sensores\n",
+ "queastion\n",
+ "goesI\n",
+ "boredem\n",
+ "desasters\n",
+ "reasoin\n",
+ "graduat\n",
+ "25yrs\n",
+ "wellwritten\n",
+ "negitative\n",
+ "hellps\n",
+ "sulfic\n",
+ "teresstial\n",
+ "acedements\n",
+ "clasess\n",
+ "learnedstudent\n",
+ "nessicisarly\n",
+ "aplies\n",
+ "deicided\n",
+ "understandabe\n",
+ "AfricaAsiaHaiti\n",
+ "petray\n",
+ "SafeWise\n",
+ "obtaing\n",
+ "peoplke\n",
+ "phonw\n",
+ "onilne\n",
+ "isting\n",
+ "carsbut\n",
+ "stady\n",
+ "extracurriular\n",
+ "disform\n",
+ "Distruct\n",
+ "resalts\n",
+ "professionallycreated\n",
+ "computuer\n",
+ "Constitutionparagraph\n",
+ "disriciminate\n",
+ "paragrahph\n",
+ "inmformation\n",
+ "woork\n",
+ "laern\n",
+ "althoghVenus\n",
+ "buttion\n",
+ "essentiallty\n",
+ "carthe\n",
+ "holidayseasonal\n",
+ "95\n",
+ "alternet\n",
+ "studyTherefore\n",
+ "expirens\n",
+ "discussionElectoral\n",
+ "articl\n",
+ "roatatinf\n",
+ "indepentent\n",
+ "Dissapointment\n",
+ "interation\n",
+ "oberserved\n",
+ "elecctorial\n",
+ "Permanetly\n",
+ "imounded\n",
+ "statingAstronomers\n",
+ "desaventage\n",
+ "pargraphe\n",
+ "scrapebookpriceless\n",
+ "oneso\n",
+ "comfired\n",
+ "somethingand\n",
+ "thinh\n",
+ "BAAAMMM\n",
+ "dothey\n",
+ "friendshave\n",
+ "equale\n",
+ "anacrinism\n",
+ "schoolpause\n",
+ "provied\n",
+ "Bannings\n",
+ "iniptitudes\n",
+ "allwell\n",
+ "braingames\n",
+ "conmtribute\n",
+ "worriedtwo\n",
+ "examplemy\n",
+ "dearcitizens\n",
+ "Comlombia\n",
+ "withit\n",
+ "tweks\n",
+ "situatin\n",
+ "ifwhen\n",
+ "TableTennis\n",
+ "calsses\n",
+ "tueday\n",
+ "fahrenheitthe\n",
+ "responsibilityallow\n",
+ "elimanating\n",
+ "gereration\n",
+ "schoolarship\n",
+ "characterlise\n",
+ "cautionsly\n",
+ "internrt\n",
+ "somwtimes\n",
+ "incompletenot\n",
+ "boreds\n",
+ "Llff\n",
+ "docter\n",
+ "slectors\n",
+ "learnread\n",
+ "tramendously\n",
+ "electory\n",
+ "walkn\n",
+ "leraning\n",
+ "manufactours\n",
+ "classwok\n",
+ "Kennedysource\n",
+ "votesthen\n",
+ "katchup\n",
+ "furope\n",
+ "slife\n",
+ "11134345\n",
+ "studentsare\n",
+ "attiontion\n",
+ "interrestiing\n",
+ "mid1990s\n",
+ "bicking\n",
+ "autoaccident\n",
+ "distions\n",
+ "llow\n",
+ "madeit\n",
+ "efficenty\n",
+ "41971\n",
+ "phoneswe\n",
+ "weught\n",
+ "gauarded\n",
+ "coold\n",
+ "landface\n",
+ "29th\n",
+ "Haidron\n",
+ "40mpg\n",
+ "joyn\n",
+ "ashmed\n",
+ "epolice\n",
+ "snoyone\n",
+ "pklaces\n",
+ "everuthing\n",
+ "jobyou\n",
+ "nondemocractiv\n",
+ "opinian\n",
+ "1521\n",
+ "motorisits\n",
+ "peoplehaving\n",
+ "bettermore\n",
+ "alantic\n",
+ "Carolinaand\n",
+ "counrties\n",
+ "disrioed\n",
+ "frontalis\n",
+ "venouse\n",
+ "comphression\n",
+ "engimes\n",
+ "nonfacial\n",
+ "malking\n",
+ "implimant\n",
+ "averyone\n",
+ "previouslymade\n",
+ "votersThere\n",
+ "mindsetIt\n",
+ "vome\n",
+ "socitiy\n",
+ "practising\n",
+ "faund\n",
+ "beliee\n",
+ "campien\n",
+ "environmentsource\n",
+ "thoughwe\n",
+ "capitilize\n",
+ "elecrion\n",
+ "candidaates\n",
+ "driving33062\n",
+ "personsaid\n",
+ "maapportionment\n",
+ "gooan\n",
+ "perswading\n",
+ "exploaration\n",
+ "recessin\n",
+ "disigne\n",
+ "reoson\n",
+ "madeby\n",
+ "girlfreind\n",
+ "ofof\n",
+ "emmisionsjust\n",
+ "winningWe\n",
+ "Electoarl\n",
+ "acceot\n",
+ "vare\n",
+ "cacn\n",
+ "eatten\n",
+ "invintions\n",
+ "straightup\n",
+ "fiting\n",
+ "corrosire\n",
+ "parigraph\n",
+ "automactily\n",
+ "bisest\n",
+ "assignements\n",
+ "logest\n",
+ "humdred\n",
+ "systemmaking\n",
+ "posiblity\n",
+ "soomth\n",
+ "Texasthat\n",
+ "aloong\n",
+ "reasonof\n",
+ "passins\n",
+ "impracticle\n",
+ "electorialvote\n",
+ "unbuyable\n",
+ "atke\n",
+ "nearrecords\n",
+ "overcomewhat\n",
+ "saysSensors\n",
+ "shuled\n",
+ "BLOOMED\n",
+ "textThe\n",
+ "oringial\n",
+ "privatepublic\n",
+ "importantFor\n",
+ "joyne\n",
+ "21st\n",
+ "livley\n",
+ "unaccouted\n",
+ "distrought\n",
+ "scoreed\n",
+ "presidention\n",
+ "evalutes\n",
+ "presidentmost\n",
+ "wjhy\n",
+ "lanform\n",
+ "gratational\n",
+ "prsidency\n",
+ "questionsand\n",
+ "surroungings\n",
+ "carewhat\n",
+ "luky\n",
+ "Lke\n",
+ "selfimage\n",
+ "lifeit\n",
+ "itS\n",
+ "vehiclesalso\n",
+ "sportsactivitys\n",
+ "iteam\n",
+ "haping\n",
+ "caputured\n",
+ "coleg\n",
+ "BeijingSource\n",
+ "end1894\n",
+ "disaddvantages\n",
+ "feesable\n",
+ "assissting\n",
+ "apprpriations\n",
+ "simoltaniously\n",
+ "curasidity\n",
+ "bussinesses\n",
+ "FKennedy\n",
+ "Legalmatch\n",
+ "depressesd\n",
+ "jailjust\n",
+ "Cwhich\n",
+ "charactersitic\n",
+ "Sutdies\n",
+ "existiing\n",
+ "decress\n",
+ "Abloish\n",
+ "nurvice\n",
+ "accidnent\n",
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+ "misions\n",
+ "chamged\n",
+ "loarn\n",
+ "friendsnot\n",
+ "assgined\n",
+ "heho\n",
+ "Denator\n",
+ "phiscally\n",
+ "confusedAlso\n",
+ "bikinng\n",
+ "metling\n",
+ "greaty\n",
+ "esaier\n",
+ "ffic\n",
+ "possibilitys\n",
+ "indivudal\n",
+ "othersor\n",
+ "desied\n",
+ "milloins\n",
+ "startedthe\n",
+ "opperational\n",
+ "1960when\n",
+ "reciaves\n",
+ "insatnce\n",
+ "adorible\n",
+ "purposesof\n",
+ "impovred\n",
+ "Franced\n",
+ "phonesvideo\n",
+ "herer\n",
+ "engins\n",
+ "abondens\n",
+ "nochance\n",
+ "crapy\n",
+ "groundlevel\n",
+ "alines\n",
+ "dangereous\n",
+ "Suburd\n",
+ "thezebra\n",
+ "examplewhen\n",
+ "stateAfter\n",
+ "personstudentteacher\n",
+ "teaachers\n",
+ "dagerous\n",
+ "groupteam\n",
+ "Alote\n",
+ "nondemocrac\n",
+ "mucle\n",
+ "extree\n",
+ "certine\n",
+ "assignmentstestsand\n",
+ "majoritys\n",
+ "partys00\n",
+ "litereally\n",
+ "theraphist\n",
+ "seventyfive\n",
+ "populraity\n",
+ "orgwikiMotor_vehicle_fatality_rate_in_US_by_year\n",
+ "Evenig\n",
+ "emotionalnally\n",
+ "daiy\n",
+ "35092\n",
+ "waythat\n",
+ "uneqe\n",
+ "instituitons\n",
+ "HellCat\n",
+ "tarvels\n",
+ "manfacturer\n",
+ "2976\n",
+ "ariseI\n",
+ "ruine\n",
+ "mulesand\n",
+ "vape\n",
+ "oculd\n",
+ "advanaced\n",
+ "fatasses\n",
+ "benifitail\n",
+ "Cowboing\n",
+ "schoolmiddle\n",
+ "situatiuon\n",
+ "supposwe\n",
+ "studensts\n",
+ "predidency\n",
+ "studends\n",
+ "slipoed\n",
+ "timeduring\n",
+ "tuoched\n",
+ "laugter\n",
+ "ditermine\n",
+ "ojective\n",
+ "everething\n",
+ "unmotiving\n",
+ "Overcorrecting\n",
+ "feelm\n",
+ "sucsseed\n",
+ "votethat\n",
+ "tehcnolgy\n",
+ "proprties\n",
+ "pacient\n",
+ "planneed\n",
+ "educatuion\n",
+ "relliy\n",
+ "vigerously\n",
+ "collegeFor\n",
+ "personshelp\n",
+ "governmant\n",
+ "foodpeople\n",
+ "Ellectoal\n",
+ "unburried\n",
+ "horiable\n",
+ "downtwon\n",
+ "maore\n",
+ "taxesand\n",
+ "college1\n",
+ "aesy\n",
+ "werr\n",
+ "caliores\n",
+ "impowered\n",
+ "queations\n",
+ "thinmk\n",
+ "friendlyfinding\n",
+ "enconutered\n",
+ "natraul\n",
+ "resoonse\n",
+ "enavles\n",
+ "reuces\n",
+ "Somg\n",
+ "thusand\n",
+ "perticular\n",
+ "selfmotivation\n",
+ "represenntitives\n",
+ "rockif\n",
+ "benefiet\n",
+ "relieabul\n",
+ "VOTORS\n",
+ "perserves\n",
+ "uncomfertable\n",
+ "nonsmart\n",
+ "pruis\n",
+ "unneccasary\n",
+ "teachthe\n",
+ "plure\n",
+ "experiencesknowledgeand\n",
+ "adknowlege\n",
+ "majortiy\n",
+ "pointout\n",
+ "820\n",
+ "atmoshphere\n",
+ "blery\n",
+ "talkIm\n",
+ "suppoded\n",
+ "perssure\n",
+ "dolimiting\n",
+ "develpoed\n",
+ "hardhowever\n",
+ "bakc\n",
+ "edmission\n",
+ "politicists\n",
+ "jamsroad\n",
+ "votebecause\n",
+ "peoplefriendly\n",
+ "nonexsitent\n",
+ "thoroughlywritten\n",
+ "inmortant\n",
+ "manifacture\n",
+ "relatiiobship\n",
+ "descion\n",
+ "useed\n",
+ "articleit\n",
+ "sentaor\n",
+ "Valledpar\n",
+ "youthenhey\n",
+ "rhole\n",
+ "interented\n",
+ "sincerally\n",
+ "slautered\n",
+ "compermize\n",
+ "studenttechnology\n",
+ "touramentsfencingboxingreadingwhittlingand\n",
+ "citiesstatesand\n",
+ "sportclubseven\n",
+ "computeranimation\n",
+ "drivingThe\n",
+ "mesalandofrms\n",
+ "cancled\n",
+ "ocur\n",
+ "alldriver\n",
+ "nonbias\n",
+ "flabergasting\n",
+ "hvaing\n",
+ "sargents\n",
+ "bestlaid\n",
+ "riduliously\n",
+ "endeavorsparagraph\n",
+ "offwith\n",
+ "carsintensive\n",
+ "sustainnot\n",
+ "outby\n",
+ "Anywaylike\n",
+ "homes1p2\n",
+ "recievetherefore\n",
+ "divr\n",
+ "permantely\n",
+ "socializng\n",
+ "hudred\n",
+ "aoutonomous\n",
+ "airplans\n",
+ "cabon\n",
+ "maybey\n",
+ "seesthe\n",
+ "carchances\n",
+ "respembles\n",
+ "catastropy\n",
+ "supossed\n",
+ "cocept\n",
+ "leaening\n",
+ "activitiesmany\n",
+ "sbasically\n",
+ "dusent\n",
+ "Canadiates\n",
+ "mannually\n",
+ "dummest\n",
+ "solvedAlthough\n",
+ "aritcle\n",
+ "Leanardo\n",
+ "Heyhow\n",
+ "rightabolish\n",
+ "transportaiont\n",
+ "illadvised\n",
+ "38\n",
+ "americ\n",
+ "Zakhareuski\n",
+ "unfait\n",
+ "prodess\n",
+ "consertrate\n",
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+ "ineresting\n",
+ "summerbut\n",
+ "clasified\n",
+ "chaneg\n",
+ "meaninfuly\n",
+ "thereafterNow\n",
+ "gamesand\n",
+ "Collegethere\n",
+ "havinng\n",
+ "buyong\n",
+ "statespassage\n",
+ "therre\n",
+ "Therere\n",
+ "thereThey\n",
+ "envirorment\n",
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+ "futer\n",
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+ "feelsbut\n",
+ "electionto\n",
+ "smileworryor\n",
+ "Transpotation\n",
+ "nonrenuable\n",
+ "anybodythere\n",
+ "photograh\n",
+ "expireneced\n",
+ "gicen\n",
+ "SAIDIN\n",
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+ "choaic\n",
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+ "spaceshuttle\n",
+ "cheaked\n",
+ "awareded\n",
+ "reaosn\n",
+ "wasd\n",
+ "eletric\n",
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+ "anilmals\n",
+ "releave\n",
+ "fiaaco\n",
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+ "Beijjing\n",
+ "peresident\n",
+ "organisum\n",
+ "amongside\n",
+ "487\n",
+ "winnere\n",
+ "bounderie\n",
+ "computerbased\n",
+ "reviws\n",
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+ "diffrents\n",
+ "streering\n",
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+ "Systemcan\n",
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+ "stion\n",
+ "oppurtnites\n",
+ "traspotantion\n",
+ "emissionsjust\n",
+ "MoreoverCitizens\n",
+ "optionsometimes\n",
+ "invisions\n",
+ "enteratinment\n",
+ "Paragrapgh\n",
+ "inschoolsuspensions\n",
+ "Surveyers\n",
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+ "comepletly\n",
+ "classput\n",
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+ "Sectin\n",
+ "privledes\n",
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+ "ectoral\n",
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+ "clssroom\n",
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+ "tempreature\n",
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+ "candidte\n",
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+ "steping\n",
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+ "consiter\n",
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+ "goand\n",
+ "presidecial\n",
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+ "beleieve\n",
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+ "deatail\n",
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+ "ELECTORS\n",
+ "trueThe\n",
+ "llook\n",
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+ "rotton\n",
+ "hoope\n",
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+ "1979\n",
+ "prefernce\n",
+ "turndownit\n",
+ "posibilty\n",
+ "ahigher\n",
+ "sizeso\n",
+ "fiascoThe\n",
+ "impotan\n",
+ "demestration\n",
+ "examble\n",
+ "kidsmy\n",
+ "ThirdWhen\n",
+ "menaing\n",
+ "seriouslyespecially\n",
+ "watere\n",
+ "candidatesBig\n",
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+ "librities\n",
+ "woner\n",
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+ "invenstment\n",
+ "emotionAlto\n",
+ "fourtyfour\n",
+ "IntroThe\n",
+ "endore\n",
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+ "fels\n",
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+ "forgetand\n",
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+ "RepublicansDemocrats\n",
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+ "questionsget\n",
+ "expirimental\n",
+ "Generic_school\n",
+ "computercontrolled\n",
+ "unfairoutdated\n",
+ "appolgized\n",
+ "winnertakeallsystem\n",
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+ "chidern\n",
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+ "timebut\n",
+ "tachnologies\n",
+ "538number\n",
+ "destantion\n",
+ "baseballbasketballtrack\n",
+ "omm\n",
+ "thereRadar\n",
+ "senosor\n",
+ "deiliver\n",
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+ "frncing\n",
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+ "agruements\n",
+ "Bombgerger\n",
+ "disscussion\n",
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+ "activitiesall\n",
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+ "massesbut\n",
+ "conclued\n",
+ "priceLEGOsthey\n",
+ "transportaiton\n",
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+ "secondsjust\n",
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+ "corbond\n",
+ "ninetyseven\n",
+ "atmsophere\n",
+ "300\n",
+ "mait\n",
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+ "Elctorial\n",
+ "frot\n",
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+ "naufacturer\n",
+ "machene\n",
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+ "somethingso\n",
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+ "HiMy\n",
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+ "2012to\n",
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+ "envirment\n",
+ "delegtion\n",
+ "completen\n",
+ "53\n",
+ "interprutation\n",
+ "PersontoPerson\n",
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+ "systemMilkyway\n",
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+ "sunthis\n",
+ "mulBtasking\n",
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+ "22011\n",
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+ "LastlyVoting\n",
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+ "sefldriving\n",
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+ "decet\n",
+ "dontlike\n",
+ "inhapitable\n",
+ "artsoccerfootballgolf\n",
+ "EarthP4\n",
+ "contenue\n",
+ "consetiment\n",
+ "negatibe\n",
+ "fantasticwait\n",
+ "automatci\n",
+ "dissipointing\n",
+ "1100\n",
+ "youvote\n",
+ "smaile\n",
+ "Idahosays\n",
+ "415\n",
+ "sosince\n",
+ "inventer\n",
+ "createa\n",
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+ "aspex\n",
+ "pnones\n",
+ "city10How\n",
+ "advanncemet\n",
+ "becuae\n",
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+ "resuply\n",
+ "Lisahe\n",
+ "Overpopulated\n",
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+ "moprning\n",
+ "extrarricular\n",
+ "expressins\n",
+ "conditiions\n",
+ "advancedment\n",
+ "bome\n",
+ "electionsthe\n",
+ "44th\n",
+ "nicerlooking\n",
+ "recessionbecause\n",
+ "talleys\n",
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+ "affter\n",
+ "avertid\n",
+ "experinental\n",
+ "throughtrying\n",
+ "contrie\n",
+ "thatthats\n",
+ "systematicaly\n",
+ "history1\n",
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+ "priocess\n",
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+ "beacauese\n",
+ "uphead\n",
+ "convinent\n",
+ "repaced\n",
+ "ttrough\n",
+ "qualilfied\n",
+ "restrants\n",
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+ "grusgt\n",
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+ "gigga\n",
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+ "Nisaan\n",
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+ "paragraph20\n",
+ "comortable\n",
+ "MArs\n",
+ "congestian\n",
+ "kniw\n",
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+ "Selfesteem\n",
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+ "Hihg\n",
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+ "jeopardising\n",
+ "Chinaover\n",
+ "hersle\n",
+ "FahrenheitAtmospheric\n",
+ "hostiptal\n",
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+ "accominate\n",
+ "bren\n",
+ "pargagraph\n",
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+ "Thoughs\n",
+ "hours2\n",
+ "thisstudents\n",
+ "acdemics\n",
+ "computersanimated\n",
+ "purposley\n",
+ "liveing\n",
+ "motorised\n",
+ "neerby\n",
+ "2paragraph\n",
+ "jointhe\n",
+ "83\n",
+ "Thatll\n",
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+ "activetylike\n",
+ "somedbody\n",
+ "intelleget\n",
+ "blunty\n",
+ "tremondus\n",
+ "distrabution\n",
+ "doesint\n",
+ "proccessnot\n",
+ "bakeoffs\n",
+ "gGlobal\n",
+ "planetThe\n",
+ "donkeybut\n",
+ "humainity\n",
+ "pictuure\n",
+ "teentoteen\n",
+ "Plazas3p24\n",
+ "Congerssional\n",
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+ "Bme\n",
+ "familiat\n",
+ "Intelegence\n",
+ "insureance\n",
+ "septemberthe\n",
+ "preparede\n",
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+ "delegationone\n",
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+ "nearcollision\n",
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+ "contrivsory\n",
+ "coworkings\n",
+ "sidewalkes\n",
+ "NASAsi\n",
+ "par28\n",
+ "highresolution\n",
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+ "suppliesand\n",
+ "convinse\n",
+ "destinatons\n",
+ "bikeit\n",
+ "inusre\n",
+ "knownwell\n",
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+ "secced\n",
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+ "aruguments\n",
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+ "whatÃÂs\n",
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+ "extraculliclar\n",
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+ "friendsfamilyand\n",
+ "babysitte\n",
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+ "workmaking\n",
+ "DECCA\n",
+ "ChinaGreeceEuropeVeniceitaly\n",
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+ "httpsexchange\n",
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+ "cponducting\n",
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+ "73\n",
+ "nessesities\n",
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+ "instructual\n",
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+ "reasonsit\n",
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+ "lunchesvending\n",
+ "unnoitcable\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "oppinouns\n",
+ "landscap\n",
+ "missourilawyers\n",
+ "willstill\n",
+ "discorvering\n",
+ "Technologyweapons\n",
+ "moodmentality\n",
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+ "eightgraders\n",
+ "drunck\n",
+ "siblibgs\n",
+ "studided\n",
+ "solfware\n",
+ "trafficand\n",
+ "secendly\n",
+ "Autonymous\n",
+ "truying\n",
+ "elington\n",
+ "voteWHY\n",
+ "home75\n",
+ "triumps\n",
+ "43m\n",
+ "bomberger\n",
+ "mechanicalbuilt\n",
+ "custmer\n",
+ "deisels\n",
+ "liqeufy\n",
+ "woudnt\n",
+ "workand\n",
+ "depate\n",
+ "winnerEach\n",
+ "modavted\n",
+ "pther\n",
+ "schoolissued\n",
+ "Conclusionso\n",
+ "extracurectular\n",
+ "whoes\n",
+ "Leonedo\n",
+ "emphisizable\n",
+ "nessessities\n",
+ "engoing\n",
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+ "selfgiven\n",
+ "Regargless\n",
+ "studentsas\n",
+ "newerand\n",
+ "baseballvolleyballtabletennis\n",
+ "Bomberberg\n",
+ "breakweather\n",
+ "possability\n",
+ "counsling\n",
+ "Segrigationists\n",
+ "favoret\n",
+ "resembeld\n",
+ "538\n",
+ "guiedece\n",
+ "answerd\n",
+ "Idont\n",
+ "showning\n",
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+ "lesserfortunate\n",
+ "moneythey\n",
+ "Adomatic\n",
+ "seashe\n",
+ "sacecraft\n",
+ "everythong\n",
+ "roadthe\n",
+ "centurys\n",
+ "harmes\n",
+ "occations\n",
+ "smogThe\n",
+ "vocanoes\n",
+ "clasroom\n",
+ "translaters\n",
+ "Posenthal\n",
+ "agasinst\n",
+ "photoAs\n",
+ "algortihms\n",
+ "softwaredata\n",
+ "evntually\n",
+ "ocasionally\n",
+ "VeniceItlay\n",
+ "fanatsy\n",
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+ "exantly\n",
+ "casr\n",
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+ "deadlockes\n",
+ "studywhen\n",
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+ "unnoticable\n",
+ "satelliterelated\n",
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+ "obviouse\n",
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+ "orglegalarticlestextingwhiledriving40319\n",
+ "likeGoogles\n",
+ "sociol\n",
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+ "safeplace\n",
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+ "conferenceor\n",
+ "democracey\n",
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+ "optins\n",
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+ "fourtionor\n",
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+ "org20091116teensanddistracteddriving\n",
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+ "elevtors\n",
+ "19680and\n",
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+ "coiuldnt\n",
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+ "couldagain\n",
+ "evnironment\n",
+ "2075\n",
+ "helpshe\n",
+ "demovratic\n",
+ "pedistrians\n",
+ "classesonline\n",
+ "transfors\n",
+ "ocurrance\n",
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+ "Cololege\n",
+ "statedBecause\n",
+ "frustratation\n",
+ "sesing\n",
+ "574\n",
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+ "signscinesetc\n",
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+ "twentysecond\n",
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+ "miight\n",
+ "potintsion\n",
+ "Avacadoes\n",
+ "injured9\n",
+ "euors\n",
+ "fedat\n",
+ "columbian\n",
+ "120mph\n",
+ "offchange\n",
+ "assignemnt\n",
+ "tadays\n",
+ "dimise\n",
+ "dicesion\n",
+ "electecity\n",
+ "readThe\n",
+ "temperatuer\n",
+ "SCHOOI\n",
+ "opertuntity\n",
+ "Cernaint\n",
+ "Geece\n",
+ "buyist\n",
+ "canhave\n",
+ "firday\n",
+ "ordanary\n",
+ "alottment\n",
+ "noschool\n",
+ "hardestits\n",
+ "presidentil\n",
+ "canael\n",
+ "constructionzone\n",
+ "dangerus\n",
+ "muscule\n",
+ "arginol\n",
+ "thingslast\n",
+ "selfabled\n",
+ "trifolds\n",
+ "slideassignment\n",
+ "physicall\n",
+ "recrate\n",
+ "candidatesknowing\n",
+ "strategys\n",
+ "enviomental\n",
+ "studentdeigned\n",
+ "countriesWe\n",
+ "2019\n",
+ "bascilly\n",
+ "facinatedby\n",
+ "challengeing\n",
+ "sayig\n",
+ "socicl\n",
+ "cattlefood\n",
+ "mesaures\n",
+ "expiereance\n",
+ "planetthan\n",
+ "CarFree\n",
+ "asnwer\n",
+ "facilite\n",
+ "recks\n",
+ "outcoem\n",
+ "likkly\n",
+ "truethfuly\n",
+ "provent\n",
+ "2Everyones\n",
+ "Reahabilitation\n",
+ "graduatied\n",
+ "regliously\n",
+ "greatyly\n",
+ "insocial\n",
+ "procced\n",
+ "veichles\n",
+ "faceID\n",
+ "photograpghy\n",
+ "Fothermore\n",
+ "disreguarded\n",
+ "advangtage\n",
+ "IMPEDIMENT\n",
+ "trigonmetry\n",
+ "Eygptianstyle\n",
+ "thereats\n",
+ "clouddrapped\n",
+ "acidresistent\n",
+ "watchgaurd\n",
+ "elimanted\n",
+ "Induvial\n",
+ "easiter\n",
+ "secondI\n",
+ "Venusthis\n",
+ "perputation\n",
+ "conntrol\n",
+ "Pararaph\n",
+ "menuver\n",
+ "deffinitaly\n",
+ "Imagne\n",
+ "dischargedLuke\n",
+ "intruptions\n",
+ "populure\n",
+ "heald\n",
+ "lyfts\n",
+ "partsect\n",
+ "becausewhy\n",
+ "Takinf\n",
+ "232\n",
+ "naughborhood\n",
+ "accustomes\n",
+ "jeffs\n",
+ "temetures\n",
+ "rockssafely\n",
+ "killl\n",
+ "secondto\n",
+ "deppresion\n",
+ "noiseses\n",
+ "casws\n",
+ "teast\n",
+ "mallwatch\n",
+ "umited\n",
+ "Californiawho\n",
+ "inlimited\n",
+ "busyer\n",
+ "fantisy\n",
+ "obvisouly\n",
+ "raditation\n",
+ "phycially\n",
+ "pecie\n",
+ "approches\n",
+ "teachergives\n",
+ "cpoint\n",
+ "consideredrespected\n",
+ "decleration\n",
+ "syack\n",
+ "stressSource\n",
+ "lifesare\n",
+ "genertation\n",
+ "decre4ases\n",
+ "teacers\n",
+ "deiselengines\n",
+ "columbias\n",
+ "twentyfirst\n",
+ "nerdwallet\n",
+ "onbord\n",
+ "reconginition\n",
+ "conspiacy\n",
+ "shorterlonger\n",
+ "wellmade\n",
+ "pantrys\n",
+ "wordillusion\n",
+ "presidentThis\n",
+ "therebyor\n",
+ "snooth\n",
+ "echanical\n",
+ "emoitional\n",
+ "choosesI\n",
+ "systemthe\n",
+ "swinglike\n",
+ "livelike\n",
+ "scecond\n",
+ "evelated\n",
+ "familiesand\n",
+ "twinkal\n",
+ "poplular\n",
+ "decisionso\n",
+ "crorrosive\n",
+ "surveyour\n",
+ "assiatant\n",
+ "geolgoy\n",
+ "assemblys\n",
+ "elrection\n",
+ "assumptionsstatements\n",
+ "countryHere\n",
+ "Plumet\n",
+ "Bejings\n",
+ "desnity\n",
+ "citizensSource\n",
+ "needwant\n",
+ "compelety\n",
+ "methodstechniques\n",
+ "REGREAT\n",
+ "scolarship\n",
+ "honers\n",
+ "forwand\n",
+ "stuipd\n",
+ "winnertakesall\n",
+ "conclucion\n",
+ "ecah\n",
+ "inconvienent\n",
+ "diging\n",
+ "carbidethough\n",
+ "smilingbut\n",
+ "hihelloor\n",
+ "repacing\n",
+ "possibel\n",
+ "treefossil\n",
+ "preforn\n",
+ "illogicallets\n",
+ "manority\n",
+ "distace\n",
+ "rumming\n",
+ "nyquil\n",
+ "drivlerless\n",
+ "unnecessay\n",
+ "equality8\n",
+ "sponserships\n",
+ "dodont\n",
+ "historyeducation\n",
+ "mjority\n",
+ "contrate\n",
+ "surpriseangerdisgust\n",
+ "nesscary\n",
+ "photosyou\n",
+ "Automible\n",
+ "exricse\n",
+ "beyandassociates\n",
+ "everyonce\n",
+ "focast\n",
+ "soldiernursedoctor\n",
+ "dayWell\n",
+ "persendent\n",
+ "understandnd\n",
+ "pymarids\n",
+ "forover\n",
+ "matrial\n",
+ "exampleObama\n",
+ "ovisely\n",
+ "peoplecountries\n",
+ "anaylze\n",
+ "balacing\n",
+ "fromation\n",
+ "pulltion\n",
+ "Inschool\n",
+ "topicLet\n",
+ "sussesful\n",
+ "tuseday\n",
+ "itsenviorment\n",
+ "mouthfunction\n",
+ "presidentthey\n",
+ "attetntion\n",
+ "baseballand\n",
+ "Certainity\n",
+ "boredTeachers\n",
+ "ususually\n",
+ "prodeuces\n",
+ "desiciosns\n",
+ "FCPSOn\n",
+ "electorssenators\n",
+ "enbedded\n",
+ "stdent\n",
+ "LisaMakeing\n",
+ "logicempty\n",
+ "eletors\n",
+ "UNNRRA\n",
+ "THousands\n",
+ "beforelike\n",
+ "hardGoing\n",
+ "withbthe\n",
+ "astetics\n",
+ "bussnesses\n",
+ "nicebut\n",
+ "precuations\n",
+ "kess\n",
+ "unblur\n",
+ "complecates\n",
+ "myselfso\n",
+ "convinc\n",
+ "purschaing\n",
+ "releive\n",
+ "Studets\n",
+ "VAUBANGermany\n",
+ "mirrorsthere\n",
+ "politcian\n",
+ "persoon\n",
+ "finacianal\n",
+ "ralliesdancesor\n",
+ "reatinging\n",
+ "elecations\n",
+ "comlegaladvicetopcausesofcaraccidents\n",
+ "atuopilot\n",
+ "WhileSome\n",
+ "sharpper\n",
+ "smartif\n",
+ "noticify\n",
+ "tookphotographs\n",
+ "Electorol\n",
+ "languiges\n",
+ "fust\n",
+ "UberLyftrideshare\n",
+ "sickgot\n",
+ "prioities\n",
+ "comepeat\n",
+ "scientifc\n",
+ "CANIDETS\n",
+ "Youhave\n",
+ "Carbondioxide\n",
+ "constructus\n",
+ "oceansriversvalleysand\n",
+ "repleting\n",
+ "populor\n",
+ "selfmanaging\n",
+ "nvenus\n",
+ "drasically\n",
+ "masacres\n",
+ "chcek\n",
+ "phycology\n",
+ "evereything\n",
+ "guve\n",
+ "Egtptianstyle\n",
+ "EuropeGreeceor\n",
+ "occationally\n",
+ "hundread\n",
+ "immedietly\n",
+ "sometghing\n",
+ "taxiss\n",
+ "937\n",
+ "indevior\n",
+ "inconclusion\n",
+ "incentuve\n",
+ "1996But\n",
+ "poissibly\n",
+ "simiply\n",
+ "pschologist\n",
+ "insructional\n",
+ "phoneit\n",
+ "conditionsAlso\n",
+ "12711\n",
+ "gorcery\n",
+ "Representitves\n",
+ "threws\n",
+ "3it\n",
+ "escpecially\n",
+ "bielive\n",
+ "thereafteraccording\n",
+ "lanndforms\n",
+ "nromal\n",
+ "ummmmmmm\n",
+ "towardsoften\n",
+ "assighn\n",
+ "to1976\n",
+ "responsibilaties\n",
+ "acuatlly\n",
+ "inpack\n",
+ "distracBng\n",
+ "atow\n",
+ "1888This\n",
+ "Adui\n",
+ "gaven\n",
+ "technologyhave\n",
+ "Becouse\n",
+ "everywherethey\n",
+ "computurized\n",
+ "thumg\n",
+ "civiliation\n",
+ "tableand\n",
+ "accidences\n",
+ "addicional\n",
+ "wourld\n",
+ "convenientbut\n",
+ "siduations\n",
+ "croosed\n",
+ "hthiother\n",
+ "jto\n",
+ "sining\n",
+ "relaxe\n",
+ "fullsaid\n",
+ "wildit\n",
+ "tracksomething\n",
+ "mareled\n",
+ "disrruption\n",
+ "cratersmountainand\n",
+ "hmuanity\n",
+ "anlantic\n",
+ "carintensive\n",
+ "werried\n",
+ "becusae\n",
+ "animalssupplies\n",
+ "awedome\n",
+ "dayGeneric_Name\n",
+ "methodshouldremain\n",
+ "electorsbut\n",
+ "unbareably\n",
+ "Teacherdesign\n",
+ "technollogy\n",
+ "electorsand\n",
+ "bisociety\n",
+ "ablie\n",
+ "sciencefiction\n",
+ "indside\n",
+ "resolveeven\n",
+ "doown\n",
+ "studentsdesigned\n",
+ "methids\n",
+ "cahnge\n",
+ "hosbital\n",
+ "arrouses\n",
+ "envorinment\n",
+ "entertaniment\n",
+ "travles\n",
+ "presious\n",
+ "aroiund\n",
+ "twoIn\n",
+ "oppenets\n",
+ "definitally\n",
+ "Bacause\n",
+ "soures\n",
+ "allbut\n",
+ "skeptisism\n",
+ "voteres\n",
+ "oldI\n",
+ "yesbut\n",
+ "imiting\n",
+ "bill80\n",
+ "pormote\n",
+ "monuvers\n",
+ "respecfulness\n",
+ "18wich\n",
+ "attepmting\n",
+ "machiene\n",
+ "explantions\n",
+ "capmpaign\n",
+ "quastions\n",
+ "contineousely\n",
+ "481000\n",
+ "mouthains\n",
+ "percentthere\n",
+ "Electorals\n",
+ "sucsesfull\n",
+ "asserstions\n",
+ "WinnerTakesAll\n",
+ "rocketscience\n",
+ "1100am\n",
+ "problemsAutomakers\n",
+ "conprehended\n",
+ "GoogleBMWGMMercedesBenzAudi\n",
+ "rearveiw\n",
+ "measruements\n",
+ "foarm\n",
+ "emotionsDAlto\n",
+ "thinkn\n",
+ "santor\n",
+ "pollued\n",
+ "happinesssurpriseangerdigustfear\n",
+ "aweinspiring\n",
+ "9yearold\n",
+ "Earhtlike\n",
+ "simlilar\n",
+ "BBCSTREET\n",
+ "onserve\n",
+ "friendit\n",
+ "examplethe\n",
+ "stretche\n",
+ "managabel\n",
+ "menwomen\n",
+ "becalled\n",
+ "geeting\n",
+ "attensiion\n",
+ "strared\n",
+ "priorieties\n",
+ "smeli\n",
+ "grogery\n",
+ "handeled\n",
+ "getg\n",
+ "accociated\n",
+ "willchange\n",
+ "inbuilding\n",
+ "eithier\n",
+ "injued\n",
+ "respensint\n",
+ "knolledge\n",
+ "made2\n",
+ "themselvs\n",
+ "waterYou\n",
+ "amendemnt\n",
+ "imphasizes\n",
+ "medeor\n",
+ "sulfaric\n",
+ "pixles\n",
+ "intoxenanated\n",
+ "foundig\n",
+ "Okayyesit\n",
+ "82001\n",
+ "mainl\n",
+ "sincearly\n",
+ "unconfortable\n",
+ "statesnotify\n",
+ "misleding\n",
+ "automessage\n",
+ "differt\n",
+ "togeter\n",
+ "aboutthose\n",
+ "COVID19\n",
+ "realImagine\n",
+ "independetly\n",
+ "vesalls\n",
+ "carswhich\n",
+ "foney\n",
+ "bealive\n",
+ "vieicle\n",
+ "recomendable\n",
+ "timeson\n",
+ "comuters\n",
+ "environmentm\n",
+ "hgives\n",
+ "carhow\n",
+ "perpestictive\n",
+ "contentHandsFree\n",
+ "ideaSuch\n",
+ "EAch\n",
+ "Seagoign\n",
+ "speaceship\n",
+ "schoolwhat\n",
+ "tocertian\n",
+ "deskandchalkboard\n",
+ "hustleandbustle\n",
+ "habitthat\n",
+ "simulatiing\n",
+ "Butits\n",
+ "tvor\n",
+ "paymants\n",
+ "Accuratey\n",
+ "timesthe\n",
+ "someonesomething\n",
+ "bikewalk\n",
+ "disvalues\n",
+ "finded\n",
+ "disaes\n",
+ "Californina\n",
+ "iut\n",
+ "secdonly\n",
+ "nintyseven\n",
+ "inocennt\n",
+ "resouceslike\n",
+ "muscels\n",
+ "contineau\n",
+ "identitfy\n",
+ "srate\n",
+ "anddesine\n",
+ "abbility\n",
+ "winnertakeall\n",
+ "caare\n",
+ "prusite\n",
+ "foccuse\n",
+ "themsekves\n",
+ "twinA\n",
+ "createdbecause\n",
+ "paragraoh\n",
+ "econdomy\n",
+ "thedriver\n",
+ "advanving\n",
+ "enjoybal\n",
+ "110\n",
+ "trhat\n",
+ "whyhe\n",
+ "sadnes\n",
+ "esepially\n",
+ "Lastlysome\n",
+ "courosity\n",
+ "Northeastetchas\n",
+ "couting\n",
+ "Professer\n",
+ "particularle\n",
+ "sceince\n",
+ "hisher\n",
+ "pruchase\n",
+ "selfies\n",
+ "priveliges\n",
+ "Tecahers\n",
+ "drivier\n",
+ "mcould\n",
+ "conspiraces\n",
+ "surfice\n",
+ "outway\n",
+ "gifen\n",
+ "anachronish\n",
+ "solutionVenus\n",
+ "smille\n",
+ "Movment\n",
+ "displin\n",
+ "dilevery\n",
+ "representator\n",
+ "faceWhen\n",
+ "Europeduring\n",
+ "inhailing\n",
+ "Secondby\n",
+ "wasWell\n",
+ "3433\n",
+ "25mins\n",
+ "depris\n",
+ "implie\n",
+ "persion\n",
+ "corses\n",
+ "devistate\n",
+ "adultor\n",
+ "nessassary\n",
+ "whatd\n",
+ "speeking\n",
+ "rewardless\n",
+ "advantaes\n",
+ "2Pharagraph\n",
+ "eemotion\n",
+ "candatite\n",
+ "spaceshuttles\n",
+ "noneone\n",
+ "andlighting\n",
+ "expenxive\n",
+ "2001a\n",
+ "beginingabout\n",
+ "regualations\n",
+ "tolde\n",
+ "aximum\n",
+ "IfeellikeIknoweveryone\n",
+ "1983it\n",
+ "solty\n",
+ "nombers\n",
+ "eround\n",
+ "cretures\n",
+ "impares\n",
+ "calcutions\n",
+ "comprihend\n",
+ "onein\n",
+ "Paragrah\n",
+ "peoplew\n",
+ "pressuure\n",
+ "anygiven\n",
+ "tradadition\n",
+ "manuverbility\n",
+ "facsFacial\n",
+ "undergroud\n",
+ "representativesbut\n",
+ "residance\n",
+ "thanus\n",
+ "money1919\n",
+ "oboma\n",
+ "dagers\n",
+ "counterarguing\n",
+ "adventually\n",
+ "obamageorge\n",
+ "precentation\n",
+ "outcomeeverystates\n",
+ "teachersfriendsand\n",
+ "saftly\n",
+ "expersience\n",
+ "FirstDriveless\n",
+ "WWoosterand\n",
+ "electection\n",
+ "beginyou\n",
+ "stupides\n",
+ "atmosher\n",
+ "bages\n",
+ "bcan\n",
+ "liscenes\n",
+ "adavnced\n",
+ "outcomeEveryones\n",
+ "collecet\n",
+ "beniffical\n",
+ "couded\n",
+ "conclucionAs\n",
+ "disinged\n",
+ "otherso\n",
+ "onlinefor\n",
+ "beenn\n",
+ "aninals\n",
+ "RiskFreeNational\n",
+ "problmes\n",
+ "travleing\n",
+ "speculaters\n",
+ "peoploe\n",
+ "destractions\n",
+ "thatelectoral\n",
+ "scchool\n",
+ "manfacuture\n",
+ "Amost\n",
+ "tutour\n",
+ "pressuire\n",
+ "Fahrenhet\n",
+ "aniamls\n",
+ "breakand\n",
+ "revitialize\n",
+ "computre\n",
+ "bodyand\n",
+ "fanominal\n",
+ "albulms\n",
+ "sputering\n",
+ "spraysetc\n",
+ "Heidurn\n",
+ "Cowbiy\n",
+ "inivations\n",
+ "inncident\n",
+ "wwwindependentmotorscom\n",
+ "gottenTheyre\n",
+ "techonolgy\n",
+ "presidints\n",
+ "Jummy\n",
+ "splended\n",
+ "Atlantisc\n",
+ "specieshow\n",
+ "surveypr\n",
+ "Sonetines\n",
+ "favorism\n",
+ "poeplepeople\n",
+ "classoms\n",
+ "extress\n",
+ "perfectlyu\n",
+ "congergations\n",
+ "shwos\n",
+ "accedentd\n",
+ "poplution\n",
+ "voyge\n",
+ "1500\n",
+ "selfparking\n",
+ "positves\n",
+ "StatesElectoral\n",
+ "saysIt\n",
+ "cabaple\n",
+ "secrect\n",
+ "caredully\n",
+ "prorcees\n",
+ "guruloungephowmuchdoridesharedriversuberandlyftmakeinatlantaga\n",
+ "Knownig\n",
+ "twocents\n",
+ "inceaced\n",
+ "bendz\n",
+ "desent\n",
+ "biasim\n",
+ "smoggreen\n",
+ "sadhappy\n",
+ "hheidun\n",
+ "varing\n",
+ "Moreoverusing\n",
+ "aiens\n",
+ "existis\n",
+ "worthty\n",
+ "selectiing\n",
+ "aroundthat\n",
+ "redident\n",
+ "easieronline\n",
+ "accidetns\n",
+ "sensore\n",
+ "emitiones\n",
+ "VeniceItalythe\n",
+ "finnishing\n",
+ "Reprsentatives\n",
+ "devoring\n",
+ "citiescountries\n",
+ "everydaysometimes\n",
+ "penguinzs\n",
+ "possinble\n",
+ "factorsthe\n",
+ "idenfy\n",
+ "caar\n",
+ "compyter\n",
+ "continuaslly\n",
+ "debre\n",
+ "5days\n",
+ "abomished\n",
+ "scroit\n",
+ "presidentNext\n",
+ "wreckessly\n",
+ "Elemtary\n",
+ "imediate\n",
+ "genthanksadded\n",
+ "distrect\n",
+ "succseful\n",
+ "14x\n",
+ "Studens\n",
+ "stolling\n",
+ "Edubirdie\n",
+ "pursut\n",
+ "outofreach\n",
+ "actitude\n",
+ "usbut\n",
+ "Protecton\n",
+ "distrating\n",
+ "1327\n",
+ "haelped\n",
+ "praagraphs\n",
+ "liscened\n",
+ "especiallyon\n",
+ "desited\n",
+ "enoughstudents\n",
+ "pronalems\n",
+ "Gorethanks\n",
+ "brutial\n",
+ "lind\n",
+ "atmosphore\n",
+ "vters\n",
+ "cobows\n",
+ "envented\n",
+ "inherant\n",
+ "usauge\n",
+ "bacis\n",
+ "areit\n",
+ "chancewill\n",
+ "lineat\n",
+ "intesresting\n",
+ "esurance\n",
+ "Systemis\n",
+ "asits\n",
+ "bycicling\n",
+ "jusrt\n",
+ "adictive\n",
+ "soIn\n",
+ "Youtuber\n",
+ "immidately\n",
+ "impedments\n",
+ "Thousnad\n",
+ "saty\n",
+ "equalling\n",
+ "selfbeing\n",
+ "reasonible\n",
+ "perfered\n",
+ "cloudles\n",
+ "sententuries\n",
+ "populary\n",
+ "graduateand\n",
+ "MarsAfter\n",
+ "acurrate\n",
+ "segegaters\n",
+ "articule\n",
+ "quistion\n",
+ "3211\n",
+ "meanig\n",
+ "destack\n",
+ "seeboth\n",
+ "agruement\n",
+ "treammate\n",
+ "dentisis\n",
+ "unfriendl\n",
+ "seasonsummer\n",
+ "chalenging\n",
+ "natmosphere\n",
+ "suburbian\n",
+ "RomeEuropeand\n",
+ "surves\n",
+ "accidentways\n",
+ "carrey\n",
+ "freedomtrustand\n",
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+ "exspressses\n",
+ "grocey\n",
+ "communting\n",
+ "amissions\n",
+ "caculation\n",
+ "abismal\n",
+ "venusmake\n",
+ "computerdriving\n",
+ "requiere\n",
+ "threeit\n",
+ "illegar\n",
+ "testproject\n",
+ "favoitism\n",
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+ "in2012s\n",
+ "caind\n",
+ "experienceing\n",
+ "Vaunbans\n",
+ "Presisent\n",
+ "Fahrenhitpressure\n",
+ "licsence\n",
+ "flexiable\n",
+ "speen\n",
+ "does21\n",
+ "candidateYes\n",
+ "motovated\n",
+ "outgoingcould\n",
+ "Elcetoral\n",
+ "hotbing\n",
+ "insuracealot\n",
+ "thinkEspecially\n",
+ "6if\n",
+ "now2years\n",
+ "roughkly\n",
+ "expontential\n",
+ "sugguest\n",
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+ "instanly\n",
+ "indanger\n",
+ "drivierless\n",
+ "becausethere\n",
+ "confusong\n",
+ "thcomputer\n",
+ "1950\n",
+ "experssing\n",
+ "philidelphia\n",
+ "AVIDs\n",
+ "techfoodcitys\n",
+ "encouaged\n",
+ "crimanal\n",
+ "uang\n",
+ "unstaurated\n",
+ "airpollution\n",
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+ "utimately\n",
+ "comities\n",
+ "Unfourtanetly\n",
+ "whisle\n",
+ "differece\n",
+ "winnier\n",
+ "satisfact\n",
+ "driverthe\n",
+ "conviniences\n",
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+ "honduras\n",
+ "concusionI\n",
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+ "cafeul\n",
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+ "antoxicated\n",
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+ "candaiets\n",
+ "ChinaSouth\n",
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+ "effiecenty\n",
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+ "FirstPeople\n",
+ "classWhich\n",
+ "cercumstance\n",
+ "motorcyclesbikes\n",
+ "ticketsfines\n",
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+ "orgnaized\n",
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+ "perosnally\n",
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+ "progran\n",
+ "imbicles\n",
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+ "Anotheryou\n",
+ "pollutionstressand\n",
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+ "unprudent\n",
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+ "represetative\n",
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+ "intersesting\n",
+ "somethimg\n",
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+ "imporantly\n",
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+ "nessicerly\n",
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+ "candidtade\n",
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+ "irrationalbut\n",
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+ "skepicts\n",
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+ "Gooogle\n",
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+ "wiserbetter\n",
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+ "councilling\n",
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+ "wolud\n",
+ "atomospheric\n",
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+ "opputunities\n",
+ "creturehuman\n",
+ "ofthen\n",
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+ "eletronice\n",
+ "silicaon\n",
+ "swithcing\n",
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+ "driverlesscar\n",
+ "technonlgical\n",
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+ "teamsclubsor\n",
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+ "savning\n",
+ "Claiforina\n",
+ "saidYou\n",
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+ "citizensOffice\n",
+ "lungrelated\n",
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+ "orgteensstaysafedistracted_driving\n",
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+ "Scietists\n",
+ "eassily\n",
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+ "academicrelated\n",
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+ "probubly\n",
+ "campagning\n",
+ "semicomplex\n",
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+ "1968There\n",
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+ {
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+ "conclusionmany\n",
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+ "1470\n",
+ "stiutation\n",
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+ "perdicted\n",
+ "internded\n",
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+ "stressrelaxingand\n",
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+ "Presidant\n",
+ "Fiftyfive\n",
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+ "2195\n",
+ "tkae\n",
+ "acrcoss\n",
+ "modefy\n",
+ "atomopherethis\n",
+ "examle\n",
+ "rething\n",
+ "opinings\n",
+ "desion\n",
+ "joinig\n",
+ "thembecoming\n"
+ ]
+ },
+ {
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+ "output_type": "stream",
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+ "civilazation\n",
+ "UNRAA\n",
+ "_Dangers\n",
+ "dregees\n",
+ "oppsoite\n",
+ "freeks\n",
+ "oppint\n",
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+ "decisionsometimes\n",
+ "thorght\n",
+ "corresponders\n",
+ "18001990s\n",
+ "govdistracteddriving\n",
+ "jdefenses\n",
+ "anymor\n",
+ "mirro\n",
+ "coopretively\n",
+ "Challeneges\n",
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+ "countriessuch\n",
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+ "shess\n",
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+ "partmer\n",
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+ "Collegewe\n",
+ "distrac\n",
+ "themselvesas\n",
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+ "practicalparagraph\n",
+ "aritical\n",
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+ "Vaubanlike\n",
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+ "plazaandrew\n",
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+ "limithing\n",
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+ "getso\n",
+ "staetd\n",
+ "indeavor\n",
+ "decuction\n",
+ "toThis\n",
+ "communicationSo\n",
+ "Muls\n",
+ "88\n",
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+ "characteristcs\n",
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+ "nearlyempty\n",
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+ "unstead\n",
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+ "Secondlywhat\n",
+ "doand\n",
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+ "driverlss\n",
+ "comthetop3mentalhealthissuesvictimsfaceafteracaraccident\n",
+ "Alsohis\n",
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+ "saterday\n",
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+ "DrHuang\n",
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+ "childyouth\n",
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+ {
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+ "anxietybut\n",
+ "communicatre\n",
+ "humanasscistance\n",
+ "mechanicdoctor\n",
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+ "handicapt\n",
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+ "SINCERILY\n",
+ "horese\n",
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+ "smileing\n",
+ "childthey\n",
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+ "freedeom\n",
+ "carfully\n",
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+ "watse\n",
+ "atomhisphare\n",
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+ "imploying\n",
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+ "schoolsbecause\n",
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+ "familyreligious\n",
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+ "colecting\n",
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+ "frenbs\n",
+ "aprove\n",
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+ "thinkingwell\n",
+ "arroused\n",
+ "proboby\n",
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+ "standiard\n",
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+ "fourthirty\n",
+ "balabce\n",
+ "arepersonality\n",
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+ "2750\n",
+ "encounteron\n",
+ "techinally\n",
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+ "someoe\n",
+ "perrils\n",
+ "otheres\n",
+ "sadmador\n",
+ "offence\n",
+ "cotrollers\n",
+ "vehiclesaaaaaaaassz\n",
+ "detentionor\n",
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+ "atmosphereetc\n",
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+ "hname\n",
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+ "saows\n",
+ "32999\n",
+ "easiernot\n",
+ "regaurding\n",
+ "daythese\n",
+ "innefectiveTherefore\n",
+ "presidentRichard\n",
+ "supst\n",
+ "artrelated\n",
+ "picsand\n",
+ "electeral\n",
+ "womever\n",
+ "sonething\n",
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+ "slowerpaced\n",
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+ "utillize\n",
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+ "marvelouse\n",
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+ "Pamama\n",
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+ "greay\n",
+ "oublic\n",
+ "unelikely\n",
+ "geogrphics\n",
+ "distraced\n",
+ "dessision\n",
+ "knowthat\n",
+ "elcectoral\n",
+ "mp3situnes\n",
+ "presidentcy\n",
+ "conceler\n",
+ "callsetc\n",
+ "absalutly\n",
+ "Strangley\n"
+ ]
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+ "confusen\n",
+ "disaplince\n",
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+ "sadhappyangry\n",
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+ "alfaganastan\n",
+ "hered\n",
+ "youHelping\n",
+ "nonexciting\n",
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+ {
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+ "promblems\n",
+ "differenciate\n",
+ "vesue\n",
+ "Googlesdriverless\n",
+ "bogota\n",
+ "HaungFacial\n",
+ "studdied\n",
+ "2711\n",
+ "alertaslo\n",
+ "communicaiton\n",
+ "cornersof\n",
+ "goeson\n",
+ "coplicated\n",
+ "gudgets\n",
+ "texasfor\n",
+ "surprizingly\n",
+ "casecrossover\n",
+ "alian\n",
+ "itothers\n",
+ "buttemesa\n",
+ "labtops\n",
+ "opportunnity\n",
+ "thieir\n",
+ "devolped\n",
+ "doesnt\n",
+ "testsexams\n",
+ "goanyway\n",
+ "restaurantsand\n",
+ "Collegeaccording\n",
+ "stamia\n",
+ "emathize\n",
+ "intimedating\n",
+ "desatrouse\n",
+ "vauble\n",
+ "deminished\n",
+ "teacherdesgins\n",
+ "ilusion\n",
+ "recognitioon\n",
+ "exitement\n",
+ "carbondioxy\n",
+ "workschool\n",
+ "learningbut\n",
+ "VeniceItaly\n",
+ "necassity\n",
+ "collegeby\n",
+ "lwas\n",
+ "assustomed\n",
+ "musemes\n",
+ "extre\n",
+ "onlinevideo\n",
+ "perants\n",
+ "sacescrafts\n",
+ "1its\n",
+ "overwieght\n",
+ "2000source\n",
+ "weared\n",
+ "twentythree\n",
+ "citypara\n",
+ "Ultimatly\n",
+ "sekf\n",
+ "interesant\n",
+ "rommey\n",
+ "yourselfmaybe\n",
+ "beinging\n",
+ "nget\n",
+ "reconise\n",
+ "simultaniously\n",
+ "speciments\n",
+ "welldesigned\n",
+ "Democtratic\n",
+ "alternativ\n",
+ "estamation\n",
+ "growinh\n",
+ "segregrationists\n",
+ "RockFord\n",
+ "Transportationcommuting\n",
+ "bizarreshaped\n",
+ "exspressen\n",
+ "149\n",
+ "incorpurating\n",
+ "saidyou\n",
+ "upearly\n",
+ "presnt\n",
+ "friendsand\n",
+ "ollusion\n",
+ "22\n",
+ "topicyou\n",
+ "averqage\n",
+ "sastifed\n",
+ "sayiong\n",
+ "unconsiously\n",
+ "experessions\n",
+ "ranworked\n",
+ "devinetly\n",
+ "suted\n",
+ "fateal\n",
+ "responsiblilities\n",
+ "inpounded\n",
+ "withsand\n",
+ "reep\n",
+ "belowaverage\n",
+ "2020s\n",
+ "explorion\n",
+ "disatvantage\n",
+ "maintnance\n",
+ "BogataColumbia\n",
+ "wtith\n",
+ "levelbase\n",
+ "Wellwhen\n",
+ "segregationsists\n",
+ "evidemce\n",
+ "appreaciate\n",
+ "disination\n",
+ "homeowrk\n",
+ "aynamous\n",
+ "Insted\n",
+ "abues\n",
+ "617\n",
+ "qulity\n",
+ "analysing\n",
+ "inorested\n",
+ "sinces\n",
+ "manty\n",
+ "suppoting\n",
+ "informormation\n",
+ "buyt\n",
+ "opttion\n",
+ "highmaintenance\n",
+ "breack\n",
+ "disoders\n",
+ "studdies\n",
+ "airoxygen\n",
+ "unnderstand\n",
+ "2Now\n",
+ "extratrrestrial\n",
+ "compatition\n",
+ "conclusionextracurricular\n",
+ "deveoloped\n",
+ "electorr\n",
+ "discovere\n",
+ "earlyer\n",
+ "prve\n",
+ "affectig\n",
+ "respondsible\n",
+ "sistence\n",
+ "playersstudents\n",
+ "relectutant\n",
+ "cardepleted\n",
+ "revelad\n",
+ "Aboloshing\n",
+ "abilitiesand\n",
+ "promte\n",
+ "trroubled\n",
+ "quation\n",
+ "belioeve\n",
+ "blommed\n",
+ "susesful\n",
+ "oppurtunites\n",
+ "represnts\n",
+ "chermindis\n",
+ "entise\n",
+ "passageThat\n",
+ "Unmasing\n",
+ "ownbut\n",
+ "dangerspursue\n",
+ "assosiats\n",
+ "postitonestimating\n",
+ "RoyalRoce\n",
+ "emotionset\n",
+ "durind\n",
+ "democratric\n",
+ "candidatesometimes\n",
+ "afraind\n",
+ "emtional\n",
+ "useles\n",
+ "planetsnappping\n",
+ "believesDriveless\n",
+ "malappotrionment\n",
+ "countrysand\n",
+ "traumaBzing\n",
+ "menatal\n",
+ "INDENSIBLE\n",
+ "Kiesbye\n",
+ "TODE\n",
+ "onmly\n",
+ "ratherless\n",
+ "sometimeas\n",
+ "revisenoodletools\n",
+ "tentimes\n",
+ "foemed\n",
+ "imporatant\n",
+ "policmen\n",
+ "though12\n",
+ "polititinn\n",
+ "strikesto\n",
+ "oddnumber\n",
+ "concloution\n",
+ "comfrotable\n",
+ "varrious\n",
+ "andand\n",
+ "sesure\n",
+ "merterd\n",
+ "THose\n",
+ "conntected\n",
+ "neccary\n",
+ "moneywhch\n",
+ "elctors\n",
+ "diciplin\n",
+ "rebuidling\n",
+ "colege\n",
+ "canadate\n",
+ "reacte\n",
+ "scates\n",
+ "statemet\n",
+ "dilike\n",
+ "basenesses\n",
+ "arrise\n",
+ "goaldirected\n",
+ "citziens\n",
+ "caoable\n",
+ "individiuals\n",
+ "HistorySocial\n",
+ "Represeitatives\n",
+ "cool20\n",
+ "915am\n",
+ "schooldetention\n",
+ "Manufacters\n",
+ "codnitions\n",
+ "NflMlbFifa\n",
+ "eighteenyearold\n",
+ "waistes\n",
+ "conclusionBeing\n",
+ "needbecause\n",
+ "messageemailing\n",
+ "62\n",
+ "incinive\n",
+ "Staris\n",
+ "gonout\n",
+ "walkThere\n",
+ "succeptable\n",
+ "endevear\n",
+ "canidxent\n",
+ "245375\n",
+ "stearing\n",
+ "Francewhich\n",
+ "constroversy\n",
+ "whows\n",
+ "trsut\n",
+ "autotrophs\n",
+ "worldsince\n",
+ "quickbecause\n",
+ "collegde\n",
+ "negavtive\n",
+ "15000001\n",
+ "chese\n",
+ "protentially\n",
+ "wouldcould\n",
+ "proffesionalism\n",
+ "majoprity\n",
+ "absoulute\n",
+ "aswome\n",
+ "dearing\n",
+ "muscles3\n",
+ "terrifc\n",
+ "discombabulated\n",
+ "spuriousWe\n",
+ "estblished\n",
+ "008\n",
+ "LIDARit\n",
+ "tornamentsfencingboxingreadingwhittlingand\n",
+ "beliveved\n",
+ "whatching\n",
+ "knowshe\n",
+ "elactoral\n",
+ "Adminisrtation\n",
+ "solvoed\n",
+ "landscapeing\n",
+ "anachorsimsnot\n",
+ "sxpression\n",
+ "thinds\n",
+ "cosequences\n",
+ "smogthe\n",
+ "highst\n",
+ "jurrasticaly\n",
+ "nikname\n",
+ "exsprenol\n",
+ "senseand\n",
+ "learen\n",
+ "wowo\n",
+ "consonlors\n",
+ "throughsome\n",
+ "unfortable\n",
+ "acivemen\n",
+ "therights\n",
+ "reiably\n",
+ "classroon\n",
+ "votes538is\n",
+ "busybut\n",
+ "lightign\n",
+ "oppoion\n",
+ "reconzie\n",
+ "seventyone\n",
+ "isPerhaps\n",
+ "needfooddrinkspencils\n",
+ "bikeing\n",
+ "restablishment\n",
+ "Cydonnia\n",
+ "saysbut\n",
+ "hsould\n",
+ "beutaful\n",
+ "ploat\n",
+ "govconsumersguidesdangerstextingwhiledriving\n",
+ "unmannedbecause\n",
+ "nitros\n",
+ "latitiude\n",
+ "capitalsLast\n",
+ "bactireal\n",
+ "educationl\n",
+ "weekes\n",
+ "inviernment\n",
+ "Firstlyan\n",
+ "calulations\n",
+ "qquivalent\n",
+ "espeacially\n",
+ "dipicted\n",
+ "Stuying\n",
+ "bullyings\n",
+ "transpotion\n",
+ "358\n",
+ "chcking\n",
+ "studentnothing\n",
+ "recomed\n",
+ "hostlie\n",
+ "futurisitc\n",
+ "travling\n",
+ "inaccident\n",
+ "palnned\n",
+ "highfive\n",
+ "opertunities\n",
+ "caplble\n",
+ "focas\n",
+ "insintives\n",
+ "bankruping\n",
+ "slso\n",
+ "playcreate\n",
+ "citizans\n",
+ "maintianing\n",
+ "thatll\n",
+ "aquantinces\n",
+ "oustand\n",
+ "ADDADHD\n",
+ "breakes\n",
+ "shaprer\n",
+ "sprialing\n",
+ "democratsrepublicans\n",
+ "absoulte\n",
+ "offstudents\n",
+ "scertainment\n",
+ "natral\n",
+ "challengies\n",
+ "Enforement\n",
+ "ecieved\n",
+ "identfy\n",
+ "homeworkfilled\n",
+ "pluality\n",
+ "civleization\n",
+ "horendus\n",
+ "portrable\n",
+ "statisitcal\n",
+ "nedd\n",
+ "dispised\n",
+ "121\n",
+ "accidentparagraph\n",
+ "distracion\n",
+ "drivihng\n",
+ "survuvable\n",
+ "thisLets\n",
+ "appointmetns\n",
+ "emoltional\n",
+ "plegde\n",
+ "hadly\n",
+ "studentrunned\n",
+ "payibg\n",
+ "extrasurricular\n",
+ "apartyment\n",
+ "ssums\n",
+ "unveilded\n",
+ "chechout\n",
+ "electione\n",
+ "oversaes\n",
+ "someuseful\n",
+ "princples\n",
+ "AustraliaGermany\n",
+ "colloege\n",
+ "mthis\n",
+ "astablished\n",
+ "smogrivaled\n",
+ "consoulor\n",
+ "intiminating\n",
+ "08\n",
+ "creachers\n",
+ "petherance\n",
+ "Starbucksmaybe\n",
+ "woyukd\n",
+ "acelerating\n",
+ "alrets\n",
+ "constituiton\n",
+ "hasle\n",
+ "Dispise\n",
+ "Venus5\n",
+ "diegosing\n",
+ "desagree\n",
+ "Aceable\n",
+ "pullited\n",
+ "germafobic\n",
+ "mounterd\n",
+ "annything\n",
+ "percentafter\n",
+ "LAZYWHEN\n",
+ "viloence\n",
+ "agedistance\n",
+ "enetertainment\n",
+ "exsisted\n",
+ "carefluol\n",
+ "differentes\n",
+ "malnfunctioned\n",
+ "presidentional\n",
+ "stressafter\n",
+ "teachdesigned\n",
+ "drveless\n",
+ "thebalance\n",
+ "manslughter\n",
+ "regeardless\n",
+ "bigining\n",
+ "youo\n",
+ "anohter\n",
+ "soruce\n",
+ "agene\n",
+ "757\n",
+ "CORVID19\n",
+ "teamdance\n",
+ "100000\n",
+ "cenury\n",
+ "leadeers\n",
+ "confuced\n",
+ "bimp\n",
+ "sometims\n",
+ "Unmaskin\n",
+ "minipulated\n",
+ "saidtyped\n",
+ "ideadesign\n",
+ "viedo\n",
+ "billoner\n",
+ "Electorlal\n",
+ "Defesnes\n",
+ "sometung\n",
+ "conferencingstudents\n",
+ "joby\n",
+ "onlineattendance\n",
+ "farmes\n",
+ "suggiested\n",
+ "personlitly\n",
+ "consitly\n",
+ "thihs\n",
+ "becoimg\n",
+ "patentially\n",
+ "I95\n",
+ "apperars\n",
+ "planningand\n",
+ "terchonology\n",
+ "neighborsfriends\n",
+ "redudced\n",
+ "livingh\n",
+ "elecotor\n",
+ "themslelves\n",
+ "aurthour\n",
+ "dcide\n",
+ "stuided\n",
+ "haopens\n",
+ "weshould\n",
+ "congressThe\n",
+ "techknowlegy\n",
+ "himselfherself\n",
+ "withwell\n",
+ "accuarcy\n",
+ "carnot\n",
+ "representable\n",
+ "eightynine\n",
+ "photots\n",
+ "situationwhich\n",
+ "regonizes\n",
+ "espiecally\n",
+ "alsothey\n",
+ "environements\n",
+ "allotmeant\n",
+ "endso\n",
+ "Asronomers\n",
+ "particapate\n",
+ "atmaspher\n",
+ "deoends\n",
+ "ffew\n",
+ "kidsadultsstudentsteachers\n",
+ "exslpore\n",
+ "rovor\n",
+ "winnong\n",
+ "cattleboating\n",
+ "interseting\n",
+ "charcteristic\n",
+ "queestion\n",
+ "mountianhills\n",
+ "tectnology\n",
+ "searve\n",
+ "perefect\n",
+ "Respresentives\n",
+ "caslte\n",
+ "perrt\n",
+ "shose\n",
+ "easist\n",
+ "worksbetter\n",
+ "actullay\n",
+ "otherity\n",
+ "SystemFACS\n",
+ "posiablity\n",
+ "CELLULAUR\n",
+ "marsi\n",
+ "reasonyou\n",
+ "irrantional\n",
+ "colassal\n",
+ "Huag\n",
+ "Lasty\n",
+ "determan\n",
+ "waytotally\n",
+ "industrialiaztion\n",
+ "priceses\n",
+ "tremendousally\n",
+ "exscuse\n",
+ "barreries\n",
+ "personially\n",
+ "condidtions\n",
+ "illusoin\n",
+ "similiarites\n",
+ "unintrested\n",
+ "sadnesseven\n",
+ "decisionbetter\n",
+ "thme\n",
+ "pelote\n",
+ "BEABLE\n",
+ "elecctors\n",
+ "imagey\n",
+ "liike\n",
+ "advancments\n",
+ "fromFreedom\n",
+ "emotionel\n",
+ "Vanus\n",
+ "citezens\n",
+ "thinkt\n",
+ "gussing\n",
+ "termenously\n",
+ "TEXTED\n",
+ "carsand\n",
+ "conseqeunces\n",
+ "allthey\n",
+ "develeoped\n",
+ "simmilar\n",
+ "conclsion\n",
+ "vedeo\n",
+ "expeacially\n",
+ "legilature\n",
+ "Ascertainmentwhich\n",
+ "Heidun\n",
+ "recuire\n",
+ "refuring\n",
+ "Regiser\n",
+ "awayon\n",
+ "VicePresidents\n",
+ "forvever\n",
+ "eyesparagraph8Im\n",
+ "treapist\n",
+ "garanteed\n",
+ "asssumption\n",
+ "incentivised\n",
+ "examplepublic\n",
+ "indiffernt\n",
+ "cietzen\n",
+ "lacidazicle\n",
+ "carWhen\n",
+ "listenconnect\n",
+ "skeptis\n",
+ "domw\n",
+ "Aluthough\n",
+ "smiplified\n",
+ "theachers\n",
+ "anteanas\n",
+ "nonreliable\n",
+ "impacient\n",
+ "examles\n",
+ "entertan\n",
+ "copuld\n",
+ "prospetive\n",
+ "illegle\n",
+ "consit\n",
+ "authorAndrew\n",
+ "70yearold\n",
+ "adultsteachers\n",
+ "agurging\n",
+ "succors\n",
+ "huffpost\n",
+ "fehreheit\n",
+ "ciltures\n",
+ "excellectual\n",
+ "closetight\n",
+ "automotavies\n",
+ "coudnt\n",
+ "instutude\n",
+ "slowlybut\n",
+ "madwhich\n",
+ "Earthit\n",
+ "charator\n",
+ "entiltled\n",
+ "reimmerse\n",
+ "SYSETM\n",
+ "animathed\n",
+ "driveDriverless\n",
+ "deasead\n",
+ "apperence\n",
+ "peresidential\n",
+ "15sec\n",
+ "alsome\n",
+ "afride\n",
+ "conceptand\n",
+ "furel\n",
+ "phonemusicand\n",
+ "persents\n",
+ "carbideparagraph\n",
+ "deveploed\n",
+ "reexplained\n",
+ "fineS2P11\n",
+ "eggspanacakesbaconsausage\n",
+ "surrent\n",
+ "emmit\n",
+ "affeects\n",
+ "abought\n",
+ "Buisnessman\n",
+ "beforeand\n",
+ "progressreport\n",
+ "aventage\n",
+ "requiresparagraph\n",
+ "againwe\n",
+ "pesesion\n",
+ "falts\n",
+ "excavtion\n",
+ "ntural\n",
+ "emmisons\n",
+ "pressuare\n",
+ "licesnced\n",
+ "ofhow\n",
+ "projectsand\n",
+ "2005\n",
+ "77\n",
+ "berucratt\n",
+ "crouded\n",
+ "obviestly\n",
+ "Colleseum\n",
+ "lewts\n",
+ "centery\n",
+ "hereand\n",
+ "outofbuilding\n",
+ "online7\n",
+ "defintley\n",
+ "compations\n",
+ "monumeant\n",
+ "choosuing\n",
+ "contitution\n",
+ "myand\n",
+ "falacy\n",
+ "guarnteed\n",
+ "440000\n",
+ "defrentiate\n",
+ "conspiarcy\n",
+ "finaces\n",
+ "privatetherefore\n",
+ "enfoced\n",
+ "httpssciaga\n",
+ "jemp\n",
+ "Bogotathe\n",
+ "MIght\n",
+ "waisitng\n",
+ "Finallykids\n",
+ "canidacy\n",
+ "perfers\n",
+ "soandso\n",
+ "Califorlina\n",
+ "treeWere\n",
+ "mnot\n",
+ "prepaired\n",
+ "Californiatexaswyomingect\n",
+ "Finallty\n",
+ "imopact\n",
+ "oppoturnity\n",
+ "foundor\n",
+ "tuesdaythursday\n",
+ "historys\n",
+ "Commuication\n",
+ "daanger\n",
+ "posiable\n",
+ "prejuice\n",
+ "cartain\n",
+ "crusie\n",
+ "pleaseit\n",
+ "mucles\n",
+ "irtrational\n",
+ "reveue\n",
+ "rescing\n",
+ "enufe\n",
+ "optionsif\n",
+ "reasom\n",
+ "prieod\n",
+ "polluated\n",
+ "understandingnot\n",
+ "Twentyseven\n",
+ "fencinggames\n",
+ "birthpalce\n",
+ "moutheyes\n",
+ "prizewhereas\n",
+ "gasemissions\n",
+ "projectdesign\n",
+ "wayin\n",
+ "strongerbecause\n",
+ "butsmart\n",
+ "Cernainty\n",
+ "happyangry\n",
+ "agrithomes\n",
+ "phonetaking\n",
+ "Finallywe\n",
+ "marveld\n",
+ "Shortof\n",
+ "Rehababilitation\n",
+ "countung\n",
+ "lifeand\n",
+ "appresions\n",
+ "hepls\n",
+ "sevenforty\n",
+ "jogrunYou\n",
+ "permmitted\n",
+ "openingv\n",
+ "peronally\n",
+ "orgroadsafetysafetytopicsdistracteddrivingcellphonedistracteddriving\n",
+ "134in\n",
+ "kide\n",
+ "shodows\n",
+ "weaknessess\n",
+ "statted\n",
+ "3640\n",
+ "considerer\n",
+ "Indifensible\n",
+ "testworkprogetor\n",
+ "traviled\n",
+ "tomplaces\n",
+ "Mondays2p18\n",
+ "distanceNASA\n",
+ "cinfused\n",
+ "solv\n",
+ "Exsactly\n",
+ "hassels\n",
+ "Conlusion\n",
+ "catergorizing\n",
+ "classesWhere\n",
+ "caost\n",
+ "friendsassociates\n",
+ "telivision\n",
+ "percetnt\n",
+ "compliacted\n",
+ "speake\n",
+ "poler\n",
+ "totall\n",
+ "numorouds\n",
+ "Explorering\n",
+ "plung\n",
+ "fechers\n",
+ "bben\n",
+ "aspeed\n",
+ "unconstutional\n",
+ "couser\n",
+ "aprehensive\n",
+ "LastlyI\n",
+ "Electotal\n",
+ "pooible\n",
+ "multile\n",
+ "gufted\n",
+ "presentadditional\n",
+ "boredthen\n",
+ "Sourse\n",
+ "unrepputable\n",
+ "telecommunting\n",
+ "placeOn\n",
+ "facialexpression\n",
+ "porposterous\n",
+ "enertainment\n",
+ "muving\n",
+ "actiivation\n",
+ "Qithout\n",
+ "polited\n",
+ "shoouldnt\n",
+ "activite\n",
+ "Manily\n",
+ "Teengers\n",
+ "couldnet\n",
+ "tecnoligy\n",
+ "misenterpret\n",
+ "invaribly\n",
+ "immaging\n",
+ "comminuty\n",
+ "dnagerous\n",
+ "situatuons\n",
+ "drivingThat\n",
+ "beilefs\n",
+ "tracka\n",
+ "USAThere\n",
+ "sizehe\n",
+ "sothey\n",
+ "nobobody\n",
+ "telecommunities\n",
+ "time10\n",
+ "sperit\n",
+ "benifishal\n",
+ "nosiey\n",
+ "reconigize\n",
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+ "engineallowing\n",
+ "ttraffic\n",
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+ ]
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+ "infermations\n",
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+ "espacially\n",
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+ "computertype\n",
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+ "choreshomeworkand\n",
+ "390000injuries\n",
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+ "butyou\n",
+ "smuthering\n",
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+ "poppular\n",
+ "Ameica\n",
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+ "ridgid\n",
+ "bettere\n",
+ "dangerousstrange\n",
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+ "inplomentedit\n",
+ "outcomesRopeik3\n",
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+ "pickig\n",
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+ "aportunity\n",
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+ "themwe\n",
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+ "beacasue\n",
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+ "occuer\n",
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+ "carkit\n",
+ "435\n",
+ "steanthen\n",
+ "accidentsand\n",
+ "Bendz\n",
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+ "adviceand\n",
+ "teachersdesigns\n",
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+ "liquidfy\n",
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+ "enturupted\n",
+ "kayotic\n",
+ "911\n",
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+ "monety\n",
+ "texasand\n",
+ "celebritys\n",
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+ "thinkl\n",
+ "winmning\n",
+ "desperatley\n",
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+ "30\n",
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+ "trammua\n",
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+ "droke\n",
+ "condicones\n",
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+ "quickier\n",
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+ "deficult\n",
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+ "politicail\n",
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+ "collegethe\n",
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+ "onetouch\n",
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+ "104\n",
+ "onley\n",
+ "restruants\n",
+ "studyig\n",
+ "divice\n",
+ "proficiant\n",
+ "youwould\n",
+ "elsections\n",
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+ "concludtion\n",
+ "gretaer\n",
+ "otherÃÂs\n",
+ "correclty\n",
+ "doJoin\n",
+ "MarsNASA\n",
+ "ecidence\n",
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+ "senatorKeeping\n",
+ "persistsand\n",
+ "servince\n",
+ "Sincerially\n",
+ "moument\n",
+ "oothers\n",
+ "stranges\n",
+ "humongus\n",
+ "indivaully\n",
+ "andBMW\n",
+ "indecicive\n",
+ "musclesthat\n",
+ "Bandwagons\n",
+ "nuimber\n",
+ "Itoldyouso\n",
+ "tyes\n",
+ "MarlinsNasas\n",
+ "syetem\n",
+ "Oceanbut\n",
+ "funespecially\n",
+ "persuide\n",
+ "piredict\n",
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+ "praticality\n",
+ "transposrtation\n",
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+ "25years\n",
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+ "foris\n",
+ "hobies\n",
+ "incoclusion\n",
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+ "sabout\n",
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+ "purchace\n",
+ "itselve\n",
+ "aicd\n",
+ "merchury\n",
+ "communitcate\n",
+ "thibk\n",
+ "byou\n",
+ "Faithfullness\n",
+ "ethoes\n",
+ "Aerinautics\n",
+ "prereceived\n",
+ "dirrectly\n",
+ "esample\n",
+ "domany\n",
+ "mid90s\n",
+ "tactict\n",
+ "permenently\n",
+ "studystudy\n",
+ "decussed\n",
+ "aserations\n",
+ "WHOOOSH\n",
+ "awareyou\n",
+ "casst\n",
+ "analougous\n",
+ "selfmotivated\n",
+ "butIf\n",
+ "exspression\n",
+ "garentee\n",
+ "Noitifcations\n",
+ "dangoroue\n",
+ "landfom\n",
+ "dreiverless\n",
+ "thnings\n",
+ "surrondeed\n",
+ "usinf\n",
+ "characeristics\n",
+ "Admendment\n",
+ "felloe\n",
+ "Veiw\n",
+ "considerinf\n",
+ "threir\n",
+ "1997Twenty\n",
+ "irresponsable\n",
+ "joind\n",
+ "eatures\n",
+ "Elecetoral\n",
+ "optionopinion\n",
+ "p10\n",
+ "noticepresidents\n",
+ "incarentertainment\n",
+ "oppisite\n",
+ "nowin\n",
+ "baseballl\n",
+ "votegot\n",
+ "Represenstatives\n",
+ "studnet\n",
+ "atomoshere\n",
+ "loudoun\n",
+ "meit\n",
+ "qualificatied\n",
+ "aposed\n",
+ "manufactorers\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "favoritem\n",
+ "1888\n",
+ "votesnow\n",
+ "remendous\n",
+ "streetsand\n",
+ "casses\n",
+ "ÃÂfalse\n",
+ "coputer\n",
+ "elecction\n",
+ "pregent\n",
+ "worldside\n",
+ "Stuadents\n",
+ "ambicouse\n",
+ "presidentia\n",
+ "submerne\n",
+ "emotionaly\n",
+ "55MPH\n",
+ "sickhandicapped\n",
+ "responsiblilty\n",
+ "threebecause\n",
+ "interracting\n",
+ "posibaly\n",
+ "tellls\n",
+ "diffference\n",
+ "morningt\n",
+ "invain\n",
+ "statepartys\n",
+ "minutessorry\n",
+ "discouaragement\n",
+ "evalutate\n",
+ "himWhy\n",
+ "classesand\n",
+ "argung\n",
+ "irrationalits\n",
+ "geneuine\n",
+ "Unquestibily\n",
+ "uneveiled\n",
+ "challeange\n",
+ "acomidation\n",
+ "vews\n",
+ "saidWell\n",
+ "shouldor\n",
+ "knowldege\n",
+ "articlewhy\n",
+ "unquine\n",
+ "bbecause\n",
+ "Drivesafe\n",
+ "Chicfila\n",
+ "daysweeksmounthsor\n",
+ "excpecially\n",
+ "ifsands\n",
+ "PLain\n",
+ "Studentdesigned\n",
+ "imrovement\n",
+ "AFLCIOthanks\n",
+ "communitie\n",
+ "pargargraph\n",
+ "handwrittting\n",
+ "laditue\n",
+ "argumental\n",
+ "purpuse\n",
+ "eveyrthing\n",
+ "soccerfootballmedia\n",
+ "factial\n",
+ "outoftouch\n",
+ "globla\n",
+ "accumilated\n",
+ "plesant\n",
+ "squarles\n",
+ "greately\n",
+ "benith\n",
+ "beiieve\n",
+ "sepreate\n",
+ "commercialand\n",
+ "electionparagraph\n",
+ "perfectlyyou\n",
+ "wirhh\n",
+ "dislikesummer\n",
+ "RomneyBecause\n",
+ "technologhy\n",
+ "eflaws\n",
+ "1car\n",
+ "Scientis\n",
+ "belife\n",
+ "upond\n",
+ "bringbut\n",
+ "ogf\n",
+ "dicisons\n",
+ "citizend\n",
+ "effecent\n",
+ "sennsors\n",
+ "sayingdoing\n",
+ "anstronount\n",
+ "thikn\n",
+ "conclusionschools\n",
+ "mealts\n",
+ "neagtives\n",
+ "photographgs\n",
+ "deflict\n",
+ "supposively\n",
+ "alble\n",
+ "gameboys\n",
+ "facsal\n",
+ "proess\n",
+ "resricted\n",
+ "learnig\n",
+ "tookwatched\n",
+ "reconmend\n",
+ "questionsadvice\n",
+ "proyect\n",
+ "votes538\n",
+ "funwhen\n",
+ "comensate\n",
+ "genertion\n",
+ "Reasonings\n",
+ "exlpore\n",
+ "ecumaliated\n",
+ "Caliornia\n",
+ "knowign\n",
+ "incoredge\n",
+ "resuers\n",
+ "manufacturerWaiting\n",
+ "Doordash\n",
+ "nonliberal\n",
+ "bettrayedbut\n",
+ "accorinding\n",
+ "geosystems\n",
+ "Althoughyour\n",
+ "playedan\n",
+ "smogSelsky\n",
+ "exhuast\n",
+ "artifitial\n",
+ "tbis\n",
+ "chalnging\n",
+ "Floridashown\n",
+ "leislatures\n",
+ "intereesting\n",
+ "notsodemoctratic\n",
+ "initiatereplyor\n",
+ "danguorse\n",
+ "inisint\n",
+ "curantly\n",
+ "assitence\n",
+ "Egyptin\n",
+ "tansporation\n",
+ "soultion\n",
+ "privelidge\n",
+ "electol\n",
+ "Firse\n",
+ "delima\n",
+ "eartth\n",
+ "descrimate\n",
+ "dumby\n",
+ "uncomforatble\n",
+ "assticance\n",
+ "presidentmany\n",
+ "tourtamentsfencingboxingwe\n",
+ "summarizeif\n",
+ "Carsshould\n",
+ "descreptions\n",
+ "depemdent\n",
+ "invole\n",
+ "ellctoral\n",
+ "rven\n",
+ "sporst\n",
+ "processnot\n",
+ "WeThe\n",
+ "naturalsaid\n",
+ "unessacery\n",
+ "drierless\n",
+ "sudying\n",
+ "adoults\n",
+ "thatby\n",
+ "onstar\n",
+ "surfuce\n",
+ "illfounded\n",
+ "matoaca\n",
+ "Refuring\n",
+ "Mindplay\n",
+ "aliencreated\n",
+ "uesd\n",
+ "PROPER_NAMEs\n",
+ "showdow\n",
+ "rummers\n",
+ "beneficicial\n",
+ "saidabolish\n",
+ "norht\n",
+ "Elcectoral\n",
+ "acticity\n",
+ "hafe\n",
+ "comprimse\n",
+ "stuents\n",
+ "civilation\n",
+ "outcomeTwo\n",
+ "ocenas\n",
+ "favour\n",
+ "fineparagraph\n",
+ "statesWhen\n",
+ "promonate\n",
+ "vintg\n",
+ "undoubtely\n",
+ "desiel\n",
+ "vehcicles\n",
+ "theiries\n",
+ "Clombia\n",
+ "ddegres\n",
+ "that1\n",
+ "alians\n",
+ "comarable\n",
+ "moviestv\n",
+ "assignmentsmultiple\n",
+ "understrand\n",
+ "trueselves\n",
+ "dumbies\n",
+ "prsons\n",
+ "annoyoing\n",
+ "chiildren\n",
+ "manfucaturing\n",
+ "citazen\n",
+ "idale\n",
+ "Condress\n",
+ "exteremely\n",
+ "drivleress\n",
+ "viewIt\n",
+ "vitue\n",
+ "Thirdi\n",
+ "disavantages\n",
+ "P10\n",
+ "studieng\n",
+ "coloubia\n",
+ "provented\n",
+ "vialotors\n",
+ "extravegant\n",
+ "compains\n",
+ "JTrump\n",
+ "therioes\n",
+ "distranted\n",
+ "halpful\n",
+ "youyou\n",
+ "doesntt\n",
+ "shoundnt\n",
+ "outguned\n",
+ "transportaton\n",
+ "elctoal\n",
+ "workzones\n",
+ "chooseso\n",
+ "Farenheitand\n",
+ "representaives\n",
+ "Venesuala\n",
+ "600PM\n",
+ "midC\n",
+ "mentoined\n",
+ "byIt\n",
+ "stochiometry\n",
+ "aboutteencrashes\n",
+ "campaigm\n",
+ "clubsprograms\n",
+ "ginourmous\n",
+ "starvingasked\n",
+ "4pedestrianbicycleand\n",
+ "academicaly\n",
+ "Stystem\n",
+ "contriversial\n",
+ "anachronism15\n",
+ "questionstudent\n",
+ "motoredvechile\n",
+ "canidets\n",
+ "electiong\n",
+ "grademake\n",
+ "wakework\n",
+ "disgustedand\n",
+ "thearpy\n",
+ "saysIf\n",
+ "humanss\n",
+ "anways\n",
+ "successivly\n",
+ "scientests\n",
+ "okanet\n",
+ "contibutes\n",
+ "kmwide\n",
+ "capitals17\n",
+ "mebut\n",
+ "Driveress\n",
+ "thatand\n",
+ "cars57percent\n",
+ "csre\n",
+ "rageuos\n",
+ "Anthother\n",
+ "dangersous\n",
+ "easylyknowing\n",
+ "usig\n",
+ "revenuepublic\n",
+ "papre\n",
+ "praticle\n",
+ "lunchan\n",
+ "5Avoid\n",
+ "importanty\n",
+ "disadantages\n",
+ "familyself\n",
+ "finesfeesgas\n",
+ "bullyied\n",
+ "Everythng\n",
+ "drivingi\n",
+ "alduts\n",
+ "unfairirrational\n",
+ "propriate\n",
+ "416000\n",
+ "sciantists\n",
+ "Represetatives\n",
+ "para2\n",
+ "parlementary\n",
+ "evena\n",
+ "anicent\n",
+ "unforutnetly\n",
+ "pollutionits\n",
+ "accidentyly\n",
+ "questionsyou\n",
+ "colled\n",
+ "uprade\n",
+ "ideasopinions\n",
+ "Indefensive\n",
+ "differentlywhich\n",
+ "deabate\n",
+ "lifeskill\n",
+ "WWIthanks\n",
+ "GreecePanamaChina\n",
+ "devolope\n",
+ "triy\n",
+ "alterenative\n",
+ "injuredhurt\n",
+ "expreess\n",
+ "holdes\n",
+ "injuredamage\n",
+ "asepcts\n",
+ "likeme\n",
+ "resoltuion\n",
+ "scince\n",
+ "Excercising\n",
+ "endles\n",
+ "conclousion\n",
+ "honeslty\n",
+ "youir\n",
+ "projectsFor\n",
+ "imbarrasing\n",
+ "empolyees\n",
+ "spacce\n",
+ "exstenive\n",
+ "explord\n",
+ "relason\n",
+ "bown\n",
+ "understandimg\n",
+ "thenbut\n",
+ "morst\n",
+ "techonologies\n",
+ "IBMYP\n",
+ "knowledgenot\n",
+ "presentationlearn\n",
+ "Itss\n",
+ "paintning\n",
+ "lazyiness\n",
+ "rethorical\n",
+ "discustfear\n",
+ "conficence\n",
+ "buolt\n",
+ "deseves\n",
+ "volleyballtabletennis\n",
+ "drugsalcohol\n"
+ ]
+ }
+ ],
+ "source": [
+ "for word in vocab:\n",
+ " if word not in embedding_map:\n",
+ " print(word)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "bd1a3ed0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def argmax(vec):\n",
+ " # return the argmax as a python int\n",
+ " _, idx = torch.max(vec, 1)\n",
+ " return idx.item()\n",
+ "\n",
+ "def prepare_seq_word2vec(sentence, embedding_map):\n",
+ " return torch.tensor([embedding_map[token] for token in sentence], dtype=torch.long)\n",
+ "\n",
+ "def prepare_sequence(seq, to_ix):\n",
+ " idxs = [to_ix[w] for w in seq]\n",
+ " return torch.tensor(idxs, dtype=torch.long)\n",
+ "\n",
+ "\n",
+ "# Compute log sum exp in a numerically stable way for the forward algorithm\n",
+ "def log_sum_exp(vec):\n",
+ " max_score = vec[0, argmax(vec)]\n",
+ " max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])\n",
+ " return max_score + \\\n",
+ " torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))\n",
+ "\n",
+ "class BiLSTM_CRF(nn.Module):\n",
+ "\n",
+ " def __init__(self, tag_to_ix, embedding_dim, hidden_dim, vocab_size=300000):\n",
+ " # what is tag_to_ix? embedding_dim OK, hidden_dim what to set?\n",
+ " \n",
+ " super(BiLSTM_CRF, self).__init__()\n",
+ " self.embedding_dim = embedding_dim\n",
+ " self.hidden_dim = hidden_dim\n",
+ " self.vocab_size = vocab_size\n",
+ " self.tag_to_ix = tag_to_ix\n",
+ " self.tagset_size = len(tag_to_ix)\n",
+ "\n",
+ " self.word_embeds = nn.Embedding(vocab_size, embedding_dim)\n",
+ " self.word_embeds.weight = nn.Parameter(torch.from_numpy(gensim_model.vectors).float())\n",
+ " # add a load from state dict\n",
+ " \n",
+ " self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,\n",
+ " num_layers=1, bidirectional=True)\n",
+ "\n",
+ " # Maps the output of the LSTM into tag space.\n",
+ " self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)\n",
+ "\n",
+ " # Matrix of transition parameters. Entry i,j is the score of\n",
+ " # transitioning *to* i *from* j.\n",
+ " self.transitions = nn.Parameter(\n",
+ " torch.randn(self.tagset_size, self.tagset_size))\n",
+ "\n",
+ " # These two statements enforce the constraint that we never transfer\n",
+ " # to the start tag and we never transfer from the stop tag\n",
+ " self.transitions.data[tag_to_ix[START_TAG], :] = -10000\n",
+ " self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000\n",
+ "\n",
+ " self.hidden = self.init_hidden()\n",
+ "\n",
+ " def init_hidden(self):\n",
+ " return (torch.randn(2, 1, self.hidden_dim // 2),\n",
+ " torch.randn(2, 1, self.hidden_dim // 2))\n",
+ "\n",
+ " def _forward_alg(self, feats):\n",
+ " # Do the forward algorithm to compute the partition function\n",
+ " init_alphas = torch.full((1, self.tagset_size), -10000.)\n",
+ " # START_TAG has all of the score.\n",
+ " init_alphas[0][self.tag_to_ix[START_TAG]] = 0.\n",
+ "\n",
+ " # Wrap in a variable so that we will get automatic backprop\n",
+ " forward_var = init_alphas\n",
+ "\n",
+ " # Iterate through the sentence\n",
+ " for feat in feats:\n",
+ " print(feat)\n",
+ " break\n",
+ " alphas_t = [] # The forward tensors at this timestep\n",
+ " for next_tag in range(self.tagset_size):\n",
+ " # broadcast the emission score: it is the same regardless of\n",
+ " # the previous tag\n",
+ " emit_score = feat[next_tag].view(\n",
+ " 1, -1).expand(1, self.tagset_size)\n",
+ " # the ith entry of trans_score is the score of transitioning to\n",
+ " # next_tag from i\n",
+ " trans_score = self.transitions[next_tag].view(1, -1)\n",
+ " # The ith entry of next_tag_var is the value for the\n",
+ " # edge (i -> next_tag) before we do log-sum-exp\n",
+ " next_tag_var = forward_var + trans_score + emit_score\n",
+ " # The forward variable for this tag is log-sum-exp of all the\n",
+ " # scores.\n",
+ " alphas_t.append(log_sum_exp(next_tag_var).view(1))\n",
+ " forward_var = torch.cat(alphas_t).view(1, -1)\n",
+ " terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]\n",
+ " alpha = log_sum_exp(terminal_var)\n",
+ " return alpha\n",
+ "\n",
+ " def _get_lstm_features(self, sentence):\n",
+ " self.hidden = self.init_hidden()\n",
+ " embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)\n",
+ " lstm_out, self.hidden = self.lstm(embeds, self.hidden)\n",
+ " lstm_out = lstm_out.view(len(sentence), self.hidden_dim)\n",
+ " lstm_feats = self.hidden2tag(lstm_out)\n",
+ " return lstm_feats\n",
+ "\n",
+ " def _score_sentence(self, feats, tags):\n",
+ " # Gives the score of a provided tag sequence\n",
+ " score = torch.zeros(1)\n",
+ " tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])\n",
+ " for i, feat in enumerate(feats):\n",
+ " score = score + \\\n",
+ " self.transitions[tags[i + 1], tags[i]] + feat[tags[i + 1]]\n",
+ " score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]\n",
+ " return score\n",
+ "\n",
+ " def _viterbi_decode(self, feats):\n",
+ " backpointers = []\n",
+ "\n",
+ " # Initialize the viterbi variables in log space\n",
+ " init_vvars = torch.full((1, self.tagset_size), -10000.)\n",
+ " init_vvars[0][self.tag_to_ix[START_TAG]] = 0\n",
+ "\n",
+ " # forward_var at step i holds the viterbi variables for step i-1\n",
+ " forward_var = init_vvars\n",
+ " for feat in feats:\n",
+ " bptrs_t = [] # holds the backpointers for this step\n",
+ " viterbivars_t = [] # holds the viterbi variables for this step\n",
+ "\n",
+ " for next_tag in range(self.tagset_size):\n",
+ " # next_tag_var[i] holds the viterbi variable for tag i at the\n",
+ " # previous step, plus the score of transitioning\n",
+ " # from tag i to next_tag.\n",
+ " # We don't include the emission scores here because the max\n",
+ " # does not depend on them (we add them in below)\n",
+ " next_tag_var = forward_var + self.transitions[next_tag]\n",
+ " best_tag_id = argmax(next_tag_var)\n",
+ " bptrs_t.append(best_tag_id)\n",
+ " viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))\n",
+ " # Now add in the emission scores, and assign forward_var to the set\n",
+ " # of viterbi variables we just computed\n",
+ " forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)\n",
+ " backpointers.append(bptrs_t)\n",
+ "\n",
+ " # Transition to STOP_TAG\n",
+ " terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]\n",
+ " best_tag_id = argmax(terminal_var)\n",
+ " path_score = terminal_var[0][best_tag_id]\n",
+ "\n",
+ " # Follow the back pointers to decode the best path.\n",
+ " best_path = [best_tag_id]\n",
+ " for bptrs_t in reversed(backpointers):\n",
+ " best_tag_id = bptrs_t[best_tag_id]\n",
+ " best_path.append(best_tag_id)\n",
+ " # Pop off the start tag (we dont want to return that to the caller)\n",
+ " start = best_path.pop()\n",
+ " assert start == self.tag_to_ix[START_TAG] # Sanity check\n",
+ " best_path.reverse()\n",
+ " return path_score, best_path\n",
+ "\n",
+ " def neg_log_likelihood(self, sentence, tags):\n",
+ " feats = self._get_lstm_features(sentence)\n",
+ " forward_score = self._forward_alg(feats)\n",
+ " gold_score = self._score_sentence(feats, tags)\n",
+ " return forward_score - gold_score\n",
+ "\n",
+ " def forward(self, sentence): # dont confuse this with _forward_alg above.\n",
+ " # Get the emission scores from the BiLSTM\n",
+ " lstm_feats = self._get_lstm_features(sentence)\n",
+ "\n",
+ " # Find the best path, given the features.\n",
+ " score, tag_seq = self._viterbi_decode(lstm_feats)\n",
+ " return score, tag_seq"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "90f0175b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "START_TAG = \"\"\n",
+ "STOP_TAG = \"\"\n",
+ "EMBEDDING_DIM = 300 # TODO need to set this to be the same as the dimension of the vectors you use\n",
+ "HIDDEN_DIM = 4\n",
+ "\n",
+ "# Make up some training data\n",
+ "training_data = [(\n",
+ " \"the wall street journal reported today that apple corporation made money\".split(),\n",
+ " \"B I I I O O O B I O O\".split()\n",
+ "), (\n",
+ " \"georgia tech is a university in georgia\".split(),\n",
+ " \"B I O O O O B\".split()\n",
+ ")]\n",
+ "word_to_ix = {}\n",
+ "for sentence, tags in training_data:\n",
+ " for word in sentence:\n",
+ " if word not in word_to_ix:\n",
+ " word_to_ix[word] = len(word_to_ix)\n",
+ "\n",
+ "tag_to_ix = {\"B\": 0, \"I\": 1, \"O\": 2, START_TAG: 3, STOP_TAG: 4}\n",
+ "\n",
+ "\n",
+ "model = BiLSTM_CRF(tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM, len(word_to_ix))\n",
+ "optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "f6a0c026",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([ 11, 2334, 1482, 8977, 343, 205, 3, 13467, 6207, 107,\n",
+ " 237])\n",
+ "tensor([-0.0513, -0.1467, 0.3591, -0.1300, -0.0219],\n",
+ " grad_fn=)\n"
+ ]
+ },
+ {
+ "ename": "KeyError",
+ "evalue": "'a'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [31]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# Step 2. Get our inputs ready for the network, that is,\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# turn them into Tensors of word indices.\u001b[39;00m\n\u001b[1;32m 11\u001b[0m sentence_in \u001b[38;5;241m=\u001b[39m prepare_sequence(sentence, word_to_ix)\n\u001b[0;32m---> 12\u001b[0m sentence_in \u001b[38;5;241m=\u001b[39m \u001b[43mprepare_seq_word2vec\u001b[49m\u001b[43m(\u001b[49m\u001b[43msentence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membedding_map\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28mprint\u001b[39m(sentence_in)\n\u001b[1;32m 14\u001b[0m targets \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([tag_to_ix[t] \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tags], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong)\n",
+ "Input \u001b[0;32mIn [29]\u001b[0m, in \u001b[0;36mprepare_seq_word2vec\u001b[0;34m(sentence, embedding_map)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprepare_seq_word2vec\u001b[39m(sentence, embedding_map):\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mtensor([embedding_map[token] \u001b[38;5;28;01mfor\u001b[39;00m token \u001b[38;5;129;01min\u001b[39;00m sentence], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong)\n",
+ "Input \u001b[0;32mIn [29]\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprepare_seq_word2vec\u001b[39m(sentence, embedding_map):\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[43membedding_map\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m token \u001b[38;5;129;01min\u001b[39;00m sentence], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong)\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'a'"
+ ]
+ }
+ ],
+ "source": [
+ "for epoch in range(\n",
+ " 300): # again, normally you would NOT do 300 epochs, it is toy data\n",
+ " for sentence, tags in training_data:\n",
+ "\n",
+ " # Step 1. Remember that Pytorch accumulates gradients.\n",
+ " # We need to clear them out before each instance\n",
+ " model.zero_grad()\n",
+ "\n",
+ " # Step 2. Get our inputs ready for the network, that is,\n",
+ " # turn them into Tensors of word indices.\n",
+ " sentence_in = prepare_sequence(sentence, word_to_ix)\n",
+ " sentence_in = prepare_seq_word2vec(sentence, embedding_map)\n",
+ " print(sentence_in)\n",
+ " targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)\n",
+ "\n",
+ " # Step 3. Run our forward pass.\n",
+ " loss = model.neg_log_likelihood(sentence_in, targets)\n",
+ "\n",
+ " # Step 4. Compute the loss, gradients, and update the parameters by\n",
+ " # calling optimizer.step()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "192e85cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from gensim.test.utils import common_texts\n",
+ "from gensim.models import Word2Vec"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "799bf276",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: 'path/to/file'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgensim\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mgensim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mKeyedVectors\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_word2vec_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpath/to/file\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m weights \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mFloatTensor(model\u001b[38;5;241m.\u001b[39mvectors)\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/gensim/models/keyedvectors.py:1629\u001b[0m, in \u001b[0;36mKeyedVectors.load_word2vec_format\u001b[0;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype, no_header)\u001b[0m\n\u001b[1;32m 1582\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 1583\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_word2vec_format\u001b[39m(\n\u001b[1;32m 1584\u001b[0m \u001b[38;5;28mcls\u001b[39m, fname, fvocab\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, binary\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf8\u001b[39m\u001b[38;5;124m'\u001b[39m, unicode_errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 1585\u001b[0m limit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, datatype\u001b[38;5;241m=\u001b[39mREAL, no_header\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 1586\u001b[0m ):\n\u001b[1;32m 1587\u001b[0m \u001b[38;5;124;03m\"\"\"Load KeyedVectors from a file produced by the original C word2vec-tool format.\u001b[39;00m\n\u001b[1;32m 1588\u001b[0m \n\u001b[1;32m 1589\u001b[0m \u001b[38;5;124;03m Warnings\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1627\u001b[0m \n\u001b[1;32m 1628\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1629\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load_word2vec_format\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1630\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfvocab\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfvocab\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbinary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbinary\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43municode_errors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43municode_errors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1631\u001b[0m \u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatatype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdatatype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mno_header\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mno_header\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1632\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/gensim/models/keyedvectors.py:1955\u001b[0m, in \u001b[0;36m_load_word2vec_format\u001b[0;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype, no_header, binary_chunk_size)\u001b[0m\n\u001b[1;32m 1952\u001b[0m counts[word] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(count)\n\u001b[1;32m 1954\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloading projection weights from \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, fname)\n\u001b[0;32m-> 1955\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m fin:\n\u001b[1;32m 1956\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m no_header:\n\u001b[1;32m 1957\u001b[0m \u001b[38;5;66;03m# deduce both vocab_size & vector_size from 1st pass over file\u001b[39;00m\n\u001b[1;32m 1958\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m binary:\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/smart_open/smart_open_lib.py:188\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(uri, mode, buffering, encoding, errors, newline, closefd, opener, ignore_ext, compression, transport_params)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m transport_params \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 186\u001b[0m transport_params \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 188\u001b[0m fobj \u001b[38;5;241m=\u001b[39m \u001b[43m_shortcut_open\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 189\u001b[0m \u001b[43m \u001b[49m\u001b[43muri\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 191\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 192\u001b[0m \u001b[43m \u001b[49m\u001b[43mbuffering\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbuffering\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 193\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 194\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 195\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 196\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fobj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fobj\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/smart_open/smart_open_lib.py:361\u001b[0m, in \u001b[0;36m_shortcut_open\u001b[0;34m(uri, mode, compression, buffering, encoding, errors, newline)\u001b[0m\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 359\u001b[0m open_kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124merrors\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m errors\n\u001b[0;32m--> 361\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_builtin_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffering\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbuffering\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mopen_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'path/to/file'"
+ ]
+ }
+ ],
+ "source": [
+ "def generate_embedding_matrix():\n",
+ " # should generate an embedding matrix that is the size of the vocab each time...\n",
+ " # we need to save this lol\n",
+ " pass\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a32cc072",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "type object 'Word2Vec' has no attribute 'vectors'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mWord2Vec\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvectors\u001b[49m\n",
+ "\u001b[0;31mAttributeError\u001b[0m: type object 'Word2Vec' has no attribute 'vectors'"
+ ]
+ }
+ ],
+ "source": [
+ "Word2Vec.vectors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "b4fd336d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_2992/1384161003.py:12: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+ " df.text = df.text.str.replace('[^\\w\\s]','')\n"
+ ]
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "d1a6d9e1",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "3ac61f95",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_word_ids(tokens, embedding_map, reduce_map):\n",
+ " embedding_token_ids = [\n",
+ " embedding_map[token] for token in tokens\n",
+ " ]\n",
+ " token_ids = [\n",
+ " reduce_map[embedding_token_id] for embedding_token_id in embedding_token_ids\n",
+ " ]\n",
+ " return token_ids"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6677bc9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "get_word_ids(['Hi my name is Yousef'], key_to_index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "82377963",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "key_to_index = model.key_to_index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "e86870d6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_2992/2150507369.py:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+ " df.text = df.text.str.replace('[^\\w\\s]','')\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'text_split' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m df\u001b[38;5;241m.\u001b[39mtext \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mtext\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m[^\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms]\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 5\u001b[0m vocab \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(df\u001b[38;5;241m.\u001b[39mtext\u001b[38;5;241m.\u001b[39mvalues)\u001b[38;5;241m.\u001b[39msplit())\n\u001b[0;32m----> 7\u001b[0m ids \u001b[38;5;241m=\u001b[39m [key_to_index[word] \u001b[38;5;28;01mfor\u001b[39;00m word \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mset\u001b[39m(\u001b[43mtext_split\u001b[49m)]\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'text_split' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "path = '../../data/bio/Persuade_postprocessed.json'\n",
+ "import pandas as pd\n",
+ "df = pd.read_json(path)\n",
+ "df.text = df.text.str.replace('[^\\w\\s]','')\n",
+ "vocab = set(' '.join(df.text.values).split())\n",
+ "\n",
+ "ids = [key_to_index[word] for word in set(text_split)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4f9e5043",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(ids)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "id": "2df0f6bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from gensim.models import FastText\n",
+ "\n",
+ "common_texts = ['Hi my name is Yousef.'.split()]\n",
+ "from gensim.test.utils import common_texts\n",
+ "model2 = FastText(vector_size=4, window=3, min_count=1, sentences=common_texts, epochs=10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "670bf244",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'vector_size': 4,\n",
+ " 'index_to_key': ['system',\n",
+ " 'graph',\n",
+ " 'trees',\n",
+ " 'user',\n",
+ " 'minors',\n",
+ " 'eps',\n",
+ " 'time',\n",
+ " 'response',\n",
+ " 'survey',\n",
+ " 'computer',\n",
+ " 'interface',\n",
+ " 'human'],\n",
+ " 'next_index': 0,\n",
+ " 'key_to_index': {'system': 0,\n",
+ " 'graph': 1,\n",
+ " 'trees': 2,\n",
+ " 'user': 3,\n",
+ " 'minors': 4,\n",
+ " 'eps': 5,\n",
+ " 'time': 6,\n",
+ " 'response': 7,\n",
+ " 'survey': 8,\n",
+ " 'computer': 9,\n",
+ " 'interface': 10,\n",
+ " 'human': 11},\n",
+ " 'vectors': array([[-0.0277314 , -0.01877754, 0.0140359 , 0.00355452],\n",
+ " [ 0.00626689, -0.04813672, 0.05594172, 0.07846048],\n",
+ " [-0.08235919, 0.01859071, 0.01354989, -0.02779197],\n",
+ " [-0.06887284, 0.00654027, 0.00479901, -0.0286346 ],\n",
+ " [ 0.07342507, -0.0066566 , -0.02673712, -0.00220155],\n",
+ " [ 0.02733365, -0.06495033, 0.01261815, -0.00852403],\n",
+ " [ 0.07512262, -0.0081279 , -0.00192467, -0.02304782],\n",
+ " [-0.04446438, 0.05171199, 0.00654341, 0.00534294],\n",
+ " [ 0.01452866, 0.02735674, 0.03940941, -0.02064527],\n",
+ " [-0.0026324 , -0.03817194, -0.02007434, 0.00741352],\n",
+ " [ 0.0113926 , -0.00874304, -0.03574701, 0.00472042],\n",
+ " [ 0.02830744, 0.01806019, -0.03648039, 0.02306542]],\n",
+ " dtype=float32),\n",
+ " 'norms': None,\n",
+ " 'expandos': {'count': array([4, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
+ " 'sample_int': array([396841800, 463795800, 463795800, 463795800, 579459574, 579459574,\n",
+ " 579459574, 579459574, 579459574, 579459574, 579459574, 579459574],\n",
+ " dtype=uint32)},\n",
+ " 'mapfile_path': None,\n",
+ " 'min_n': 3,\n",
+ " 'max_n': 6,\n",
+ " 'bucket': 2000000,\n",
+ " 'buckets_word': [array([ 577843, 796992, 410716, 298171, 1365014, 1338566, 1502105,\n",
+ " 705564, 1815523, 467698, 350029, 223369, 750181, 1020056,\n",
+ " 688338, 1354765, 1500233, 1459435], dtype=uint32),\n",
+ " array([ 606866, 1714185, 337403, 365161, 1700295, 1257365, 1650887,\n",
+ " 920315, 1567620, 1780484, 215758, 1736948, 1848702, 128481],\n",
+ " dtype=uint32),\n",
+ " array([ 485437, 1197320, 561431, 487916, 74386, 1839573, 1004626,\n",
+ " 56836, 543215, 1012756, 122014, 965700, 1461902, 445173],\n",
+ " dtype=uint32),\n",
+ " array([1339771, 876474, 1735640, 1857042, 1170932, 501362, 1556644,\n",
+ " 1112181, 1981201, 742840], dtype=uint32),\n",
+ " array([1512081, 1193581, 703654, 1662652, 1609815, 1634216, 1361070,\n",
+ " 1441895, 550755, 211779, 208528, 1057386, 840088, 1582449,\n",
+ " 125789, 760149, 86385, 725444], dtype=uint32),\n",
+ " array([ 677246, 723575, 567275, 127225, 1150149, 1971742], dtype=uint32),\n",
+ " array([ 373532, 1815011, 1934322, 1659044, 1943145, 253156, 1258414,\n",
+ " 1296042, 773628, 255579], dtype=uint32),\n",
+ " array([ 724938, 107323, 254993, 1534554, 546073, 1160139, 371628,\n",
+ " 493158, 232795, 1200732, 229942, 1033887, 1608155, 259055,\n",
+ " 737172, 1945059, 766122, 509979, 940250, 890988, 1127577,\n",
+ " 1978420, 666558, 868281, 1860869, 455041], dtype=uint32),\n",
+ " array([1909271, 1767039, 334507, 872138, 1672037, 579305, 1299940,\n",
+ " 517799, 917648, 181423, 1567714, 1996372, 866548, 1983607,\n",
+ " 1468395, 1653895, 1388859, 1430911], dtype=uint32),\n",
+ " array([1361509, 7960, 635832, 821303, 603614, 900202, 690637,\n",
+ " 1739323, 1336315, 274442, 1323738, 1263533, 512791, 1076185,\n",
+ " 885364, 525682, 1118190, 52561, 979225, 1230821, 434219,\n",
+ " 26267, 400575, 1248576, 404442, 742840], dtype=uint32),\n",
+ " array([ 481960, 13140, 1330851, 215299, 1107422, 1850849, 87529,\n",
+ " 1512477, 738446, 1537972, 128278, 462037, 1248576, 1973970,\n",
+ " 1631561, 1420574, 1196176, 137187, 1425664, 1037439, 1535288,\n",
+ " 332865, 1899436, 367830, 711889, 255708, 1619270, 1506472,\n",
+ " 1273890, 1514545], dtype=uint32),\n",
+ " array([1630484, 453115, 1790062, 1061888, 1914993, 755376, 915322,\n",
+ " 1525004, 303314, 312436, 1178494, 1773551, 1718531, 1270280],\n",
+ " dtype=uint32)],\n",
+ " 'vectors_vocab': array([[-0.01340568, 0.00591078, 0.12758374, 0.22523183],\n",
+ " [-0.23257375, -0.17792022, 0.16147181, 0.2243247 ],\n",
+ " [-0.1253857 , -0.09408429, 0.18451262, -0.03833678],\n",
+ " [-0.11341632, 0.16383694, -0.12151156, -0.04539765],\n",
+ " [ 0.07193237, 0.02480325, -0.20713237, -0.23621911],\n",
+ " [ 0.18279415, 0.12675655, 0.16894233, 0.01907164],\n",
+ " [ 0.15877226, -0.08513415, -0.02366003, 0.14421433],\n",
+ " [-0.18804094, -0.09840259, -0.18778956, -0.02325106],\n",
+ " [ 0.23846525, -0.18297876, -0.05834286, -0.04843989],\n",
+ " [ 0.20193493, -0.1482866 , 0.00112159, -0.11884059],\n",
+ " [-0.24008876, 0.12518233, -0.21898964, -0.10979563],\n",
+ " [-0.0008775 , -0.00740454, -0.191531 , 0.24036857]],\n",
+ " dtype=float32),\n",
+ " 'vectors_ngrams': array([[ 0.16878772, -0.11919394, -0.19534728, -0.10075444],\n",
+ " [-0.04309317, 0.15711287, -0.02436498, -0.20404205],\n",
+ " [-0.08255851, 0.05005026, 0.15659463, 0.11428025],\n",
+ " ...,\n",
+ " [ 0.1943017 , -0.1197423 , 0.08607841, 0.12445804],\n",
+ " [ 0.09043515, -0.04263142, -0.22345108, -0.12466013],\n",
+ " [-0.19872016, -0.22180456, 0.10834444, 0.09445775]],\n",
+ " dtype=float32),\n",
+ " 'compatible_hash': True,\n",
+ " 'vectors_vocab_lockf': array([1.], dtype=float32),\n",
+ " 'vectors_ngrams_lockf': array([1.], dtype=float32),\n",
+ " 'vectors_lockf': array([1.], dtype=float32)}"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model2.wv.__dict__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "00ee00da",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_41137/383823899.py:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
+ " df.text = df.text.str.replace('[^\\w\\s]','')\n"
+ ]
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "078a3ccd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "68705\n"
+ ]
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "650628a2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "34256\n"
+ ]
+ }
+ ],
+ "source": [
+ "ids = []\n",
+ "for word in set(text_split):\n",
+ " if word in key_to_index:\n",
+ " ids.append(key_to_index[word])\n",
+ "print(len(ids))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b128f37c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/DataProcessor_dev.ipynb b/experiments/yousef/DataProcessor_dev.ipynb
new file mode 100644
index 0000000..802f7b5
--- /dev/null
+++ b/experiments/yousef/DataProcessor_dev.ipynb
@@ -0,0 +1,2495 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "ee4b6bb3",
+ "metadata": {},
+ "source": [
+ "# Notebook to dev the DataProcessors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "12822b29",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Google Drive import failed... continuing anyways...\n",
+ "CUDA device NOT detected. Using CPU...\n",
+ "Import of ArgMiner successful\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- public imports\n",
+ "import os\n",
+ "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
+ "import pandas as pd\n",
+ "from torch.utils.data import DataLoader\n",
+ "import torch\n",
+ "from pandas.testing import assert_frame_equal\n",
+ "import time\n",
+ "\n",
+ "# -- private imports\n",
+ "from colabtools.utils import move_to_device\n",
+ "\n",
+ "# -- dev imports\n",
+ "#%load_ext autoreload\n",
+ "#%autoreload 2\n",
+ "\n",
+ "from argminer.data import ArgumentMiningDataset, TUDarmstadtProcessor, PersuadeProcessor, DataProcessor, create_labels_doc_level, df_from_text_files\n",
+ "from argminer.evaluation import inference"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3656e605",
+ "metadata": {},
+ "source": [
+ "# Kaggle Dataset (PERSUADE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "23a14770",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#path = '../../data/kaggle/feedback-prize-2021'\n",
+ "#df_expected = create_labels_doc_level(os.path.join(path, 'train'), os.path.join(path, 'train.csv'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "bb61488b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df_expected[df_expected.id == '423A1CA112E2'].text.values[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "2b9be081",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#ft = df_expected[df_expected.id == '423A1CA112E2']\n",
+ "#len(ft.labels.values[0])\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "aeb87a61",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df_texts = df_from_text_files(os.path.join(path, 'train'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c20c685b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df_texts[df_texts.id == '423A1CA112E2'].text.values[0][8:229]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "99fa776d",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_15570/2067667223.py:6: UserWarning: PersuadeProcessor does not have a preprocessor. Instead the postprocess method will prepare the data end-to-end\n",
+ " processor = processor.preprocess().process('bieo').postprocess()\n",
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_15570/2067667223.py:6: UserWarning: PersuadeProcessor does not have a processor. Instead the postprocess method will prepare the data end-to-end\n",
+ " processor = processor.preprocess().process('bieo').postprocess()\n",
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_15570/2067667223.py:6: DeprecationWarning: The postprocess method is behaving in a special way because of data corruption. This behaviour will change in the future.\n",
+ " processor = processor.preprocess().process('bieo').postprocess()\n"
+ ]
+ }
+ ],
+ "source": [
+ "## verify predictionString start\n",
+ "\n",
+ "# TODO bieo not working?\n",
+ "path_train = '../../data/kaggle/feedback-prize-2021'\n",
+ "processor = PersuadeProcessor(path_train)\n",
+ "processor = processor.preprocess().process('bieo').postprocess()\n",
+ "df = processor.dataframe "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "57d0caa6",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "df_dict = PersuadeProcessor().from_json().get_tts(0.3, 0.2)\n",
+ "df_test = df_tes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "520e19a5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'test': doc_id text \\\n",
+ " 6302 54EC5A090FB2 Dear Florida Senator,\\n\\nThe Electoral College... \n",
+ " 11840 62BC144D48C1 The new technology called Facial Action Coding... \n",
+ " 9550 4006B9AAD071 Dear State Senator,\\n\\nThe Electoral College i... \n",
+ " 879 7AD4CB4029C0 Dear Principal,\\n\\nI think that we do need to ... \n",
+ " 7565 7D16EFA4EEAD I never regretted joining the Seagoing Cowboys... \n",
+ " ... ... ... \n",
+ " 9496 D1AF5CE5040E Generic_Name\\n\\nWriting SOL\\n\\nMarch 11, 2020\\... \n",
+ " 13977 387C6CD3FD66 Did you ever work on yearbook, play a sport, o... \n",
+ " 10079 DC8FC7A75827 I think that the schools policy for phones is ... \n",
+ " 11894 D447C1CC7423 Dear TEACHER_NAME,\\n\\nI do think community ser... \n",
+ " 3781 490C2D42AFCE Mr. senator i think we should get rid of the e... \n",
+ " \n",
+ " labels \n",
+ " 6302 [O, O, O, B-Position, I-Position, I-Position, ... \n",
+ " 11840 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... \n",
+ " 9550 [O, O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Le... \n",
+ " 879 [O, O, B-Position, I-Position, I-Position, I-P... \n",
+ " 7565 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... \n",
+ " ... ... \n",
+ " 9496 [O, O, O, O, O, O, O, O, O, B-Lead, I-Lead, I-... \n",
+ " 13977 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... \n",
+ " 10079 [B-Position, I-Position, I-Position, I-Positio... \n",
+ " 11894 [O, O, B-Position, I-Position, I-Position, I-P... \n",
+ " 3781 [B-Position, I-Position, I-Position, I-Positio... \n",
+ " \n",
+ " [4678 rows x 3 columns],\n",
+ " 'train': doc_id text \\\n",
+ " 3699 D6E6CFF686BF Opinions are great when it comes to seeking ad... \n",
+ " 13272 3E2162BC32F6 I agree with the development of driverless car... \n",
+ " 2784 46E51980E4FB Transpotation has been a key part of many soci... \n",
+ " 2475 4BA9296EBE9F A topic that is debated is if students should ... \n",
+ " 11771 8C55F83A618F If people limited the use of cars the earth wo... \n",
+ " ... ... ... \n",
+ " 3341 B33AAEEF92E5 Dear principle,\\n\\nThe purpose of this letter ... \n",
+ " 5500 CDE3CED1BF53 Have you ever recieved helpful advice from som... \n",
+ " 8172 F9EBBBF6710E Driverless cars are very dangerous. I think th... \n",
+ " 3391 754AA308D69B Dear Principal,\\n\\nI don't think that kid's sh... \n",
+ " 14988 836389DC45F4 A letter to my principle\\n\\nI think students s... \n",
+ " \n",
+ " labels \n",
+ " 3699 [B-Position, I-Position, I-Position, I-Positio... \n",
+ " 13272 [B-Position, I-Position, I-Position, I-Positio... \n",
+ " 2784 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... \n",
+ " 2475 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... \n",
+ " 11771 [B-Position, I-Position, I-Position, I-Positio... \n",
+ " ... ... \n",
+ " 3341 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... \n",
+ " 5500 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... \n",
+ " 8172 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... \n",
+ " 3391 [O, O, B-Position, I-Position, I-Position, I-P... \n",
+ " 14988 [O, O, O, O, O, B-Position, I-Position, I-Posi... \n",
+ " \n",
+ " [10916 rows x 3 columns]}"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "processor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "54ca31fc",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'processor' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mprocessor\u001b[49m\u001b[38;5;241m.\u001b[39mfrom_json()\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'processor' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "processor.from_json()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "a1d154a9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mPersuadeProcessor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[0;31mTypeError\u001b[0m: from_json() missing 1 required positional argument: 'self'"
+ ]
+ }
+ ],
+ "source": [
+ "PersuadeProcessor.from_json()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "b2da4867",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame({'test':[1,2,3,4]}).tail(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3bd30c2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'df' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m a \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mdf\u001b[49m[[\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m]]\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "a = np.arange(2)\n",
+ "df[[True, False]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "8f9ce1b8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ True, True, True, True, True, True, True, True, True,\n",
+ " True])"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.full(10, True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "adbfe4c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "'[1] not in index'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [18]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/pandas/core/indexing.py:967\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 964\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 966\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m--> 967\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/pandas/core/indexing.py:1191\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m key\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 1189\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index with multidimensional key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_iterable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1193\u001b[0m \u001b[38;5;66;03m# nested tuple slicing\u001b[39;00m\n\u001b[1;32m 1194\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_nested_tuple(key, labels):\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/pandas/core/indexing.py:1132\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m 1131\u001b[0m \u001b[38;5;66;03m# A collection of keys\u001b[39;00m\n\u001b[0;32m-> 1132\u001b[0m keyarr, indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_reindex_with_indexers(\n\u001b[1;32m 1134\u001b[0m {axis: [keyarr, indexer]}, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, allow_dups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 1135\u001b[0m )\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/pandas/core/indexing.py:1327\u001b[0m, in \u001b[0;36m_LocIndexer._get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1324\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m 1325\u001b[0m axis_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis_name(axis)\n\u001b[0;32m-> 1327\u001b[0m keyarr, indexer \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1329\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/pandas/core/indexes/base.py:5782\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m 5779\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 5780\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 5782\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5784\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m 5785\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[1;32m 5786\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/pandas/core/indexes/base.py:5845\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m 5842\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5844\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m-> 5845\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "\u001b[0;31mKeyError\u001b[0m: '[1] not in index'"
+ ]
+ }
+ ],
+ "source": [
+ "df.loc[np.array([1,3])]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7bdab05c",
+ "metadata": {},
+ "source": [
+ "# Persuade Dataset /w Adversarial"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "85f59b70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "preprocess --> text in raw -> finds segments that are labelled other, adds in correct to data\n",
+ "process --> inputs (id, segment, segment_label) -> processor(inputs) -> labelling(inputs) -> output(id, segment, word_labels) \n",
+ "postprocess --> agg into passages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4ff85810",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "d53d714e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class PersuadeProcessor(DataProcessor):\n",
+ " \n",
+ " def __init__(self, path=''):\n",
+ " super().__init__(path)\n",
+ " \n",
+ " def _preprocess(self):\n",
+ " path_to_text_dir = os.path.join(self.path, 'train')\n",
+ " path_to_ground_truth = os.path.join(self.path, 'train.csv')\n",
+ " \n",
+ " df_ground_truth = pd.read_csv(path_to_ground_truth)\n",
+ " df_ground_truth.predictionstring = df_ground_truth.predictionstring.apply(lambda x: [int(num) for num in x.split()])\n",
+ " df_ground_truth = df_ground_truth.sort_values(['id', 'discourse_start', 'discourse_end'])\n",
+ " df_ground_truth['pred_str_start_id'] = df_ground_truth.predictionstring.apply(lambda x: x[0])\n",
+ " df_ground_truth['pred_str_end_id'] = df_ground_truth.predictionstring.apply(lambda x: x[-1])\n",
+ " \n",
+ " df_texts = df_from_text_files(path_to_text_dir)\n",
+ " \n",
+ " df_texts['text_split'] = df_texts.text.str.split()\n",
+ " df_texts['range'] = df_texts['text_split'].apply(lambda x: list(range(len(x))))\n",
+ " df_texts['start_id'] = df_texts['range'].apply(lambda x: x[0])\n",
+ " df_texts['end_id'] = df_texts['range'].apply(lambda x: x[-1])\n",
+ " df_ground_truth = df_ground_truth.merge(\n",
+ " df_texts\n",
+ " )\n",
+ " max_id = df_ground_truth.groupby('id').agg({'pred_str_end_id':'max'}).reset_index()\n",
+ " print(max_id)\n",
+ " end_id = df_texts[['id', 'end_id']]\n",
+ " ids = max_id.merge(end_id)\n",
+ " print((ids.pred_str_end_id <= ids.end_id).all())\n",
+ " \n",
+ " df_ground_truth['discourse_text_pred_string'] = df_ground_truth.apply(\n",
+ " lambda x: ' '.join(x.text_split[x.predictionstring[0]: x.predictionstring[-1]+1]), axis=1\n",
+ " )\n",
+ " \n",
+ " discourse_text = df_ground_truth.discourse_text.str.strip().values\n",
+ " discourse_text_pred = df_ground_truth.discourse_text_pred_string.values\n",
+ " ids_not_equal = discourse_text != discourse_text_pred\n",
+ " \n",
+ " print(ids_not_equal.mean())\n",
+ " for i, row in enumerate(df_ground_truth[ids_not_equal].itertuples(index=False)):\n",
+ " print(row.discourse_text)\n",
+ " print(row.discourse_text_pred_string)\n",
+ " print()\n",
+ " if i > 100:\n",
+ " break\n",
+ " #print(row.predictionstring[0], row.predictionstring[-1])\n",
+ "\n",
+ " # print(row.discourse_text, )\n",
+ " # break\n",
+ " #from pandas.testing import assert_frame_equal\n",
+ " #assert_frame_equal(df_texts, df_texts_2)\n",
+ " self.dataframe = df\n",
+ " self.status = 'preprocessed'\n",
+ " return self\n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "1df7078e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " id pred_str_end_id\n",
+ "0 0000D23A521A 250\n",
+ "1 00066EA9880D 645\n",
+ "2 000E6DE9E817 271\n",
+ "3 001552828BD0 511\n",
+ "4 0016926B079C 260\n",
+ "... ... ...\n",
+ "15589 FFF1442D6698 650\n",
+ "15590 FFF1ED4F8544 411\n",
+ "15591 FFF868E06176 445\n",
+ "15592 FFFD0AF13501 249\n",
+ "15593 FFFF80B8CC2F 167\n",
+ "\n",
+ "[15594 rows x 2 columns]\n",
+ "True\n",
+ "0.26261149189496374\n",
+ "even though there are some children out there that really needs help with their academic work\n",
+ "even though there are some children out there that really needs help with their academic work,\n",
+ "\n",
+ "Sometimes teachers or even principal needs to consider that we should give the help that any student should have\n",
+ "Sometimes teachers or even principal needs to consider that we should give the help that any student should have.\n",
+ "\n",
+ "Not only does diesel fuel hurt our ecosystem, it hurts our wallets too\n",
+ "Not only does diesel fuel hurt our ecosystem, it hurts our wallets too.\n",
+ "\n",
+ "I think that students would benefit from learning at home\n",
+ "I think that students would benefit from learning at home,because\n",
+ "\n",
+ "because they wont have to change \n",
+ "they wont have to change and\n",
+ "\n",
+ " get up early in the morning to shower and do there hair\n",
+ "get up early in the morning to shower and do there hair.\n",
+ "\n",
+ "they'll be pay more attention\n",
+ "they'll be pay more attention.\n",
+ "\n",
+ "most students usually take showers before school. they either take it before they sleep or when they wake up. some students do both to smell good. that causes them do miss the bus and effects on there lesson time cause they come late to school \n",
+ "most students usually take showers before school. they either take it before they sleep or when they wake up. some students do both to smell good. that causes them do miss the bus and effects on there lesson time cause they come late to school.\n",
+ "\n",
+ "when your home your comfortable and you pay attention \n",
+ "when your home your comfortable and you pay attention.\n",
+ "\n",
+ "The text says in paragraph five \"... Venus would allow scientists to float above the fray. Imagine a bimp -like vehicle hovering 30 or so miles above the roiling Venusian landscape. Just as our jet airplaines travel at a higher altitude to fly over many storms, a vehicle hovering over Venus would avoid the unfriendly ground conditions by staying up and out of their way\".\n",
+ "\n",
+ "The author is showing that it will be some improvement on this planet which is why it is worth visiting. Also in paragraph six, towards the ending that researches are working on a way to improve our knowlege on Venus. \n",
+ "The text says in paragraph five \"... Venus would allow scientists to float above the fray. Imagine a bimp -like vehicle hovering 30 or so miles above the roiling Venusian landscape. Just as our jet airplaines travel at a higher altitude to fly over many storms, a vehicle hovering over Venus would avoid the unfriendly ground conditions by staying up and out of their way\". The author is showing that it will be some improvement on this planet which is why it is worth visiting. Also in paragraph six, towards the ending that researches are working on a way to improve our knowlege on Venus.\n",
+ "\n",
+ "It is every student's dream to be able to lounge around at home, and complete school from the comforts of their own beds. Though, for some students, this long desired fantasy is becoming a reality. With the introduction of new and innovative technologies into the classroom, distance learning is now a common practice, whether it be online or through video conferencing. However, to pretend that this is a solution to the ever-growing problems within the education system, is willfully ignorant \n",
+ "It is every student's dream to be able to lounge around at home, and complete school from the comforts of their own beds. Though, for some students, this long desired fantasy is becoming a reality. With the introduction of new and innovative technologies into the classroom, distance learning is now a common practice, whether it be online or through video conferencing. However, to pretend that this is a solution to the ever-growing problems within the education system, is willfully ignorant.\n",
+ "\n",
+ "But, they would then forfeit the opportunity to build relationships with other kids of their own age. Especially with younger kids and kids who like to stay at home or be alone, learning and building social skills starts at school.\n",
+ "But, they would then forfeit the opportunity to build relationships with other kids of their own age. Especially with younger kids and kids who like to stay at home or be alone, learning and building social skills starts at school.\n",
+ "\n",
+ "Having a friend who is online-schooled, who admits frequently that he has ample free time, which he wastes on playing video games and watching TV, while at the same time, students in the classroom are made to do tedious book-work, is very eye-opening to a pressing issue. Students who take classes online have more time to slack off and goof around, and for the less-disciplined child, this will reflect negatively in their grades \n",
+ "Having a friend who is online-schooled, who admits frequently that he has ample free time, which he wastes on playing video games and watching TV, while at the same time, students in the classroom are made to do tedious book-work, is very eye-opening to a pressing issue. Students who take classes online have more time to slack off and goof around, and for the less-disciplined child, this will reflect negatively in their grades.\n",
+ "\n",
+ "However, some people will raise the point that there are distractions within in the classroom and students will slack off anyway, if they so desire \n",
+ "However, some people will raise the point that there are distractions within in the classroom and students will slack off anyway, if they so desire.\n",
+ "\n",
+ "It's easily understandable that for some students, distance learning is simply the only viable option. Whether they physically cannot make it to the school building, or because their school merely doesn't offer the classes they wish to take, sometimes students are more or less forced to take online classes. And this is completely acceptable and understandable, and no one should fault these students for desiring an education and seeking out alternative options \n",
+ "It's easily understandable that for some students, distance learning is simply the only viable option. Whether they physically cannot make it to the school building, or because their school merely doesn't offer the classes they wish to take, sometimes students are more or less forced to take online classes. And this is completely acceptable and understandable, and no one should fault these students for desiring an education and seeking out alternative options.\n",
+ "\n",
+ "Distance education will most likely always be a hotly debated topic, in which both sides of the argument will raise many deeply introspective and thought-provoking questions and points. In some cases, distance education is necessary and important. However, it cannot be ignored that distance learning is not conducive or beneficial to most students. The lack of a normal classroom set-up, with teachers and classmates replaced for a laptop and a of couple blankets, fails to help students develop vital social and communication skills, while simultaneously aiding in the slacking off and wasting time of many students. All in all, distance learning and education isn't an appropriate replacement for a normal education that is received in a classroom, at a desk. \n",
+ "Distance education will most likely always be a hotly debated topic, in which both sides of the argument will raise many deeply introspective and thought-provoking questions and points. In some cases, distance education is necessary and important. However, it cannot be ignored that distance learning is not conducive or beneficial to most students. The lack of a normal classroom set-up, with teachers and classmates replaced for a laptop and a of couple blankets, fails to help students develop vital social and communication skills, while simultaneously aiding in the slacking off and wasting time of many students. All in all, distance learning and education isn't an appropriate replacement for a normal education that is received in a classroom, at a desk.\n",
+ "\n",
+ "Cars have been an issue to our community for a very long time. Car usage has been a problem to our pollution and emission. Limiting car usage would be able to help eliminate the smog that is polluting. There are beneficial implications for carbon emissions and the environment\n",
+ "Cars have been an issue to our community for a very long time. Car usage has been a problem to our pollution and emission. Limiting car usage would be able to help eliminate the smog that is polluting. There are beneficial implications for carbon emissions and the environment.\n",
+ "\n",
+ "People have been fined for causing accidents due to the smog in the air from cars in Paris. Delivery companies even lost renevue when cars were being made \n",
+ "People have been fined for causing accidents due to the smog in the air from cars in Paris. Delivery companies even lost renevue when cars were being made.\n",
+ "\n",
+ "New communities are becoming less dependent on cars \n",
+ "New communities are becoming less dependent on cars.\n",
+ "\n",
+ "If all people did was use cars as transportation, there would be no way in getting that exercise and fresh air you're supposed to get \n",
+ "If all people did was use cars as transportation, there would be no way in getting that exercise and fresh air you're supposed to get.\n",
+ "\n",
+ "Advantages in limiting car usage is endless \n",
+ "Advantages in limiting car usage is endless,\n",
+ "\n",
+ "Waking up from a wonderful dream, you look around to what you can describe as your dull or gloomy-like room. Without even realizing, you've already picked up your phone and start to browse your social media. You check the time and go look outside your window and it looks like a wonderful day. You quickly decide to blast your music and dress up fashionably before you go out to your friend's place. Alast you finish by taking many, many selfies and videos and then share them all to your friends and a few social media platforms while getting in the car. Now you've started the car and are now on the way to your friend's house as planned before. On your drive, text messages start to ring your phone a lot and people try calling you. Meanwhile you are on the highway. Mind you, you just started driving two weeks before, so you're practically new to this.\n",
+ "\n",
+ "At this point you only have two options, are you willing to get on this phone while driving? Or should you wait until your vehicle is at rest? You want to convince yourself that it may be an emergency but is it really a smart decision to fiddle with that device while driving? Let alone on a highway at about 75 mph? Which leads to the topic of this essay, whether or not a driver should use their cell phone in any capacity while operating a vehicle. \n",
+ "Waking up from a wonderful dream, you look around to what you can describe as your dull or gloomy-like room. Without even realizing, you've already picked up your phone and start to browse your social media. You check the time and go look outside your window and it looks like a wonderful day. You quickly decide to blast your music and dress up fashionably before you go out to your friend's place. Alast you finish by taking many, many selfies and videos and then share them all to your friends and a few social media platforms while getting in the car. Now you've started the car and are now on the way to your friend's house as planned before. On your drive, text messages start to ring your phone a lot and people try calling you. Meanwhile you are on the highway. Mind you, you just started driving two weeks before, so you're practically new to this. At this point you only have two options, are you willing to get on this phone while driving? Or should you wait until your vehicle is at rest? You want to convince yourself that it may be an emergency but is it really a smart decision to fiddle with that device while driving? Let alone on a highway at about 75 mph? Which leads to the topic of this essay, whether or not a driver should use their cell phone in any capacity while operating a vehicle.\n",
+ "\n",
+ "More research states that pros of using a cellphone are for lifeline, navigation, cameras, traffic reports, and crime reporting. Drivers may need lifeline for road-side assistance for problems like car accidents where time is tight or paramedics and other emergency contacts that may be needed. Navigation assists drivers in locationing and maps providing real-time traffic reports, this supports the hands-free benefit of cell phones by a lot because it is very much needed. Cameras are important for quick situations such as car crashes. The person may need to hurry and catch a picture of the licence plate of whom might be performing a hit and run. Traffic reports provide drivers with up-to-date traffic updates on traffic jams, highway closings, and accidents. And lastly crime reporting for suspicious and illegal actions a driver may have spotted. With research it's clear many drivers can support that there are ways to operate vehicles while driving safely\n",
+ "More research states that pros of using a cellphone are for lifeline, navigation, cameras, traffic reports, and crime reporting. Drivers may need lifeline for road-side assistance for problems like car accidents where time is tight or paramedics and other emergency contacts that may be needed. Navigation assists drivers in locationing and maps providing real-time traffic reports, this supports the hands-free benefit of cell phones by a lot because it is very much needed. Cameras are important for quick situations such as car crashes. The person may need to hurry and catch a picture of the licence plate of whom might be performing a hit and run. Traffic reports provide drivers with up-to-date traffic updates on traffic jams, highway closings, and accidents. And lastly crime reporting for suspicious and illegal actions a driver may have spotted. With research it's clear many drivers can support that there are ways to operate vehicles while driving safely.\n",
+ "\n",
+ " What if all the good people in sports have a C average in school \n",
+ "What if all the good people in sports have a C average in school,\n",
+ "\n",
+ "that means that they cant play and were going to lose every game we have. That's a good policy to get grade's up but don't take away something they care about. Everyone should be able to go out for sports if they want to\n",
+ "that means that they cant play and were going to lose every game we have. That's a good policy to get grade's up but don't take away something they care about. Everyone should be able to go out for sports if they want to.\n",
+ "\n",
+ "I know if you do intend to proceed in changing the policy we students will try to get better grades to play the sport\n",
+ "I know if you do intend to proceed in changing the policy we students will try to get better grades to play the sport,\n",
+ "\n",
+ " but not every kid has a GPA of 4.0\n",
+ "but not every kid has a GPA of 4.0.\n",
+ "\n",
+ ". So their for i propose my idea to keep the policy that you just have to have a 2.0!\n",
+ "So their for i propose my idea to keep the policy that you just have to have a 2.0! Seriously,\n",
+ "\n",
+ "Seriously, as my 6th grade teacher told me, \"something is better than nothing\". Sure you want us to have good grades but us kids,(teens) want to be involved in school activities but with or with out good grades i think if they strive hard enough they deserve the right to be involved\n",
+ "Seriously, as my 6th grade teacher told me, \"something is better than nothing\". Sure you want us to have good grades but us kids,(teens) want to be involved in school activities but with or with out good grades i think if they strive hard enough they deserve the right to be involved.\n",
+ "\n",
+ "The face on Mars soon became a pop icon; shot in movies, appeared in books, magazines, radio talk shows, and haunted grocery store checkout lines for 25 years. Some people thought the natural landform was evidence of life on Mars, and that us scientists wanted to hide it, but really, the defenders of the NASA budget wish there was ancient civilization on Mars. We decided to take another shot just to make sure we weren't wrong, on April 5, 1998. Michael Malin and his Mars Orbiter camera team took a picture that was ten times sharper than the original Viking photos, revealing a natural landform, which meant no alien monument \n",
+ "The face on Mars soon became a pop icon; shot in movies, appeared in books, magazines, radio talk shows, and haunted grocery store checkout lines for 25 years. Some people thought the natural landform was evidence of life on Mars, and that us scientists wanted to hide it, but really, the defenders of the NASA budget wish there was ancient civilization on Mars. We decided to take another shot just to make sure we weren't wrong, on April 5, 1998. Michael Malin and his Mars Orbiter camera team took a picture that was ten times sharper than the original Viking photos, revealing a natural landform, which meant no alien monument.\n",
+ "\n",
+ "\"Voters in presidential elections are people who want to express a politcal prefrence rather than people who think that a single vote may decide an election......\"\n",
+ "\"Voters in presidential elections are people who want to express a politcal prefrence rather than people who think that a single vote may decide an election......\"in\n",
+ "\n",
+ "the people who vote get to express a political prefrence than a person who just votes just to see if they can make a diffrence in just one vote .\n",
+ "the people who vote get to express a political prefrence than a person who just votes just to see if they can make a diffrence in just one vote .The\n",
+ "\n",
+ "The reason larger states get more attention than small states is because the population is higher in the large states and the more people there is have diffrent opinions. \n",
+ "reason larger states get more attention than small states is because the population is higher in the large states and the more people there is have diffrent opinions. This\n",
+ "\n",
+ "I disagree in having to make students participate in a sport, or extracurricular activity.\n",
+ "I disagree in having to make students participate in a sport, or extracurricular activity.\n",
+ "\n",
+ "Not all students enjoy playing sports or participating in a school activity \n",
+ "Not all students enjoy playing sports or participating in a school activity.\n",
+ "\n",
+ "Not many parents have the time or money to put their kids in such either\n",
+ "Not many parents have the time or money to put their kids in such either,\n",
+ "\n",
+ "When being forced to do something such as a sport or school activity the school has to find a variety of the kids liking \n",
+ "When being forced to do something such as a sport or school activity the school has to find a variety of the kids liking.\n",
+ "\n",
+ "Making sure all the kids in his school have a place to have fun at and not just something they have to do and dont enjoy. Having all kids in something at a school can cause running into dept because thats a lot of kids you have to put money towards. Sports can run as much as two to a hundred dollars per kid for any sport, and when having to be forced into a sport that you dont enjoy why waist money. Even when the kids dont want to participate why even try putting your hard working money towards it. \n",
+ "Making sure all the kids in his school have a place to have fun at and not just something they have to do and dont enjoy. Having all kids in something at a school can cause running into dept because thats a lot of kids you have to put money towards. Sports can run as much as two to a hundred dollars per kid for any sport, and when having to be forced into a sport that you dont enjoy why waist money. Even when the kids dont want to participate why even try putting your hard working money towards it.\n",
+ "\n",
+ "The school has to buy equipment and tools for those opportunities and, sometimes not all parents can physically do that. Sports are a lot of money and when having to be put into one not knowing how you are going to pay for it can be embarrassing. Letting your kid down is the least a father or mother would ever want to do, and when working in the school board having to deal with phone calls from parents saying they cant afford a extracurricular activity would be heartbreaking. \n",
+ "The school has to buy equipment and tools for those opportunities and, sometimes not all parents can physically do that. Sports are a lot of money and when having to be put into one not knowing how you are going to pay for it can be embarrassing. Letting your kid down is the least a father or mother would ever want to do, and when working in the school board having to deal with phone calls from parents saying they cant afford a extracurricular activity would be heartbreaking.\n",
+ "\n",
+ "Kids work really hard to put forth effort on school work,homework, and participate in school projects. That can be already enough stress on a kid no matter what age \n",
+ "Kids work really hard to put forth effort on school work,homework, and participate in school projects. That can be already enough stress on a kid no matter what age.\n",
+ "\n",
+ "This is why I disagree on having to make kids participate in a sport or activity. Its way to much work, stress, and money. So when coming down to a decision I agree with a no because having every kid in a sport or activity is absurd. \n",
+ "This is why I disagree on having to make kids participate in a sport or activity. Its way to much work, stress, and money. So when coming down to a decision I agree with a no because having every kid in a sport or activity is absurd.\n",
+ "\n",
+ "I agree 100% with you. i think in order to be on a team sport you will have to have you grade up to a B \n",
+ "I agree 100% with you. i think in order to be on a team sport you will have to have you grade up to a B.\n",
+ "\n",
+ "I think that because its not fair to the teachers if all you think about is sports and your grades are going down in school but you are doing sports. That telling the teachers you just don't care. Also i think its unfair to the other students that you are on the soccer team but have grades that are failing and you miss practice all the time because you have detention. Its fair do have the honor students play for are school even if there not the best in the school. Also its unfair to you. You will have bad grades but do well in sports \n",
+ "I think that because its not fair to the teachers if all you think about is sports and your grades are going down in school but you are doing sports. That telling the teachers you just don't care. Also i think its unfair to the other students that you are on the soccer team but have grades that are failing and you miss practice all the time because you have detention. Its fair do have the honor students play for are school even if there not the best in the school. Also its unfair to you. You will have bad grades but do well in sports.\n",
+ "\n",
+ "how to make our community better place\n",
+ "how to make our community better place,\n",
+ "\n",
+ "why is community important for students to know\n",
+ "why is community important for students to know.\n",
+ "\n",
+ "students will participate in a group of students making a list what our community needs, therefore students will learn what our community needs! students will present their list of things our community needs \n",
+ "students will participate in a group of students making a list what our community needs, therefore students will learn what our community needs! students will present their list of things our community needs!\n",
+ "\n",
+ "but when get to know what our community is, they would want to know about and would want to participate and know why is our community important\n",
+ "but when get to know what our community is, they would want to know about and would want to participate and know why is our community important.\n",
+ "\n",
+ "tudents will receive a good grade if they participate in this community because is like an extra credit. \n",
+ "will receive a good grade if they participate in this community because is like an extra credit. due\n",
+ "\n",
+ "Reducing the temperature of a rapidly heating planet will allow us to savour our oceans rather than running from them as they grow to envelop our costal cities\n",
+ "Reducing the temperature of a rapidly heating planet will allow us to savour our oceans rather than running from them as they grow to envelop our costal cities.\n",
+ "\n",
+ "To begin, cars emit green house gasses which will acumulate in our atmosphere trapping heat underneath it as it tries to leave our planet. This leads to a cumilative effect that causes the earth's average temperature to rise. In Suburb, Life Goes On Without Cars Elizabeth Rosenthal says \"Green house gas emitions from cars in America make up fifty percent of all greenhouse gas emitions in the country.\" America barely has any regulations in relation to greenhouse gas emitions to start with but this is crazy. This problem could be easily solved with a few regulations and some smart city planning. The benifits of this includes but is not limited to, more temperate climate, less airborne carcinogens and of course no crazy drivers. \n",
+ "To begin, cars emit green house gasses which will acumulate in our atmosphere trapping heat underneath it as it tries to leave our planet. This leads to a cumilative effect that causes the earth's average temperature to rise. In Suburb, Life Goes On Without Cars Elizabeth Rosenthal says \"Green house gas emitions from cars in America make up fifty percent of all greenhouse gas emitions in the country.\" America barely has any regulations in relation to greenhouse gas emitions to start with but this is crazy. This problem could be easily solved with a few regulations and some smart city planning. The benifits of this includes but is not limited to, more temperate climate, less airborne carcinogens and of course no crazy drivers.\n",
+ "\n",
+ "this story really showed me that the face on mars is just a landform \n",
+ "this story really showed me that the face on mars is just a landform.\n",
+ "\n",
+ "First, it was just a shadow that caught NASA's eye \n",
+ "First, it was just a shadow that caught NASA's eye.\n",
+ "\n",
+ "Second, it was shown has a huge rock formation \n",
+ "Second, it was shown has a huge rock formation.\n",
+ "\n",
+ "When NASA first saw this they thought it looked like a face \n",
+ "When NASA first saw this they thought it looked like a face.\n",
+ "\n",
+ "Finally, the main thought of as a alien momument \n",
+ "Finally, the main thought of as a alien momument.\n",
+ "\n",
+ "There is no way we can embed every single one of America's roads with an electrical cable, it's just not a possible thing to do. With America already in so much debt, I can't see how making these roads a reality would actually help the United States in any way.\n",
+ "\n",
+ "Now we are going to scratch the smart road thing because we all know that it's ridiculous, and move on to a semi-possible option-smartcars. \n",
+ "There is no way we can embed every single one of America's roads with an electrical cable, it's just not a possible thing to do. With America already in so much debt, I can't see how making these roads a reality would actually help the United States in any way. Now we are going to scratch the smart road thing because we all know that it's ridiculous, and move on to a semi-possible option-smartcars.\n",
+ "\n",
+ " for they would be deprived of social interactions \n",
+ "for they would be deprived of social interactions,\n",
+ "\n",
+ "hands-on experiences\n",
+ "hands-on experiences,\n",
+ "\n",
+ "Through online schooling, students would not be allowed opportunities to interact with their peers, inhibiting the development of their social skills. Instead of collaborating with their classmates, students enrolled in online schools would have to complete assignments independently, limiting the sharing of ideas. Without the opportunity to share and discuss ideas, students would not be able to develop significant social skills, which include patience, open-mindedness, and the ability to peacefully collaborate.\n",
+ "\n",
+ "Without such skills, the students would be at a great disadvantage in future endeavors requiring teamwork, which are commonly presented throughout adulthood. \n",
+ "Through online schooling, students would not be allowed opportunities to interact with their peers, inhibiting the development of their social skills. Instead of collaborating with their classmates, students enrolled in online schools would have to complete assignments independently, limiting the sharing of ideas. Without the opportunity to share and discuss ideas, students would not be able to develop significant social skills, which include patience, open-mindedness, and the ability to peacefully collaborate. Without such skills, the students would be at a great disadvantage in future endeavors requiring teamwork, which are commonly presented throughout adulthood.\n",
+ "\n",
+ "In traditional science classes, for example, students participate in laboratory investigations regarding pertinent aspects of their studies. Through such activities, students are allowed to apply their knowledge and reasoning skills to create memorable understandings of the investigated topics.\n",
+ "\n",
+ "Online schools, however, cannot offer such activities, confining students to less involved and therefore less effective means of learning. Without interactive activities, students of online schools would not be able to obtain enduring understandings of learned concepts, ultimately limiting their educational horizons. \n",
+ "In traditional science classes, for example, students participate in laboratory investigations regarding pertinent aspects of their studies. Through such activities, students are allowed to apply their knowledge and reasoning skills to create memorable understandings of the investigated topics. Online schools, however, cannot offer such activities, confining students to less involved and therefore less effective means of learning. Without interactive activities, students of online schools would not be able to obtain enduring understandings of learned concepts, ultimately limiting their educational horizons.\n",
+ "\n",
+ "Overall, while online schooling would allow more comfort for the students, it would greatly inhibit their academic affairs, for they would not have access to crucial aspects of educational development, including social interaction, interactive experiences, and individualized relationships with educators. Without these fundamental components, students would not acquire lasting, solid educations, limiting their career possibilities and therefore the quality of their lives.\n",
+ "\n",
+ "To optimize the educational as well as general quality of future generations, physical schooling must be promoted rather than the attendance of school from home. \n",
+ "Overall, while online schooling would allow more comfort for the students, it would greatly inhibit their academic affairs, for they would not have access to crucial aspects of educational development, including social interaction, interactive experiences, and individualized relationships with educators. Without these fundamental components, students would not acquire lasting, solid educations, limiting their career possibilities and therefore the quality of their lives. To optimize the educational as well as general quality of future generations, physical schooling must be promoted rather than the attendance of school from home.\n",
+ "\n",
+ "One thing that you could do to maybe improve the grades is making sure that they are always up to date on their work and making sure that they are getting decent grades\n",
+ "One thing that you could do to maybe improve the grades is making sure that they are always up to date on their work and making sure that they are getting decent grades.\n",
+ "\n",
+ ". My old volleyball coach had us do a grade checks and S-U forms every week to keep up with our grades. We would have to have a teacher, parent, and the coach sign it every week. The S was for is for satisfactory or good behavior in class and the U was for unsatisfactory, or needs work on the behavior in class. The teacher would circle the S or the U twice a day, once in the beginning of class and then once at the end of the class. Then the teacher would leave a comment and what homework that was assigned that day. The grade checks allowed the teacher to keep in touch with the student's grade as well as the coach, and parent. \n",
+ "My old volleyball coach had us do a grade checks and S-U forms every week to keep up with our grades. We would have to have a teacher, parent, and the coach sign it every week. The S was for is for satisfactory or good behavior in class and the U was for unsatisfactory, or needs work on the behavior in class. The teacher would circle the S or the U twice a day, once in the beginning of class and then once at the end of the class. Then the teacher would leave a comment and what homework that was assigned that day. The grade checks allowed the teacher to keep in touch with the student's grade as well as the coach, and parent. Some\n",
+ "\n",
+ "\n",
+ "Have you ever seen someone on their phone while driving and not paying any attention? It's becoming more common to see drives on the road and using their cell phones. Although cell phones have not been around for a very long time , they have become a key part of our lives now. People today use their phones to check up on social media, the latest news reports and also to stay in touch with loved ones \n",
+ "Have you ever seen someone on their phone while driving and not paying any attention? It's becoming more common to see drives on the road and using their cell phones. Although cell phones have not been around for a very long time , they have become a key part of our lives now. People today use their phones to check up on social media, the latest news reports and also to stay in touch with loved ones.\n",
+ "\n",
+ "Another reason why phones should not be used while driving. Because driving and on the phone is against the law \n",
+ "Another reason why phones should not be used while driving. Because driving and on the phone is against the law.\n",
+ "\n",
+ "According to state law \" Drivers may listen to streaming music that does not include videos on the screen of their phone or device while driving but cannot touch their phones while on the road to activate or program any music streaming app.\n",
+ "\n",
+ "Streaming music that is controlled by and listened through the vehicle's radio is allowed. \n",
+ "According to state law \" Drivers may listen to streaming music that does not include videos on the screen of their phone or device while driving but cannot touch their phones while on the road to activate or program any music streaming app. Streaming music that is controlled by and listened through the vehicle's radio is allowed.\n",
+ "\n",
+ "School isn't the funneist things that kids want to do, but it is a part of life and kids have to do it. With a new program or coding like this it could make school seem not so bad. In paragraph 6 it said \"if you smile when a web ad appears on your screen,\n",
+ "\n",
+ "a similar ad might follow.\" If my schools computers did this i would like school at lot better but they can't so school still sucks. If school started to teach this kind of coding and our school computer would have facial recognization then school would'nt be so bad.\n",
+ "School isn't the funneist things that kids want to do, but it is a part of life and kids have to do it. With a new program or coding like this it could make school seem not so bad. In paragraph 6 it said \"if you smile when a web ad appears on your screen, a similar ad might follow.\" If my schools computers did this i would like school at lot better but they can't so school still sucks. If school started to teach this kind of coding and our school computer would have facial recognization then school would'nt be so bad.\n",
+ "\n",
+ "because students are getting tred of the same classes over an over again\n",
+ "because students are getting tred of the same classes over an over again.\n",
+ "\n",
+ " If schools brought in something new to our class sign up sheets there would be no doubt that a bunch of students would want to try this class ou\n",
+ "If schools brought in something new to our class sign up sheets there would be no doubt that a bunch of students would want to try this class out.\n",
+ "\n",
+ "I think that the face is a natural landform because there is no life on Mars that we have descovered yet \n",
+ "I think that the face is a natural landform because there is no life on Mars that we have descovered yet.\n",
+ "\n",
+ "Talking to someone to get an outside opinion could help because this person doesn't know all parts of the story or maybe doesn't know everybody involved personally. This is helpful because they have no sense of bias on who is involved and what is happening in the situation. This person isn't going to sugar coat their words and tell you the truth even if it's something that you don't want to hear. They may simply listen to what you have to say and will already be ready to introduce to you new opinions and will help you look at it from a different perspective. They may tell us the truth to address the things we may be avoiding in the situation such as something that is right or wrong. This helps us because sometimes we need to hear the truth to see why we are right or wrong in these situations. By hearing the truth and possibly getting advice to move forward you get to move forward with all your facts laid out.\n",
+ "Talking to someone to get an outside opinion could help because this person doesn't know all parts of the story or maybe doesn't know everybody involved personally. This is helpful because they have no sense of bias on who is involved and what is happening in the situation. This person isn't going to sugar coat their words and tell you the truth even if it's something that you don't want to hear. They may simply listen to what you have to say and will already be ready to introduce to you new opinions and will help you look at it from a different perspective. They may tell us the truth to address the things we may be avoiding in the situation such as something that is right or wrong. This helps us because sometimes we need to hear the truth to see why we are right or wrong in these situations. By hearing the truth and possibly getting advice to move forward you get to move forward with all your facts laid out.\n",
+ "\n",
+ " Different people that are helpful to seek advice from is someone who is more of an outside opinion because they tell you the truth and dont sugar coat their thoughts. If you seek someone who has a different mindset they will introduce new ways of thinking and different approaches you could take in the situation.\n",
+ "\n",
+ "By expressing all your feelings, thoughts, and emotions you are letting someone know how you feel and getting the support you need to move forward confidently. By talking to multiple people exploring new thoughts and mindsets,\n",
+ "\n",
+ "getting an unbiased opinion, and being able to express and organize your thoughts you are getting set up to make good choices rationally. I personally believe that when someone asks multiple people for advice it and seeking multiple opinions it helps them be able to make smart and rational choices while being confident with their choice and content with their thoughts and emotions. \n",
+ "Different people that are helpful to seek advice from is someone who is more of an outside opinion because they tell you the truth and dont sugar coat their thoughts. If you seek someone who has a different mindset they will introduce new ways of thinking and different approaches you could take in the situation. By expressing all your feelings, thoughts, and emotions you are letting someone know how you feel and getting the support you need to move forward confidently. By talking to multiple people exploring new thoughts and mindsets, getting an unbiased opinion, and being able to express and organize your thoughts you are getting set up to make good choices rationally. I personally believe that when someone asks multiple people for advice it and seeking multiple opinions it helps them be able to make smart and rational choices while being confident with their choice and content with their thoughts and emotions.\n",
+ "\n",
+ "Students who are unable, or not comfortable with traditional education in the classroom have a new alternative that gives them a chance that they have not had before. Students can learn more conveniently for their busy schedules from having to go to school, sports, and other extracurricular activities. This helps many who were originally not able to get an education due to the fact that a traditional classroom was unfit for them and their lifestyle\n",
+ "Students who are unable, or not comfortable with traditional education in the classroom have a new alternative that gives them a chance that they have not had before. Students can learn more conveniently for their busy schedules from having to go to school, sports, and other extracurricular activities. This helps many who were originally not able to get an education due to the fact that a traditional classroom was unfit for them and their lifestyle.\n",
+ "\n",
+ " Distance learning provides students with a convenient learning pace \n",
+ "Distance learning provides students with a convenient learning pace,\n",
+ "\n",
+ "gets parents involved \n",
+ "gets parents involved,\n",
+ "\n",
+ "and a flexible schedule\n",
+ "and a flexible schedule.\n",
+ "\n",
+ "A benefit of learning from home is the increased parent involvement \n",
+ "A benefit of learning from home is the increased parent involvement.\n",
+ "\n",
+ "Although there are downsides to learning from home. While the students are home the opportunity to cheat is much higher since at home there are no teachers constantly monitoring the students work and progress \n",
+ "Although there are downsides to learning from home. While the students are home the opportunity to cheat is much higher since at home there are no teachers constantly monitoring the students work and progress.\n",
+ "\n",
+ "Getting in touch with people while being on the go is an unfortunate hazard in many occupations\n",
+ "Getting in touch with people while being on the go is an unfortunate hazard in many occupations.\n",
+ "\n",
+ "as you may know some students already use their phone during school time. Though some are careful, and use it during gym or lunch,\n",
+ "\n",
+ "not during class.\n",
+ "\n",
+ "One of my friends uses her phone during lunch. She does not cause a problem to anyone and she has the best grades in the class. \n",
+ "as you may know some students already use their phone during school time. Though some are careful, and use it during gym or lunch, not during class. One of my friends uses her phone during lunch. She does not cause a problem to anyone and she has the best grades in the class.\n",
+ "\n",
+ "Since we can not seat anywhere we want at lunch time and we do not always have classes with our friends, I believe that it is not such a a bad idea to use our phones during lunch. We can communicate silently and not disturbed the ones around us\n",
+ "Since we can not seat anywhere we want at lunch time and we do not always have classes with our friends, I believe that it is not such a a bad idea to use our phones during lunch. We can communicate silently and not disturbed the ones around us.\n",
+ "\n",
+ "Honestly, if our school did have the rule that we could have our cell phones out at lunch or free times and not in classes, everyone I know would want that, but so many kids would be texting during class and not paying attention \n",
+ "Honestly, if our school did have the rule that we could have our cell phones out at lunch or free times and not in classes, everyone I know would want that, but so many kids would be texting during class and not paying attention.\n",
+ "\n",
+ "\n",
+ "Either way kids are probably going to still have their phones out and using them. But, I bet if you surveyed the entire school, pretty much everyone would say they wanted to be able to have their phones out, instead of hiding them like they usually do during class \n",
+ "Either way kids are probably going to still have their phones out and using them. But, I bet if you surveyed the entire school, pretty much everyone would say they wanted to be able to have their phones out, instead of hiding them like they usually do during class.\n",
+ "\n",
+ "My name is STUDENT_NAME, and I'm in the 8th grade (also, as you may know, I am one of your Office Aide's for 7th period). I am writing this letter to let you know how I feel about the new policy that you are trying to enforce upon us students. I personally am not in any sports this year, nor was I last year. I do not care very much about them, and many others have called me crazy for that. But, I doubt that there are very many more students that also agree with you're possible policy that \"The Only Way a Student May Participate in Sports or Other Activities Unless They Maintain a B Average\n",
+ "My name is STUDENT_NAME, and I'm in the 8th grade (also, as you may know, I am one of your Office Aide's for 7th period). I am writing this letter to let you know how I feel about the new policy that you are trying to enforce upon us students. I personally am not in any sports this year, nor was I last year. I do not care very much about them, and many others have called me crazy for that. But, I doubt that there are very many more students that also agree with you're possible policy that \"The Only Way a Student May Participate in Sports or Other Activities Unless They Maintain a B Average\".\n",
+ "\n",
+ ". I agree that that policy should be enforced upon us,\n",
+ "I agree that that policy should be enforced upon us, because\n",
+ "\n",
+ " having to maintain a B average might push students harder in doing better in school\n",
+ "having to maintain a B average might push students harder in doing better in school;\n",
+ "\n",
+ "ike studying more and being more focused, now that their sports are at risk. If I were in any sports, and you made that policy official, I would definitely try to study harder, be more focused, and do better in my classes that I don't do as well as others in. \n",
+ "studying more and being more focused, now that their sports are at risk. If I were in any sports, and you made that policy official, I would definitely try to study harder, be more focused, and do better in my classes that I don't do as well as others in. Thank\n",
+ "\n",
+ "Schools in some places require students to complete summer long assignments in order for them to continue their studies over break. Summer assignments are often papers, book reports, or various other works created with the intent of learning outside the classroom. Some schools allow these assignments have the chance to be designed either by students or by teachers\n",
+ "Schools in some places require students to complete summer long assignments in order for them to continue their studies over break. Summer assignments are often papers, book reports, or various other works created with the intent of learning outside the classroom. Some schools allow these assignments have the chance to be designed either by students or by teachers.\n",
+ "\n",
+ "Giving them a chance to explore the works of design enriches their learning experience. The student body is more than capable of teaching each other as peers in ways teachers are unable to connect. Students should be the ones creating these summer assignments because they are more empathetic and will benefit from the practical skills learned through teaching others. \n",
+ "Giving them a chance to explore the works of design enriches their learning experience. The student body is more than capable of teaching each other as peers in ways teachers are unable to connect. Students should be the ones creating these summer assignments because they are more empathetic and will benefit from the practical skills learned through teaching others.\n",
+ "\n",
+ "I believe you should use policy 1 allow students to bring there cell phones to school.\n",
+ "\n",
+ "As long as they use them at lunch or break or any free time during the school day and have them turned off before they come to class. \n",
+ "I believe you should use policy 1 allow students to bring there cell phones to school. As long as they use them at lunch or break or any free time during the school day and have them turned off before they come to class.\n",
+ "\n",
+ " If you had policy 1\n",
+ "\n",
+ "then they could call their parents and get taken home to be cared for i know there are plenty of times when i didn't feel well and i wanted to go home. I could have used my phone but i couldn't because we are not allowed to use our phones at school only after school.\n",
+ "\n",
+ "There is a possibility that some students wouldn't follow the rules about putting there phones away in class and teachers can take them away and they can get them at the end of class or the end of the day if this becomes a problem you can always consider changing the rules to policy 2 don't allow students to have there cell phones at all at school. \n",
+ "If you had policy 1 then they could call their parents and get taken home to be cared for i know there are plenty of times when i didn't feel well and i wanted to go home. I could have used my phone but i couldn't because we are not allowed to use our phones at school only after school. There is a possibility that some students wouldn't follow the rules about putting there phones away in class and teachers can take them away and they can get them at the end of class or the end of the day if this becomes a problem you can always consider changing the rules to policy 2 don't allow students to have there cell phones at all at school.\n",
+ "\n",
+ "people at school also need to use there phones after school to have their parents pick them up or if there parents cant they wouldn't have any way of reaching them\n",
+ "people at school also need to use there phones after school to have their parents pick them up or if there parents cant they wouldn't have any way of reaching them.\n",
+ "\n",
+ ", adding the last two reasons together makes for another very important factor in all of this: everyone's general health. Both stress and high concentrations of smog can do a toll on one's body and the reduction of cars, once again, helps to reduce this terrible down-sides to the easy transportation option \n",
+ "adding the last two reasons together makes for another very important factor in all of this: everyone's general health. Both stress and high concentrations of smog can do a toll on one's body and the reduction of cars, once again, helps to reduce this terrible down-sides to the easy transportation option. If\n",
+ "\n",
+ "To just put it bluntly, the Electoral College is a huge waste of time and pointless processing. It is completely unfair to the voters, who most of the time feel as if they're voicing their opinion for no reason since it doesn't even get taken into consideration\n",
+ "To just put it bluntly, the Electoral College is a huge waste of time and pointless processing. It is completely unfair to the voters, who most of the time feel as if they're voicing their opinion for no reason since it doesn't even get taken into consideration.\n",
+ "\n",
+ "When more and more technology is put into a car, more problems can arise \n",
+ "When more and more technology is put into a car, more problems can arise.\n",
+ "\n",
+ " lots of people love going out to eat. Many people try new places to eat at some may hear good or bad opinion about a place but until you try it you can't tell your self is great or bad. Sometimes Google lets you see other people opinion the other people leaved about a certain place. to be honest my dad does this sometimes he goes online and search is up the place and then looks at the rate the place have and the comment as a result of the deadly corovirus (Covid-19) lots of people have decide not to travel. But theres certain people that just love to travel many of the occasion people travel to a place because they hear good thing about a place. for example thats how me and my family hear of Lego land my aunt tell my dad that this place was so amazing me and my family we have been a big fan o Lego and we really dint know this place existed.\n",
+ "lots of people love going out to eat. Many people try new places to eat at some may hear good or bad opinion about a place but until you try it you can't tell your self is great or bad. Sometimes Google lets you see other people opinion the other people leaved about a certain place. to be honest my dad does this sometimes he goes online and search is up the place and then looks at the rate the place have and the comment as a result of the deadly corovirus (Covid-19) lots of people have decide not to travel. But theres certain people that just love to travel many of the occasion people travel to a place because they hear good thing about a place. for example thats how me and my family hear of Lego land my aunt tell my dad that this place was so amazing me and my family we have been a big fan o Lego and we really dint know this place existed.\n",
+ "\n",
+ "multiple people can help a person make a better choice \n",
+ "multiple people can help a person make a better choice,\n",
+ "\n",
+ "it can help a person change their out look on the matter\n",
+ "it can help a person change their out look on the matter,\n",
+ "\n",
+ "The elctoral votes are utterly useless or unimportant,though. If there is ever happens to be a tie between the two candidates the electoral votes can help break it. It'd be less of a hassle to have the florida residents vote,directly, on someone besides the president. Less worring about if theyre going to win or not \n",
+ "The elctoral votes are utterly useless or unimportant,though. If there is ever happens to be a tie between the two candidates the electoral votes can help break it. It'd be less of a hassle to have the florida residents vote,directly, on someone besides the president. Less worring about if theyre going to win or not.\n",
+ "\n",
+ "The process begins when the computer puts together a 3-D digital model. There are 44 major muscles in your face that the computer has to detect. Eckman classified six emotions happiness, surprise, anger, disgust, fear, and sadness. He then \" associated each with characteristic movements of facial muscles.\"\n",
+ "\n",
+ "For example the frontalis pars lateralis muscle is above your eye which shows your surpried when it is raised.\n",
+ "\n",
+ "Us humans alone can identify facial expressions on peoples faces. If you look at a friend you can tell how they are feeling at that moment. Da Vinci studied human anatomy to help paint the facial muscles percisely on the Mona Lisa painting. Dr. Huangs has new computer software it stores similar info as electronic code. He thinks there could be a different type of code to the painting.\n",
+ "\n",
+ "The process begins when the computer puts together a 3-D digital model. There are 44 major muscles in your face that the computer has to detect. Eckman classified six emotions happiness, surprise, anger, disgust, fear, and sadness. He then \" associated each with characteristic movements of facial muscles.\" For example the frontalis pars lateralis muscle is above your eye which shows your surpried when it is raised. Us humans alone can identify facial expressions on peoples faces. If you look at a friend you can tell how they are feeling at that moment. Da Vinci studied human anatomy to help paint the facial muscles percisely on the Mona Lisa painting. Dr. Huangs has new computer software it stores similar info as electronic code. He thinks there could be a different type of code to the painting.\n",
+ "\n",
+ "When facial facial expressions a made its about muscular action units. The school computer shouldn't have to know how you are feeling. What kind of problem is that supposed to solve \n",
+ "When facial facial expressions a made its about muscular action units. The school computer shouldn't have to know how you are feeling. What kind of problem is that supposed to solve?\n",
+ "\n",
+ "the fact that its really easy for someone else to get into your phone or tablet through face reconition \n",
+ "the fact that its really easy for someone else to get into your phone or tablet through face reconition.\n",
+ "\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'df' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [7]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../data/kaggle/feedback-prize-2021\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m processor \u001b[38;5;241m=\u001b[39m \u001b[43mPersuadeProcessor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpreprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+ "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36mPersuadeProcessor._preprocess\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;66;03m#print(row.predictionstring[0], row.predictionstring[-1])\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \n\u001b[1;32m 48\u001b[0m \u001b[38;5;66;03m# print(row.discourse_text, )\u001b[39;00m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;66;03m# break\u001b[39;00m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;66;03m#from pandas.testing import assert_frame_equal\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;66;03m#assert_frame_equal(df_texts, df_texts_2)\u001b[39;00m\n\u001b[0;32m---> 52\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataframe \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpreprocessed\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "path = '../../data/kaggle/feedback-prize-2021'\n",
+ "processor = PersuadeProcessor(path).preprocess()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "75cf56e4",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "0c5d4947",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "processor = Processor(os.path.join(project_path, f'data/{strat_label}'))\n",
+ "processor = processor.from_json()\n",
+ "df_total = processor.dataframe\n",
+ "\n",
+ "df_dict = processor.get_tts(test_size=0.3, val_size=0.1)\n",
+ "df_train = df_dict.get('train')[['text', 'labels']]\n",
+ "df_test = df_dict.get('test')[['text', 'labels']]\n",
+ "df_val = df_dict.get('val')[['text', 'labels']]\n",
+ "\n",
+ "#df_train = df_total[['text', 'labels']].head(10) \n",
+ "#df_test = df_total[['text', 'labels']].tail(201)\n",
+ "\n",
+ "\n",
+ "#assert_frame_equal(df_total[['text', 'labels']], pd.concat([df_train, df_test]))\n",
+ "\n",
+ "# todo this changes NOTE FIXED BT STRATEGY!!\n",
+ "# todo this needs to get updated as well.....\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "ccf36a48",
+ "metadata": {
+ "id": "ccf36a48"
+ },
+ "outputs": [],
+ "source": [
+ "train_set = ArgumentMiningDataset(df_label_map, df_train, tokenizer, max_length, strategy)\n",
+ "test_set = ArgumentMiningDataset(df_label_map, df_test, tokenizer, max_length, strategy, is_train=False)\n",
+ "\n",
+ "train_loader = DataLoader(train_set, batch_size=batch_size)\n",
+ "test_loader = DataLoader(test_set, batch_size=batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "d2aa673e",
+ "metadata": {
+ "id": "d2aa673e",
+ "outputId": "ad9cf94d-5991-4a83-8443-af62a83b3a68",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 852
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Model pushed to device: cuda\n",
+ "EPOCH 1 STARTED\n",
+ "---------------\n",
+ "GPU Utilisation at batch 1 after data loading: 2739\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/transformers/models/big_bird/modeling_big_bird.py:978: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
+ " * num_indices_to_pick_from\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "GPU Utilisation at batch 1 after training: 5763\n",
+ "Batch 1 complete. Time taken: load(0.0248), train(2.19), total(2.22). \n",
+ "GPU Utilisation at batch 2 after data loading: 2797\n",
+ "GPU Utilisation at batch 2 after training: 7251\n",
+ "Batch 2 complete. Time taken: load(0.0156), train(2.04), total(2.05). \n",
+ "GPU Utilisation at batch 3 after data loading: 2797\n",
+ "GPU Utilisation at batch 3 after training: 7251\n",
+ "Batch 3 complete. Time taken: load(0.0133), train(2.03), total(2.04). \n",
+ "GPU Utilisation at batch 4 after data loading: 2797\n",
+ "GPU Utilisation at batch 4 after training: 7251\n",
+ "Batch 4 complete. Time taken: load(0.0162), train(2.04), total(2.06). \n",
+ "GPU Utilisation at batch 5 after data loading: 2797\n",
+ "GPU Utilisation at batch 5 after training: 7251\n",
+ "Batch 5 complete. Time taken: load(0.0172), train(2.03), total(2.04). \n",
+ "GPU Utilisation at batch 6 after data loading: 2797\n",
+ "GPU Utilisation at batch 6 after training: 7251\n",
+ "Batch 6 complete. Time taken: load(0.024), train(2.02), total(2.05). \n",
+ "GPU Utilisation at batch 7 after data loading: 2797\n"
+ ]
+ },
+ {
+ "output_type": "error",
+ "ename": "KeyboardInterrupt",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'attention_mask'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 29\u001b[0m )\n\u001b[1;32m 30\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mDEVICE\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'cpu'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1103\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/big_bird/modeling_big_bird.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 2878\u001b[0m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2879\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2880\u001b[0;31m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2881\u001b[0m )\n\u001b[1;32m 2882\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1103\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/big_bird/modeling_big_bird.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 2150\u001b[0m \u001b[0mto_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mto_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2151\u001b[0m \u001b[0mblocked_encoder_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblocked_encoder_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2152\u001b[0;31m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2153\u001b[0m )\n\u001b[1;32m 2154\u001b[0m \u001b[0msequence_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1103\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/big_bird/modeling_big_bird.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, band_mask, from_mask, to_mask, blocked_encoder_mask, return_dict)\u001b[0m\n\u001b[1;32m 1639\u001b[0m \u001b[0mblocked_encoder_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1640\u001b[0m \u001b[0mpast_key_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1641\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1642\u001b[0m )\n\u001b[1;32m 1643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1103\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/big_bird/modeling_big_bird.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, band_mask, from_mask, to_mask, blocked_encoder_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m 1492\u001b[0m \u001b[0mto_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mto_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1493\u001b[0m \u001b[0mfrom_blocked_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblocked_encoder_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1494\u001b[0;31m \u001b[0mto_blocked_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblocked_encoder_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1495\u001b[0m )\n\u001b[1;32m 1496\u001b[0m \u001b[0mattention_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself_attention_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1103\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/big_bird/modeling_big_bird.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask)\u001b[0m\n\u001b[1;32m 1395\u001b[0m ), \"BigBird cannot be used as a decoder when config.attention_type != 'original_full'\"\n\u001b[1;32m 1396\u001b[0m self_outputs = self.self(\n\u001b[0;32m-> 1397\u001b[0;31m \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mband_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_blocked_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_blocked_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1398\u001b[0m )\n\u001b[1;32m 1399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1103\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/big_bird/modeling_big_bird.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, output_attentions)\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0mplan_from_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 489\u001b[0m \u001b[0mplan_num_rand_blocks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 491\u001b[0m )\n\u001b[1;32m 492\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/big_bird/modeling_big_bird.py\u001b[0m in \u001b[0;36mbigbird_block_sparse_attention\u001b[0;34m(self, query_layer, key_layer, value_layer, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, n_heads, n_rand_blocks, attention_head_size, from_block_size, to_block_size, batch_size, from_seq_len, to_seq_len, seed, plan_from_length, plan_num_rand_blocks, output_attentions)\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 592\u001b[0m \u001b[0mrand_attn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrand_attn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m \u001b[0mrand_attn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrand_attn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquery_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 594\u001b[0m \u001b[0mrand_attn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[0mrand_attn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrand_attn\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "if not os.path.exists('models'):\n",
+ " os.makedirs('models')\n",
+ " print('models directory created!')\n",
+ "model.to(DEVICE)\n",
+ "print(f'Model pushed to device: {DEVICE}')\n",
+ "for epoch in range(epochs):\n",
+ " model.train()\n",
+ " start_epoch_message = f'EPOCH {epoch + 1} STARTED'\n",
+ " print(start_epoch_message)\n",
+ " print(f'{\"-\" * len(start_epoch_message)}')\n",
+ " start_epoch = time.time()\n",
+ "\n",
+ " start_load = time.time()\n",
+ " training_loss = 0\n",
+ " for i, (inputs, targets) in enumerate(train_loader):\n",
+ " start_train = time.time()\n",
+ " inputs = move_to_device(inputs, DEVICE)\n",
+ " targets = move_to_device(targets, DEVICE)\n",
+ " if DEVICE != 'cpu':\n",
+ " print(f'GPU Utilisation at batch {i+1} after data loading: {get_gpu_utilization()}')\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " loss, outputs = model(\n",
+ " labels=targets,\n",
+ " input_ids=inputs['input_ids'],\n",
+ " attention_mask=inputs['attention_mask'],\n",
+ " return_dict=False\n",
+ " )\n",
+ " if DEVICE != 'cpu':\n",
+ " print(f'GPU Utilisation at batch {i+1} after training: {get_gpu_utilization()}')\n",
+ "\n",
+ "\n",
+ " training_loss += loss.item()\n",
+ "\n",
+ " # backward pass\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " del targets, inputs, loss, outputs\n",
+ " gc.collect()\n",
+ " torch.cuda.empty_cache()\n",
+ "\n",
+ " end_train = time.time()\n",
+ "\n",
+ " if verbose > 1:\n",
+ " print(\n",
+ " f'Batch {i + 1} complete. Time taken: load({start_train - start_load:.3g}), '\n",
+ " f'train({end_train - start_train:.3g}), total({end_train - start_load:.3g}). '\n",
+ " )\n",
+ " start_load = time.time()\n",
+ "\n",
+ " print_message = f'Epoch {epoch + 1}/{epochs} complete. ' \\\n",
+ " f'Time taken: {start_load - start_epoch:.3g}. ' \\\n",
+ " f'Loss: {training_loss/(i+1): .3g}'\n",
+ "\n",
+ " if verbose:\n",
+ " print(f'{\"-\" * len(print_message)}')\n",
+ " print(print_message)\n",
+ " print(f'{\"-\" * len(print_message)}')\n",
+ "\n",
+ " if epoch % save_freq == 0:\n",
+ " encoded_model_name = encode_model_name(model_name, epoch+1)\n",
+ " save_path = f'models/{encoded_model_name}'\n",
+ " model.save_pretrained(save_path)\n",
+ " print(f'Model saved at epoch {epoch+1} at: {save_path}')\n",
+ "\n",
+ "encoded_model_name = encode_model_name(model_name, 'final')\n",
+ "save_path = f'models/{encoded_model_name}'\n",
+ "model.save_pretrained(save_path)\n",
+ "print(f'Model saved at epoch {epoch + 1} at: {save_path}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12afdc17",
+ "metadata": {
+ "id": "12afdc17"
+ },
+ "outputs": [],
+ "source": [
+ "# load trained model\n",
+ "path = ''\n",
+ "trained_model = AutoModelForTokenClassification.from_pretrained(path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df_metrics, df_scores = inference(trained_model, test_loader)"
+ ],
+ "metadata": {
+ "id": "VQcpzbhuPPwu"
+ },
+ "id": "VQcpzbhuPPwu",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd86b6d3",
+ "metadata": {
+ "id": "bd86b6d3"
+ },
+ "outputs": [],
+ "source": [
+ "df_scores"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "KSc1Z5UFQJgU"
+ },
+ "id": "KSc1Z5UFQJgU",
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "WoOfJ70gQeAk"
+ },
+ "id": "WoOfJ70gQeAk",
+ "execution_count": null,
+ "outputs": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ },
+ "colab": {
+ "name": "End-to-end_no_GPU.ipynb",
+ "provenance": []
+ },
+ "accelerator": "GPU"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/experiments/yousef/End-to-end_no_GPU.ipynb b/experiments/yousef/End-to-end_no_GPU.ipynb
new file mode 100644
index 0000000..b63cc88
--- /dev/null
+++ b/experiments/yousef/End-to-end_no_GPU.ipynb
@@ -0,0 +1,303 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1c2ec16d",
+ "metadata": {},
+ "source": [
+ "# End-to-end\n",
+ "\n",
+ "This notebook should form the core skeleton of the 'run' function"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c1302e4f",
+ "metadata": {},
+ "source": [
+ "### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "7d8ed729",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- public imports\n",
+ "\n",
+ "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
+ "import pandas as pd\n",
+ "from torch.utils.data import DataLoader\n",
+ "import torch\n",
+ "from pandas.testing import assert_frame_equal\n",
+ "import time\n",
+ "\n",
+ "# -- private imports\n",
+ "from colabtools.utils import move_to_device\n",
+ "\n",
+ "# -- dev imports\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "from argminer.data import ArgumentMiningDataset, TUDarmstadtProcessor\n",
+ "from argminer.evaluation import inference"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "59272e27",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# constants (these will be abstracted away by inputs that you give to run)\n",
+ "model_name = 'google/bigbird-roberta-base'\n",
+ "max_length = 1024\n",
+ "epochs = 1\n",
+ "batch_size = 2\n",
+ "strategy = 'standard_bio'\n",
+ "strat_name = strategy.split('_')[1]\n",
+ "DEVICE = 'cpu'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f5b81b26",
+ "metadata": {},
+ "source": [
+ "### Tokenizer, Model and Optimizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "882197f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n",
+ "Some weights of the model checkpoint at google/bigbird-roberta-base were not used when initializing BigBirdForTokenClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias']\n",
+ "- This IS expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of BigBirdForTokenClassification were not initialized from the model checkpoint at google/bigbird-roberta-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained(model_name, add_prefix_space=True)\n",
+ "model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=15) \n",
+ "# TODO force_download\n",
+ "# TODO add option for optimizer\n",
+ "optimizer = torch.optim.Adam(params=model.parameters())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cb3e225a",
+ "metadata": {},
+ "source": [
+ "### Dataset \n",
+ "Note this will change as the Processor develops. On the cluster you will need to use different options"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "ccc67877",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "murdering criminals is therefore immoral and hard to accept\n",
+ "\n",
+ "\"murdering\" criminals is therefore immoral and hard to accept\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "Click is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\n",
+ "\"Click\" is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "Blood diamond, an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\n",
+ "\"Blood diamond\", an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "rush hours are usually not the direct consequence of drivers\n",
+ "\n",
+ "\"rush hours\" are usually not the direct consequence of drivers\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "processor = TUDarmstadtProcessor('../../data/UCL/dataset2/ArgumentAnnotatedEssays-2.0/brat-project-final')\n",
+ "processor = processor.preprocess().process(strat_name).postprocess()\n",
+ "df_total = processor.dataframe\n",
+ "df_train = df_total[['text', 'labels']].head(201) \n",
+ "df_test = df_total[['text', 'labels']].tail(201)\n",
+ "\n",
+ "assert_frame_equal(df_total[['text', 'labels']], pd.concat([df_train, df_test]))\n",
+ "\n",
+ "# todo this changes NOTE FIXED BT STRATEGY!!\n",
+ "df_label_map = pd.DataFrame({\n",
+ " 'label_id':[0,1,2,3,4,5,6],\n",
+ " 'label':['O', 'B-MajorClaim', 'I-MajorClaim', 'B-Claim', 'I-Claim', 'B-Premise', 'I-Premise']\n",
+ "})\n",
+ "\n",
+ "assert set(df_train.labels.values[0]) == set(df_label_map.label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "ccf36a48",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_set = ArgumentMiningDataset(df_label_map, df_train, tokenizer, max_length, strategy)\n",
+ "test_set = ArgumentMiningDataset(df_label_map, df_test, tokenizer, max_length, strategy, is_train=False)\n",
+ "\n",
+ "train_loader = DataLoader(train_set, batch_size=batch_size)\n",
+ "test_loader = DataLoader(test_set, batch_size=batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d2aa673e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.to(DEVICE)\n",
+ "print(f'Model pushed to device: {DEVICE}')\n",
+ "for epoch in range(epochs):\n",
+ " model.train()\n",
+ " start_epoch_message = f'EPOCH {epoch + 1} STARTED'\n",
+ " print(start_epoch_message)\n",
+ " print(f'{\"-\" * len(start_epoch_message)}')\n",
+ " start_epoch = time.time()\n",
+ "\n",
+ " start_load = time.time()\n",
+ " training_loss = 0\n",
+ " for i, (inputs, targets) in enumerate(train_loader):\n",
+ " start_train = time.time()\n",
+ " inputs = move_to_device(inputs, DEVICE)\n",
+ " targets = move_to_device(targets, DEVICE)\n",
+ " if DEVICE != 'cpu':\n",
+ " print(f'GPU Utilisation at batch {i+1} after data loading: {get_gpu_utilization()}')\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " loss, outputs = model(\n",
+ " labels=targets,\n",
+ " input_ids=inputs['input_ids'],\n",
+ " attention_mask=inputs['attention_mask'],\n",
+ " return_dict=False\n",
+ " )\n",
+ " if DEVICE != 'cpu':\n",
+ " print(f'GPU Utilisation at batch {i+1} after training: {get_gpu_utilization()}')\n",
+ "\n",
+ "\n",
+ " training_loss += loss.item()\n",
+ "\n",
+ " # backward pass\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " del targets, inputs, loss, outputs\n",
+ " gc.collect()\n",
+ " torch.cuda.empty_cache()\n",
+ "\n",
+ " end_train = time.time()\n",
+ "\n",
+ " if verbose > 1:\n",
+ " print(\n",
+ " f'Batch {i + 1} complete. Time taken: load({start_train - start_load:.3g}), '\n",
+ " f'train({end_train - start_train:.3g}), total({end_train - start_load:.3g}). '\n",
+ " )\n",
+ " start_load = time.time()\n",
+ "\n",
+ " print_message = f'Epoch {epoch + 1}/{epochs} complete. ' \\\n",
+ " f'Time taken: {start_load - start_epoch:.3g}. ' \\\n",
+ " f'Loss: {training_loss/(i+1): .3g}'\n",
+ "\n",
+ " if verbose:\n",
+ " print(f'{\"-\" * len(print_message)}')\n",
+ " print(print_message)\n",
+ " print(f'{\"-\" * len(print_message)}')\n",
+ "\n",
+ " if epoch % save_freq == 0:\n",
+ " encoded_model_name = encode_model_name(model_name, epoch+1)\n",
+ " save_path = f'models/{encoded_model_name}.pt'\n",
+ " torch.save(model.state_dict(), save_path)\n",
+ " print(f'Model saved at epoch {epoch+1} at: {save_path}')\n",
+ "\n",
+ "encoded_model_name = encode_model_name(model_name, 'final')\n",
+ "save_path = f'models/{encoded_model_name}.pt'\n",
+ "torch.save(model.state_dict(), save_path)\n",
+ "print(f'Model saved at epoch {epoch + 1} at: {save_path}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12afdc17",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd86b6d3",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/Final_Inference.ipynb b/experiments/yousef/Final_Inference.ipynb
new file mode 100644
index 0000000..f2f889d
--- /dev/null
+++ b/experiments/yousef/Final_Inference.ipynb
@@ -0,0 +1,1395 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "c88b4628",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
+ "import warnings\n",
+ "from argminer.data import ArgumentMiningDataset, PersuadeProcessor, TUDarmstadtProcessor\n",
+ "from argminer.utils import decode_model_name\n",
+ "from argminer.evaluation import inference\n",
+ "from argminer.config import LABELS_MAP_DICT\n",
+ "import time\n",
+ "from torch.utils.data import DataLoader\n",
+ "from mlutils.torchtools.metrics import FScore"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a3ce3f0c",
+ "metadata": {},
+ "source": [
+ "## PATHS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "c74cf0fa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# need to have a relative load of the models\n",
+ "import os\n",
+ "os.chdir('/Users/yousefnami/Desktop')\n",
+ "\n",
+ "# path to models\n",
+ "JOB_DIR = 'tmpdir/job'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "fec9a578",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# TODO double check config MAP!\n",
+ "CONFIG_MAP = {\n",
+ " '822594.undefined': dict(\n",
+ " processor=PersuadeProcessor,\n",
+ " strat='bieo'\n",
+ " ),\n",
+ " '822595.undefined': dict(\n",
+ " processor=PersuadeProcessor,\n",
+ " strat='bio'\n",
+ " ),\n",
+ " '822596.undefined': dict(\n",
+ " processor=PersuadeProcessor,\n",
+ " strat='io'\n",
+ " ),\n",
+ " '820966.undefined': dict(\n",
+ " processor=PersuadeProcessor,\n",
+ " strat='bieo'\n",
+ " ),\n",
+ " '820965.undefined': dict(\n",
+ " processor=PersuadeProcessor,\n",
+ " strat='bio' \n",
+ " ),\n",
+ " '820962.undefined': dict(\n",
+ " processor=PersuadeProcessor,\n",
+ " strat='io'\n",
+ " ),\n",
+ " '820985.undefined': dict(\n",
+ " processor=TUDarmstadtProcessor,\n",
+ " strat='bieo'\n",
+ " ),\n",
+ " '820986.undefined': dict(\n",
+ " processor=TUDarmstadtProcessor,\n",
+ " strat='bio'\n",
+ " ),\n",
+ " '820987.undefined': dict(\n",
+ " processor=TUDarmstadtProcessor,\n",
+ " strat='io'\n",
+ " )\n",
+ "}\n",
+ "\n",
+ "MAX_LENGTH_DICT = {\n",
+ " 'google/bigbird-roberta-base': 1024,\n",
+ " 'roberta-base': 512\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "f9512f59",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "RESULTS = {}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "448f6abf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#metrics = [FScore(average='macro')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "5b8fec1c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def _get_loader(df_label_map, df_test, batch_size):\n",
+ " testset = ArgumentMiningDataset(\n",
+ " df_label_map, df_test, tokenizer, max_length, f'standard_{strategy}', is_train=False\n",
+ " )\n",
+ " testloader = DataLoader(testset, batch_size=batch_size)\n",
+ " return testloader\n",
+ "\n",
+ "def _get_data(path, Processor, strategy, batch_size=32, limit=None):\n",
+ " processor = Processor(path).from_json()\n",
+ " if 'test' in path:\n",
+ " df_test = processor.dataframe[['text', 'labels']]\n",
+ " # TODO here might need to do a label_map\n",
+ " else:\n",
+ " df_dict = processor.get_tts(test_size=0.3)\n",
+ " df_test = df_dict['test'][['text', 'labels']]\n",
+ " if limit is not None:\n",
+ " warnings.warn('LOADING LIMITED DATA')\n",
+ " df_test = df_test.head(limit)\n",
+ " \n",
+ " df_label_map = LABELS_MAP_DICT[processor.__class__.__name__.split('Processor')[0]][strategy]\n",
+ " return df_test, df_label_map\n",
+ "\n",
+ "def _get_core_data(path, Processor, strategy, batch_size=32, limit=None):\n",
+ " #processor = Processor(path).from_json()\n",
+ " #if 'test' in path:\n",
+ " # print( processor.dataframe.head())\n",
+ " # df_test = processor.dataframe[['text', 'labels']]\n",
+ " #else:\n",
+ " # df_dict = processor.get_tts(test_size=0.3)\n",
+ " # df_test = df_dict['test'][['text', 'labels']]\n",
+ " #if limit is not None:\n",
+ " # warnings.warn('LOADING LIMITED DATA')\n",
+ " # df_test = df_test.head(limit)\n",
+ " \n",
+ " #df_label_map = LABELS_MAP_DICT[processor.__class__.__name__.split('Processor')[0]][strategy]\n",
+ " \n",
+ " #testset = ArgumentMiningDataset(\n",
+ " # df_label_map, df_test, tokenizer, max_length, f'standard_{strategy}', is_train=False\n",
+ " #)\n",
+ " #testloader = DataLoader(testset, batch_size=batch_size)\n",
+ " df_test, df_label_map = _get_data(path, Processor, strategy, batch_size, limit)\n",
+ " testloader = _get_loader(df_label_map, df_test, batch_size)\n",
+ " \n",
+ " return testloader\n",
+ "\n",
+ "\n",
+ "def _get_other_data(path, Processor, strategy, batch_size=32, limit=None):\n",
+ " if Processor == PersuadeProcessor:\n",
+ " # we are using a Persuade Model but would like to test on TUDarmstadt\n",
+ " df_test, _ = _get_data(path, TUDarmstadtProcessor, strategy, batch_size, limit)\n",
+ " # HARD RESET DF-LABEL-MAP\n",
+ " df_label_map = LABELS_MAP_DICT['Persuade'][strategy]\n",
+ " \n",
+ " label_map_dict = {\n",
+ " 'Claim': 'Claim',\n",
+ " 'MajorClaim': 'Position',\n",
+ " 'Premise': 'O',\n",
+ " 'O': 'O'\n",
+ " }\n",
+ " \n",
+ " else:\n",
+ " df_test, _ = _get_data(path, PersuadeProcessor, strategy, batch_size, limit)\n",
+ " df_label_map = LABELS_MAP_DICT['TUDarmstadt'][strategy]\n",
+ "\n",
+ "\n",
+ " label_map_dict = {\n",
+ " 'Lead': 'O',\n",
+ " 'Rebuttal': 'O',\n",
+ " 'Concluding Statement': 'O',\n",
+ " 'Position': 'MajorClaim',\n",
+ " 'Evidence': 'O',\n",
+ " 'Claim': 'Claim',\n",
+ " 'Counterclaim': 'O',\n",
+ " 'O': 'O'\n",
+ " }\n",
+ " df_test.labels = df_test.labels.apply(\n",
+ " lambda x: [\n",
+ " 'O' if label_map_dict[text.split('-')[-1]] =='O' else text.replace(\n",
+ " text.split('-')[-1],\n",
+ " label_map_dict[text.split('-')[-1]]\n",
+ " ) for text in x]\n",
+ " )\n",
+ "\n",
+ "\n",
+ " testloader = _get_loader(df_label_map, df_test, batch_size)\n",
+ " return testloader\n",
+ "\n",
+ "def _get_scores_agg(df):\n",
+ " df = df.groupby('class').sum()\n",
+ " df['f1'] = df.tp / (df.tp + 1/2*(df.fp + df.fn))\n",
+ " macro_f1 = df['f1'].mean()\n",
+ " return macro_f1, df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "14354111",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "BATCH_SIZE = 128\n",
+ "LIMIT = None"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "b331ab21",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "RUNNING FOR MODEL 0: roberta-base at PATH: tmpdir/job/822594.undefined/models/cm9iZXJ0YS1iYXNlX2ZpbmFs\n",
+ "PARAMETERS: bieo. \n",
+ "=================================================================================\n",
+ "Loaded and data loaded. Time: 8.91\n",
+ "Prediction time: 468\n",
+ "tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7])\n",
+ "Agg to word time: 2.21\n",
+ "Get predstring time: 0.226\n",
+ "Evaluate time: 1.77\n",
+ "Batch 1 complete.\n",
+ "Prediction time: 555\n",
+ "tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7])\n",
+ "Agg to word time: 1.51\n",
+ "Get predstring time: 0.163\n",
+ "Evaluate time: 0.411\n",
+ "Batch 2 complete.\n",
+ "Prediction time: 420\n",
+ "tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7])\n",
+ "Agg to word time: 1.66\n",
+ "Get predstring time: 0.153\n",
+ "Evaluate time: 0.475\n",
+ "Batch 3 complete.\n",
+ "Prediction time: 433\n",
+ "tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7])\n",
+ "Agg to word time: 1.31\n",
+ "Get predstring time: 0.12\n",
+ "Evaluate time: 0.406\n",
+ "Batch 4 complete.\n",
+ "Prediction time: 315\n",
+ "tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7])\n",
+ "Agg to word time: 6.5\n",
+ "Get predstring time: 0.069\n",
+ "Evaluate time: 0.349\n",
+ "Batch 5 complete.\n",
+ "Prediction time: 406\n",
+ "tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7])\n",
+ "Agg to word time: 1.52\n",
+ "Get predstring time: 0.211\n",
+ "Evaluate time: 0.519\n",
+ "Batch 6 complete.\n",
+ "Prediction time: 606\n",
+ "tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7])\n",
+ "Agg to word time: 2.9\n",
+ "Get predstring time: 0.431\n",
+ "Evaluate time: 0.848\n",
+ "Batch 7 complete.\n",
+ "Prediction time: 574\n",
+ "tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7])\n",
+ "Agg to word time: 1.69\n",
+ "Get predstring time: 0.231\n",
+ "Evaluate time: 0.527\n",
+ "Batch 8 complete.\n",
+ "Prediction time: 585\n",
+ "tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7])\n",
+ "Agg to word time: 2.05\n",
+ "Get predstring time: 0.192\n",
+ "Evaluate time: 0.66\n",
+ "Batch 9 complete.\n",
+ "Prediction time: 538\n",
+ "tensor([0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7])\n",
+ "Agg to word time: 2.85\n",
+ "Get predstring time: 0.157\n",
+ "Evaluate time: 0.739\n",
+ "Batch 10 complete.\n"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [43]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 50\u001b[0m SELF \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 51\u001b[0m s \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m---> 52\u001b[0m df_metrics, df_scores \u001b[38;5;241m=\u001b[39m \u001b[43minference\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrained_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtestloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 53\u001b[0m macro_f1, df_scores_agg \u001b[38;5;241m=\u001b[39m _get_scores_agg(df_scores)\n\u001b[1;32m 54\u001b[0m RESULTS[job][SELF][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcore\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmacro_f1\u001b[39m\u001b[38;5;124m'\u001b[39m: macro_f1, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscores\u001b[39m\u001b[38;5;124m'\u001b[39m: df_scores_agg}\n",
+ "File \u001b[0;32m~/Desktop/Main/0.Education/2.UCL/Courses/NLP/argument-mining/argminer/evaluation.py:253\u001b[0m, in \u001b[0;36minference\u001b[0;34m(model, testloader, metrics)\u001b[0m\n\u001b[1;32m 250\u001b[0m targets \u001b[38;5;241m=\u001b[39m move_to_device(targets, DEVICE)\n\u001b[1;32m 252\u001b[0m s \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;66;03m# TODO add verbose statement\u001b[39;00m\n\u001b[0;32m--> 253\u001b[0m loss, outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 254\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mattention_mask\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[1;32m 258\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrediction time: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m s\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3g\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 261\u001b[0m word_ids \u001b[38;5;241m=\u001b[39m inputs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mword_ids\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py:1397\u001b[0m, in \u001b[0;36mRobertaForTokenClassification.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1391\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1392\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\u001b[39;00m\n\u001b[1;32m 1393\u001b[0m \u001b[38;5;124;03m Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.\u001b[39;00m\n\u001b[1;32m 1394\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1395\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 1397\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mroberta\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1398\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1399\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1400\u001b[0m \u001b[43m \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1401\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1402\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1403\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1404\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1405\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1406\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1407\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1409\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1411\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout(sequence_output)\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py:850\u001b[0m, in \u001b[0;36mRobertaModel.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 841\u001b[0m head_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_head_mask(head_mask, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mnum_hidden_layers)\n\u001b[1;32m 843\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(\n\u001b[1;32m 844\u001b[0m input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m 845\u001b[0m position_ids\u001b[38;5;241m=\u001b[39mposition_ids,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 848\u001b[0m past_key_values_length\u001b[38;5;241m=\u001b[39mpast_key_values_length,\n\u001b[1;32m 849\u001b[0m )\n\u001b[0;32m--> 850\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 851\u001b[0m \u001b[43m \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 852\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 853\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 854\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 855\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_extended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 856\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 857\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 858\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 862\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 863\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(sequence_output) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py:526\u001b[0m, in \u001b[0;36mRobertaEncoder.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 517\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mcheckpoint\u001b[38;5;241m.\u001b[39mcheckpoint(\n\u001b[1;32m 518\u001b[0m create_custom_forward(layer_module),\n\u001b[1;32m 519\u001b[0m hidden_states,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 523\u001b[0m encoder_attention_mask,\n\u001b[1;32m 524\u001b[0m )\n\u001b[1;32m 525\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 526\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 527\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 528\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 529\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 530\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 531\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 532\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 533\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 534\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 536\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 537\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache:\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py:453\u001b[0m, in \u001b[0;36mRobertaLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m 450\u001b[0m cross_attn_present_key_value \u001b[38;5;241m=\u001b[39m cross_attention_outputs[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 451\u001b[0m present_key_value \u001b[38;5;241m=\u001b[39m present_key_value \u001b[38;5;241m+\u001b[39m cross_attn_present_key_value\n\u001b[0;32m--> 453\u001b[0m layer_output \u001b[38;5;241m=\u001b[39m \u001b[43mapply_chunking_to_forward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeed_forward_chunk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunk_size_feed_forward\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mseq_len_dim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattention_output\u001b[49m\n\u001b[1;32m 455\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 456\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (layer_output,) \u001b[38;5;241m+\u001b[39m outputs\n\u001b[1;32m 458\u001b[0m \u001b[38;5;66;03m# if decoder, return the attn key/values as the last output\u001b[39;00m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/transformers/modeling_utils.py:2438\u001b[0m, in \u001b[0;36mapply_chunking_to_forward\u001b[0;34m(forward_fn, chunk_size, chunk_dim, *input_tensors)\u001b[0m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# concatenate output at same dimension\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcat(output_chunks, dim\u001b[38;5;241m=\u001b[39mchunk_dim)\n\u001b[0;32m-> 2438\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minput_tensors\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py:465\u001b[0m, in \u001b[0;36mRobertaLayer.feed_forward_chunk\u001b[0;34m(self, attention_output)\u001b[0m\n\u001b[1;32m 464\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfeed_forward_chunk\u001b[39m(\u001b[38;5;28mself\u001b[39m, attention_output):\n\u001b[0;32m--> 465\u001b[0m intermediate_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintermediate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattention_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 466\u001b[0m layer_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput(intermediate_output, attention_output)\n\u001b[1;32m 467\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m layer_output\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py:364\u001b[0m, in \u001b[0;36mRobertaIntermediate.forward\u001b[0;34m(self, hidden_states)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, hidden_states):\n\u001b[0;32m--> 364\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdense\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 365\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintermediate_act_fn(hidden_states)\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m hidden_states\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/linear.py:103\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/functional.py:1848\u001b[0m, in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m 1846\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_variadic(\u001b[38;5;28minput\u001b[39m, weight, bias):\n\u001b[1;32m 1847\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(linear, (\u001b[38;5;28minput\u001b[39m, weight, bias), \u001b[38;5;28minput\u001b[39m, weight, bias\u001b[38;5;241m=\u001b[39mbias)\n\u001b[0;32m-> 1848\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_nn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "for MODEL_ID, job in enumerate(os.listdir(JOB_DIR)):\n",
+ " if job != '.DS_Store':\n",
+ " job_path = os.path.join(JOB_DIR, job)\n",
+ " model_dir = os.path.join(job_path, 'models')\n",
+ " model_name = [item for item in os.listdir(model_dir) if item != '.DS_Store'][0]\n",
+ " model_name_decoded = decode_model_name(model_name).split('_')[0] # get base model name\n",
+ " \n",
+ " max_length = MAX_LENGTH_DICT[model_name_decoded]\n",
+ " # define tokenizer\n",
+ " tokenizer = AutoTokenizer.from_pretrained(model_name_decoded, add_prefix_space=True)\n",
+ " model_path = os.path.join(model_dir, model_name)\n",
+ " \n",
+ " strategy = CONFIG_MAP[job].get('strat')\n",
+ " Processor = CONFIG_MAP[job].get('processor')\n",
+ " print(\n",
+ " f'RUNNING FOR MODEL {MODEL_ID}: {model_name_decoded} at PATH: {model_path}\\n'\n",
+ " f'PARAMETERS: {strategy}. {Processor}'\n",
+ " )\n",
+ " print('=================================================================================')\n",
+ " \n",
+ " \n",
+ "\n",
+ "\n",
+ " s = time.time()\n",
+ " trained_model = AutoModelForTokenClassification.from_pretrained(model_path)\n",
+ " \n",
+ " RESULTS[job] = {}\n",
+ " \n",
+ " # test the model against itself\n",
+ " RESULTS[job]['self'] = {}\n",
+ " \n",
+ " # specify the path to the json\n",
+ " path = f'Main/0.Education/2.UCL/Courses/NLP/argument-mining/data/core/{strategy}'\n",
+ "\n",
+ " #df_test, df_label_map = _get_core_data(path, Processor, strategy)\n",
+ " #processor = Processor(path).from_json()\n",
+ " #df_dict = processor.get_tts(test_size=0.3)\n",
+ " #df_test = df_dict['test'][['text', 'labels']]\n",
+ " #df_label_map = LABELS_MAP_DICT[processor.__class__.__name__.split('Processor')[0]][strategy]\n",
+ " \n",
+ " #testset = ArgumentMiningDataset(\n",
+ " # df_label_map, df_test, tokenizer, max_length, f'standard_{strategy}', is_train=False\n",
+ " #)\n",
+ " #testloader = DataLoader(testset, batch_size=32)\n",
+ " testloader = _get_core_data(path, Processor, strategy, batch_size=BATCH_SIZE, limit=LIMIT)\n",
+ " print(f'Loaded and data loaded. Time: {time.time() - s: .3g}')\n",
+ "\n",
+ "\n",
+ " # TODO add metrics support\n",
+ " SELF = 'self'\n",
+ " s = time.time()\n",
+ " df_metrics, df_scores = inference(trained_model, testloader, )\n",
+ " macro_f1, df_scores_agg = _get_scores_agg(df_scores)\n",
+ " RESULTS[job][SELF]['core'] = {'macro_f1': macro_f1, 'scores': df_scores_agg}\n",
+ " \n",
+ " print(f'Took {time.time() -s:.3g} to get scores')\n",
+ " # aggregate the scores \n",
+ " # TODO save these scores!!!!!\n",
+ " \n",
+ " \n",
+ " \n",
+ " # test models against self adversarial examples\n",
+ "\n",
+ "\n",
+ " augmented_path = f'test/{strategy}'\n",
+ " for augmentation in os.listdir(augmented_path):\n",
+ " if augmentation != '.DS_Store':\n",
+ " augmentation_path = os.path.join(augmented_path, augmentation)\n",
+ " testloader = _get_core_data(augmentation_path, Processor, strategy,\n",
+ " batch_size=BATCH_SIZE, limit=LIMIT)\n",
+ " #processor = Processor(augmentation).from_json()\n",
+ " #df_test = processor.dataframe[['text', 'labels']]\n",
+ " #testset = ArgumentMiningDataset(\n",
+ " # df_label_map, df_test, tokenizer, max_length, f'standard_{strategy}', is_train=False\n",
+ " #)\n",
+ " #testloader = DataLoader(testset, batch_size=32)\n",
+ " df_metrics, df_scores = inference(trained_model, testloader, )\n",
+ " macro_f1, df_scores_agg = _get_scores_agg(df_scores)\n",
+ " RESULTS[job][SELF][augmentation] = {'macro_f1': macro_f1, 'scores': df_scores_agg}\n",
+ " # aggregate the scores\n",
+ " \n",
+ " \n",
+ " OTHER = 'other'\n",
+ " RESULTS[job][OTHER] = {}\n",
+ " \n",
+ " # get other processor\n",
+ " testloader = _get_other_data(path, Processor, strategy, batch_size=BATCH_SIZE, limit=LIMIT)\n",
+ " df_metrics, df_scores = inference(trained_model, testloader)\n",
+ " macro_f1, df_scores_agg = _get_scores_agg(df_scores)\n",
+ " RESULTS[job][OTHER]['core'] = {'macro_f1': macro_f1, 'scores': df_scores_agg}\n",
+ " \n",
+ " \n",
+ " for augmentation in os.listdir(augmented_path):\n",
+ " if augmentation != '.DS_Store':\n",
+ " augmentation_path = os.path.join(augmented_path, augmentation)\n",
+ " testloader = _get_other_data(augmentation_path, Processor, strategy,\n",
+ " batch_size=BATCH_SIZE, limit=LIMIT)\n",
+ " df_metrics, df_scores = inference(trained_model, testloader)\n",
+ " macro_f1, df_scores_agg = _get_scores_agg(df_scores)\n",
+ " RESULTS[job][OTHER][augmentation] = {'macro_f1': macro_f1, 'scores': df_scores_agg}\n",
+ " \n",
+ " #RESULTS[model_name_decoded]['self']['core'] # the score\n",
+ " \n",
+ " \n",
+ " # test model against other datasets, need to convert datasets\n",
+ " #RESULTS[model_name_decoded]['transfer'] = {}\n",
+ "\n",
+ "\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "2142cf8a",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'822594.undefined': {'self': {'core': {'macro_f1': 0.46401515151515155,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 0 0.0 2.0 4.0 0.000000\n",
+ " 1 1.0 1.0 1.0 0.500000\n",
+ " 2 1.0 1.0 1.0 0.500000\n",
+ " 3 3.0 4.0 6.0 0.375000\n",
+ " 6 5.0 0.0 1.0 0.909091\n",
+ " 7 1.0 1.0 1.0 0.500000},\n",
+ " 'custom_fillers': {'macro_f1': 0.0,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 1 0.0 0 2 0.0\n",
+ " 2 0.0 1 2 0.0\n",
+ " 3 0.0 0 2 0.0\n",
+ " 5 0.0 1 1 0.0\n",
+ " 6 0.0 1 2 0.0\n",
+ " 7 0.0 0 1 0.0},\n",
+ " 'synonym': {'macro_f1': 0.0,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 1 0.0 0 1 0.0\n",
+ " 2 0.0 1 2 0.0\n",
+ " 3 0.0 0 4 0.0\n",
+ " 5 0.0 1 0 0.0\n",
+ " 6 0.0 1 4 0.0},\n",
+ " 'keywordChange': {'macro_f1': 0.0,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 1 0.0 0 2 0.0\n",
+ " 2 0.0 1 4 0.0\n",
+ " 3 0.0 0 5 0.0\n",
+ " 5 0.0 1 1 0.0\n",
+ " 6 0.0 1 3 0.0},\n",
+ " 'spellingError': {'macro_f1': 0.0,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 1 0.0 0 2 0.0\n",
+ " 2 0.0 1 3 0.0\n",
+ " 3 0.0 0 2 0.0\n",
+ " 5 0.0 1 0 0.0\n",
+ " 6 0.0 1 6 0.0},\n",
+ " 'antonym': {'macro_f1': 0.0,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 1 0.0 0 3 0.0\n",
+ " 2 0.0 1 3 0.0\n",
+ " 3 0.0 0 3 0.0\n",
+ " 4 0.0 0 1 0.0\n",
+ " 5 0.0 1 2 0.0\n",
+ " 6 0.0 1 9 0.0}},\n",
+ " 'other': {'core': {'macro_f1': 0.09294871794871795,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 0 0.0 13 8.0 0.000000\n",
+ " 1 0.0 0 2.0 0.000000\n",
+ " 2 1.0 3 3.0 0.250000\n",
+ " 3 2.0 5 4.0 0.307692\n",
+ " 6 0.0 0 4.0 0.000000\n",
+ " 7 0.0 0 3.0 0.000000},\n",
+ " 'custom_fillers': {'macro_f1': 0.08465608465608465,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 0 0.0 13 7.0 0.000000\n",
+ " 1 0.0 0 3.0 0.000000\n",
+ " 2 1.0 3 2.0 0.285714\n",
+ " 3 2.0 5 9.0 0.222222\n",
+ " 6 0.0 0 6.0 0.000000\n",
+ " 7 0.0 0 2.0 0.000000},\n",
+ " 'synonym': {'macro_f1': 0.16666666666666666,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 0 0.0 13 6.0 0.000000\n",
+ " 1 0.0 0 2.0 0.000000\n",
+ " 2 2.0 2 0.0 0.666667\n",
+ " 3 2.0 5 3.0 0.333333\n",
+ " 6 0.0 0 5.0 0.000000\n",
+ " 7 0.0 0 2.0 0.000000},\n",
+ " 'keywordChange': {'macro_f1': 0.09294871794871795,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 0 0.0 13 9.0 0.000000\n",
+ " 1 0.0 0 4.0 0.000000\n",
+ " 2 1.0 3 3.0 0.250000\n",
+ " 3 2.0 5 4.0 0.307692\n",
+ " 6 0.0 0 4.0 0.000000\n",
+ " 7 0.0 0 2.0 0.000000},\n",
+ " 'spellingError': {'macro_f1': 0.1414141414141414,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 0 0.0 13 6.0 0.000000\n",
+ " 1 0.0 0 2.0 0.000000\n",
+ " 2 2.0 2 0.0 0.666667\n",
+ " 3 1.0 6 3.0 0.181818\n",
+ " 6 0.0 0 3.0 0.000000\n",
+ " 7 0.0 0 2.0 0.000000},\n",
+ " 'antonym': {'macro_f1': 0.15584415584415584,\n",
+ " 'scores': tp fn fp f1\n",
+ " class \n",
+ " 0 0.0 13 9.0 0.000000\n",
+ " 1 0.0 0 3.0 0.000000\n",
+ " 2 2.0 2 1.0 0.571429\n",
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+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "RESULTS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ac81730",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/GPU_otpimisation.ipynb b/experiments/yousef/GPU_otpimisation.ipynb
new file mode 100644
index 0000000..49cfee3
--- /dev/null
+++ b/experiments/yousef/GPU_otpimisation.ipynb
@@ -0,0 +1,365 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "GPU_otpimisation.ipynb",
+ "provenance": [],
+ "collapsed_sections": [],
+ "authorship_tag": "ABX9TyMvFUc4LzQTj4P9dJez/Oby",
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "72gEKBK2Ck4V",
+ "outputId": "2ad06bf4-6282-4efc-e81b-5f1e1ece5ffb"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting git+https://github.com/namiyousef/colab-utils.git\n",
+ " Cloning https://github.com/namiyousef/colab-utils.git to /tmp/pip-req-build-zdd9jwpb\n",
+ " Running command git clone -q https://github.com/namiyousef/colab-utils.git /tmp/pip-req-build-zdd9jwpb\n",
+ "Requirement already satisfied: nvidia-ml-py3 in /usr/local/lib/python3.7/dist-packages (from colabtools==0.0.1) (7.352.0)\n",
+ "Google Drive import successful.\n",
+ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
+ "Google Drive mount successful.\n",
+ "Import of ArgMiner successful\n",
+ "CUDA device detected. Using GPU...\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- env setup\n",
+ "import os\n",
+ "import gc\n",
+ "\n",
+ "!python3.7 -m pip install git+https://github.com/namiyousef/colab-utils.git\n",
+ "from colabtools.utils import get_gpu_utilization, mount_drive, install_private_library\n",
+ "\n",
+ "drive_path = mount_drive()\n",
+ "project_path = os.path.join(drive_path, 'argument-mining')\n",
+ "development_dir = os.path.join(drive_path, 'argument-mining/experiments/yousef')\n",
+ "\n",
+ "install_private_library(os.path.join(project_path, 'data/github_config.json'), 'argument-mining')\n",
+ "\n",
+ "# -- public imports\n",
+ "import sys\n",
+ "from transformers import BigBirdTokenizer, AutoTokenizer, AutoModelForTokenClassification, AutoConfig\n",
+ "import pandas as pd\n",
+ "from torch.utils.data import DataLoader\n",
+ "import torch\n",
+ "\n",
+ "# -- private imports\n",
+ "from argminer.data import KaggleDataset\n",
+ "\n",
+ "# -- dev imports\n",
+ "# add working package to system path\n",
+ "sys.path.append(development_dir)\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "from python_files.data import create_labels_doc_level\n",
+ "from python_files.run import train_longformer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "MODEL_NAME = 'allenai/longformer-base-4096'\n",
+ "print(f'GPU usage: {get_gpu_utilization()}')\n",
+ "try:\n",
+ " ma = torch.cuda.memory_allocated()\n",
+ " print(ma)\n",
+ "\n",
+ " mc = torch.cuda.memory_reserved()\n",
+ " print(mc)\n",
+ "\n",
+ " del tokenizer, model, optimizer, dataset, train_loader, df_kaggle, config_model\n",
+ " gc.collect()\n",
+ " torch.cuda.empty_cache()\n",
+ " ma = torch.cuda.memory_allocated()\n",
+ " print(ma)\n",
+ "\n",
+ " mc = torch.cuda.memory_cached()\n",
+ " print(mc)\n",
+ " print(f'GPU usage after deletion: {get_gpu_utilization()}')\n",
+ "except:\n",
+ " tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, add_prefix_space=True)\n",
+ "\n",
+ " config_model = AutoConfig.from_pretrained(MODEL_NAME) \n",
+ " config_model.num_labels = 15\n",
+ "\n",
+ " model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME, config=config_model)\n",
+ " optimizer = torch.optim.Adam(params=model.parameters())\n",
+ "\n",
+ " df_train = pd.read_csv(os.path.join(project_path, 'data/kaggle/test.csv'))\n",
+ " df_kaggle = df_train[['text', 'labels']].copy(); del df_train\n",
+ " df_kaggle.labels = df_kaggle.labels.apply(lambda x: [int(num) for num in x[1:-1].split(', ')])\n",
+ " df_kaggle.head(), df_kaggle.info()\n",
+ " \n",
+ " dataset = KaggleDataset(df_kaggle, tokenizer, 512)\n",
+ " train_loader = DataLoader(dataset, shuffle=True, batch_size=1)\n",
+ "\n",
+ " print(f'GPU usage after creation: {get_gpu_utilization()}')\n",
+ " torch.backends.cudnn.benchmark = True\n",
+ " torch.backends.cudnn.enabled = True\n",
+ " print(f'GPU mem: {get_gpu_utilization()}')\n",
+ " model.to('cuda:0')\n",
+ " print(f'GPU mem after model sent: {get_gpu_utilization()}')\n",
+ " for i, (inputs, targets) in enumerate(train_loader):\n",
+ " inputs = {key: val.to('cuda:0') for key, val in inputs.items()}\n",
+ " targets = targets.to('cuda:0')\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " loss, outputs = model(labels=targets, **inputs, return_dict=False)\n",
+ " print(f'GPU mem modelling batch {i+1}: {get_gpu_utilization()}')\n",
+ " del targets, inputs, loss, outputs\n",
+ " gc.collect()\n",
+ " torch.cuda.empty_cache()\n",
+ " print(f'GPU mem batch {i+1}: {get_gpu_utilization()}')\n",
+ " break\n",
+ " model.to('cpu')\n",
+ "\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JXbK-lQACvlQ",
+ "outputId": "c78af27e-c9ca-4cc5-8a21-da26e95ca8a4"
+ },
+ "execution_count": 31,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "GPU usage: 1330\n",
+ "0\n",
+ "0\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the model checkpoint at allenai/longformer-base-4096 were not used when initializing LongformerForTokenClassification: ['lm_head.dense.weight', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.decoder.weight']\n",
+ "- This IS expected if you are initializing LongformerForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing LongformerForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of LongformerForTokenClassification were not initialized from the model checkpoint at allenai/longformer-base-4096 and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "RangeIndex: 15594 entries, 0 to 15593\n",
+ "Data columns (total 2 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 text 15594 non-null object\n",
+ " 1 labels 15594 non-null object\n",
+ "dtypes: object(2)\n",
+ "memory usage: 243.8+ KB\n",
+ "GPU usage after creation: 1330\n",
+ "GPU mem: 1330\n",
+ "GPU mem after model sent: 1954\n",
+ "GPU mem modelling batch 1: 2806\n",
+ "GPU mem batch 1: 1954\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "hIxVRnrykku7"
+ },
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "wu9LFIm_l3sW"
+ },
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!nvidia-smi --gpu-reset"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ItvdqTBWmiaP",
+ "outputId": "43be8554-1171-40bf-ff63-2d0f360c0012"
+ },
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "GPU 00000000:00:04.0 is currently in use by another process.\n",
+ "\n",
+ "1 device is currently being used by one or more other processes (e.g., Fabric Manager, CUDA application, graphics application such as an X server, or a monitoring application such as another instance of nvidia-smi). Please first kill all processes using this device and all compute applications running in the system.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!sudo fuser -v /dev/nvidia*"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5a-cPS9EnaFJ",
+ "outputId": "cb973220-62ff-4e34-a14f-cdc55b7b3bc9"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " USER PID ACCESS COMMAND\n",
+ "/dev/nvidia0: root 14034 F...m python3\n",
+ "/dev/nvidiactl: root 14034 F...m python3\n",
+ "/dev/nvidia-uvm: root 14034 F...m python3\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!sudo fuser -v /dev/nvidia*"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "hjny6jbNnjFk",
+ "outputId": "0f4258f9-cfcc-4852-9562-ce02ed3ef8b5"
+ },
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " USER PID ACCESS COMMAND\n",
+ "/dev/nvidia0: root 14034 F...m python3\n",
+ "/dev/nvidiactl: root 14034 F...m python3\n",
+ "/dev/nvidia-uvm: root 14034 F...m python3\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!nvidia-smi"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "toeQ8EkPnnVT",
+ "outputId": "3d729f08-4119-48ef-c3a3-782d1704a6c1"
+ },
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Mon Mar 7 23:34:52 2022 \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
+ "|-------------------------------+----------------------+----------------------+\n",
+ "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
+ "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
+ "| | | MIG M. |\n",
+ "|===============================+======================+======================|\n",
+ "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
+ "| N/A 43C P0 27W / 70W | 1954MiB / 15109MiB | 0% Default |\n",
+ "| | | N/A |\n",
+ "+-------------------------------+----------------------+----------------------+\n",
+ " \n",
+ "+-----------------------------------------------------------------------------+\n",
+ "| Processes: |\n",
+ "| GPU GI CI PID Type Process name GPU Memory |\n",
+ "| ID ID Usage |\n",
+ "|=============================================================================|\n",
+ "+-----------------------------------------------------------------------------+\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import argminer"
+ ],
+ "metadata": {
+ "id": "9ZO5cVFgnptY"
+ },
+ "execution_count": 7,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "9MDNujKznrCm"
+ },
+ "execution_count": 7,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/experiments/yousef/Inference_non_poc.ipynb b/experiments/yousef/Inference_non_poc.ipynb
new file mode 100644
index 0000000..b74a197
--- /dev/null
+++ b/experiments/yousef/Inference_non_poc.ipynb
@@ -0,0 +1,2684 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "220598f1",
+ "metadata": {},
+ "source": [
+ "# End-to-end run no GPU"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8cffbd00",
+ "metadata": {},
+ "source": [
+ "### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "66f9f69a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# -- public imports\n",
+ "\n",
+ "from transformers import BigBirdTokenizer, AutoTokenizer, AutoModelForTokenClassification, AutoConfig\n",
+ "import pandas as pd\n",
+ "from torch.utils.data import DataLoader\n",
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "15f82e11",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- dev imports\n",
+ "# TODO prediction string stasrt from 0 or 1?\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "from argminer.data import ArgumentMiningDataset\n",
+ "from argminer.evaluation import inference, get_word_labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "24fcd3f8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, (inputs, targets) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[43mvalloader\u001b[49m):\n\u001b[1;32m 4\u001b[0m loss, outputs \u001b[38;5;241m=\u001b[39m model(\n\u001b[1;32m 5\u001b[0m labels\u001b[38;5;241m=\u001b[39mtargets,\n\u001b[1;32m 6\u001b[0m input_ids\u001b[38;5;241m=\u001b[39minputs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 7\u001b[0m attention_mask\u001b[38;5;241m=\u001b[39minputs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mattention_mask\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 8\u001b[0m return_dict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 9\u001b[0m )\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# validation should have this, all can still be on the GPU\u001b[39;00m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'valloader' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "with torch.no_grad():\n",
+ " \n",
+ " for i, (inputs, targets) in enumerate(valloader):\n",
+ " loss, outputs = model(\n",
+ " labels=targets,\n",
+ " input_ids=inputs['input_ids'],\n",
+ " attention_mask=inputs['attention_mask'],\n",
+ " return_dict=False\n",
+ " )\n",
+ " \n",
+ " # validation should have this, all can still be on the GPU\n",
+ " word_ids = inputs['word_ids']\n",
+ " doc_ids = inputs['index']\n",
+ " \n",
+ " word_label_probas = get_word_labels(word_ids, outputs, agg_strategy='first', has_x=False)\n",
+ " # can still be GPU\n",
+ " word_labels = [tensor.argmax(dim=1) for tensor in word_label_probas]\n",
+ " # still GPU\n",
+ " \n",
+ " target_labels = get_word_labels(word_ids, targets, agg_strategy='first', has_x=False)\n",
+ " \n",
+ " \n",
+ " # todo need to have a label to map groups of items back to each other,\n",
+ " # so first convert back to strings, then relabel based on strings, e.g. remove the 'B-I' stuff!\n",
+ " df_targets_predString = get_predictionString(target_labels, doc_ids)\n",
+ " df_outputs_predString = get_predictionString(word_labels, doc_ids)\n",
+ " \n",
+ " evaluate(df_outputs_predString, df_targets_predString)\n",
+ " \n",
+ " \n",
+ " # also needs the doc ID for the mapping, so doc_id is missing!!!!\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "id": "a6627d50",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m outputs \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39margmax(outputs, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39msum(outputs \u001b[38;5;241m==\u001b[39m targets, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m/\u001b[39m targets\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 14\u001b[0m \u001b[43minference\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mMultiAccuracy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Desktop/Main/0.Education/2.UCL/Courses/NLP/argument-mining/argminer/evaluation.py:179\u001b[0m, in \u001b[0;36minference\u001b[0;34m(model, testloader, metrics)\u001b[0m\n\u001b[1;32m 177\u001b[0m inputs \u001b[38;5;241m=\u001b[39m move_to_device(inputs, DEVICE)\n\u001b[1;32m 178\u001b[0m targets \u001b[38;5;241m=\u001b[39m move_to_device(targets, DEVICE)\n\u001b[0;32m--> 179\u001b[0m loss, outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 180\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 181\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 182\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mattention_mask\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 183\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[1;32m 184\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 185\u001b[0m df_metrics_no_agg \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame\u001b[38;5;241m.\u001b[39mfrom_records({\n\u001b[1;32m 186\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmetric\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_no_agg\u001b[39m\u001b[38;5;124m'\u001b[39m: metric(outputs, targets) \u001b[38;5;28;01mfor\u001b[39;00m metric \u001b[38;5;129;01min\u001b[39;00m metrics\n\u001b[1;32m 187\u001b[0m })\n\u001b[1;32m 189\u001b[0m word_ids \u001b[38;5;241m=\u001b[39m inputs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mword_ids\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/transformers/models/big_bird/modeling_big_bird.py:2871\u001b[0m, in \u001b[0;36mBigBirdForTokenClassification.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 2865\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2866\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\u001b[39;00m\n\u001b[1;32m 2867\u001b[0m \u001b[38;5;124;03m Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.\u001b[39;00m\n\u001b[1;32m 2868\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2869\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 2871\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbert\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2872\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2873\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2874\u001b[0m \u001b[43m \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2875\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2876\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2877\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2878\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2879\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2880\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2881\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2883\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 2885\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout(sequence_output)\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/transformers/models/big_bird/modeling_big_bird.py:2138\u001b[0m, in \u001b[0;36mBigBirdModel.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 2128\u001b[0m head_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_head_mask(head_mask, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mnum_hidden_layers)\n\u001b[1;32m 2130\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(\n\u001b[1;32m 2131\u001b[0m input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m 2132\u001b[0m position_ids\u001b[38;5;241m=\u001b[39mposition_ids,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2135\u001b[0m past_key_values_length\u001b[38;5;241m=\u001b[39mpast_key_values_length,\n\u001b[1;32m 2136\u001b[0m )\n\u001b[0;32m-> 2138\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2139\u001b[0m \u001b[43m \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2140\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2141\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2142\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2143\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_extended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2144\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2145\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2146\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2147\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2148\u001b[0m \u001b[43m \u001b[49m\u001b[43mband_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mband_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2149\u001b[0m \u001b[43m \u001b[49m\u001b[43mfrom_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2150\u001b[0m \u001b[43m \u001b[49m\u001b[43mto_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mto_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2151\u001b[0m \u001b[43m \u001b[49m\u001b[43mblocked_encoder_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblocked_encoder_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2152\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2153\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2154\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 2156\u001b[0m pooler_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mactivation(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(sequence_output[:, \u001b[38;5;241m0\u001b[39m, :])) \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/transformers/models/big_bird/modeling_big_bird.py:1647\u001b[0m, in \u001b[0;36mBigBirdEncoder.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, band_mask, from_mask, to_mask, blocked_encoder_mask, return_dict)\u001b[0m\n\u001b[1;32m 1645\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache:\n\u001b[1;32m 1646\u001b[0m next_decoder_cache \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (layer_outputs[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m],)\n\u001b[0;32m-> 1647\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43moutput_attentions\u001b[49m:\n\u001b[1;32m 1648\u001b[0m all_self_attentions \u001b[38;5;241m=\u001b[39m all_self_attentions \u001b[38;5;241m+\u001b[39m (layer_outputs[\u001b[38;5;241m1\u001b[39m],)\n\u001b[1;32m 1649\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39madd_cross_attention:\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "class MultiAccuracy(torch.nn.Module):\n",
+ " \"\"\"Calculates accuracy for multiclass inputs (batchsize, feature length) by determining the most likely class\n",
+ " using argmax -> (batchsize,) and then comparing with targets which are also (batchsize,)\n",
+ " \"\"\"\n",
+ " def __init__(self):\n",
+ " super(MultiAccuracy, self).__init__()\n",
+ "\n",
+ " def forward(self, outputs, targets):\n",
+ " if outputs.shape != targets.shape:\n",
+ " outputs = torch.argmax(outputs, dim=-1)\n",
+ " return torch.sum(outputs == targets, dim=-1) / targets.shape[-1]\n",
+ "\n",
+ " \n",
+ "inference(model, valloader, metrics=[MultiAccuracy()])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "79b20a06",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/Longformer_debug.ipynb b/experiments/yousef/Longformer_debug.ipynb
new file mode 100644
index 0000000..81c1e4a
--- /dev/null
+++ b/experiments/yousef/Longformer_debug.ipynb
@@ -0,0 +1,4187 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8zIVWaSRUxuA"
+ },
+ "source": [
+ "# Foreword:\n",
+ "To run in Colab, we need to have the files in Drive. \n",
+ "To do this easily, we have to follow a couple of steps:\n",
+ "1. Go to Federico's `NLP_project` folder in Drive [here](https://drive.google.com/drive/folders/16Gm33Ckb_YoX_z_x9xVITt2afa-aAPLX?usp=sharing), and Add a shortcut to your drive.\n",
+ "2. Mount google drive on Colab by running the code cells that will follow.\n",
+ "3. Done, the directory structure will look like this:\n",
+ "```\n",
+ "YOUR_GOOGLE_DRIVE/\n",
+ "└── COMP0087/\n",
+ " ├── data/\n",
+ " │ ├── test\n",
+ " │ ├── train\n",
+ " │ ├── train.csv\n",
+ " │ └── sample_submission.csv\n",
+ " ├── model\n",
+ " └── output\n",
+ "```\n",
+ "4. Make sure you change the directory you are using in the `HyperParameters` class defined below to `/content/drive/MyDrive/NLP_project`\n",
+ "I have already done this automatically by setting a cd to that folder if we are on colab, I am writing this just so that you are aware."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "ifzzOtkEUxuC",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "2be4b55c-738a-4f60-c694-6b3c68fc8843"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+ ]
+ }
+ ],
+ "source": [
+ "ON_COLAB = True\n",
+ "if ON_COLAB:\n",
+ " # Mount drive:\n",
+ " from google.colab import drive, files\n",
+ " # mount Google Drive\n",
+ " drive.mount(\"/content/drive\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "V5K5Cr2fUxuE"
+ },
+ "source": [
+ "# Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "eriNZqVPVW9a"
+ },
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "# if on Colab, we need to install missing stuff!\n",
+ "if ON_COLAB:\n",
+ " !pip install transformers\n",
+ " !pip install iterative-stratification\n",
+ " !pip install nvidia-ml-py3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "OtO0xcw1UxuF"
+ },
+ "outputs": [],
+ "source": [
+ "import gc\n",
+ "import os\n",
+ "import torch\n",
+ "import random\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import torch.nn as nn\n",
+ "from pynvml import nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit\n",
+ "\n",
+ "from tqdm.notebook import tqdm\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "from torch.cuda.amp import autocast, GradScaler\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "from transformers import AutoConfig, AutoModel, AutoTokenizer\n",
+ "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def print_gpu_utilization():\n",
+ " print(f\"GPU memory occupied: {get_gpu_utilization()} MB.\")\n",
+ "\n",
+ "def get_gpu_utilization():\n",
+ " nvmlInit()\n",
+ " handle = nvmlDeviceGetHandleByIndex(0)\n",
+ " info = nvmlDeviceGetMemoryInfo(handle)\n",
+ " return info.used//1024**2\n",
+ "\n",
+ "def print_summary(result):\n",
+ " print(f\"Time: {result.metrics['train_runtime']:.2f}\")\n",
+ " print(f\"Samples/second: {result.metrics['train_samples_per_second']:.2f}\")\n",
+ " print_gpu_utilization()\n",
+ "\n",
+ "print_gpu_utilization()"
+ ],
+ "metadata": {
+ "id": "4EE28HBXpjrJ",
+ "outputId": "5429cf18-4ca2-4f2b-81c3-639fadde27d6",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "GPU memory occupied: 0 MB.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "i9N0xf4RXlVL"
+ },
+ "outputs": [],
+ "source": [
+ "if ON_COLAB:\n",
+ " !cd /content/drive/MyDrive/NLP_project\n",
+ "\n",
+ "\n",
+ "# DATA DIR ---- TO CHANGE\n",
+ "DATA_DIR = 'drive/MyDrive/NLP_project/'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_lgNlqCIUxuF"
+ },
+ "source": [
+ "Config class containing all necessary hyperparameters:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "COkK9LFUUxuG"
+ },
+ "outputs": [],
+ "source": [
+ "class HyperParameters:\n",
+ " \n",
+ " # Here we choose model type. Can be changed for others\n",
+ " name = 'longformer'\n",
+ " model_savename = 'longformer'\n",
+ " model_name = 'allenai/longformer-base-4096' # this is the most important: determines what transformer is used in training\n",
+ " \n",
+ " # Directory hyperparameters: make sure to change with what you are using! Only needed to change here\n",
+ " base_dir = DATA_DIR\n",
+ " data_dir = os.path.join(base_dir, 'data')\n",
+ " pre_data_dir = os.path.join(base_dir, 'data/preprocessed')\n",
+ " model_dir = os.path.join(base_dir, f'model/{name}')\n",
+ " output_dir = os.path.join(base_dir, f'output/{name}')\n",
+ " \n",
+ " # Training hyperparameters\n",
+ " is_debug = False\n",
+ " n_epoch = 2 # not to exceed runtime limit\n",
+ " n_fold = 5\n",
+ " verbose_steps = 500\n",
+ " random_seed = 42\n",
+ "\n",
+ " # Model specific hyperparameters\n",
+ " max_length = 1024\n",
+ " inference_max_length = 4096\n",
+ " train_batch_size = 4\n",
+ " valid_batch_size = 4\n",
+ " lr = 4e-5\n",
+ "\n",
+ " # Task hyperparameters\n",
+ " num_labels = 15\n",
+ " label_subtokens = True\n",
+ " output_hidden_states = True\n",
+ " hidden_dropout_prob = 0.1\n",
+ " layer_norm_eps = 1e-7\n",
+ " add_pooling_layer = False\n",
+ " verbose_steps = 500\n",
+ " if is_debug:\n",
+ " debug_sample = 1000\n",
+ " verbose_steps = 16\n",
+ " n_epoch = 1\n",
+ " n_fold = 2\n",
+ "\n",
+ "if not os.path.exists(HyperParameters.model_dir):\n",
+ " !mkdir $HyperParameters.model_dir"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BF5bW6aVUxuG"
+ },
+ "source": [
+ "Constant for the task:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "id": "08glsD47UxuH"
+ },
+ "outputs": [],
+ "source": [
+ "IGNORE_INDEX = -100\n",
+ "NON_LABEL = -1\n",
+ "OUTPUT_LABELS = ['O', 'B-Lead', 'I-Lead', 'B-Position', 'I-Position', 'B-Claim', 'I-Claim', 'B-Counterclaim', 'I-Counterclaim', \n",
+ " 'B-Rebuttal', 'I-Rebuttal', 'B-Evidence', 'I-Evidence', 'B-Concluding Statement', 'I-Concluding Statement']\n",
+ "LABELS_TO_IDS = {v:k for k,v in enumerate(OUTPUT_LABELS)}\n",
+ "IDS_TO_LABELS = {k:v for k,v in enumerate(OUTPUT_LABELS)}\n",
+ "\n",
+ "MIN_THRESH = {\n",
+ " \"I-Lead\": 9,\n",
+ " \"I-Position\": 5,\n",
+ " \"I-Evidence\": 14,\n",
+ " \"I-Claim\": 3,\n",
+ " \"I-Concluding Statement\": 11,\n",
+ " \"I-Counterclaim\": 6,\n",
+ " \"I-Rebuttal\": 4,\n",
+ "}\n",
+ "\n",
+ "PROB_THRESH = {\n",
+ " \"I-Lead\": 0.7,\n",
+ " \"I-Position\": 0.55,\n",
+ " \"I-Evidence\": 0.65,\n",
+ " \"I-Claim\": 0.55,\n",
+ " \"I-Concluding Statement\": 0.7,\n",
+ " \"I-Counterclaim\": 0.5,\n",
+ " \"I-Rebuttal\": 0.55,\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5WJbWh4GUxuI"
+ },
+ "source": [
+ "Taming randomness and setting device"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "2u_8GvKvUxuI",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "862f4ecd-b342-482b-f278-b81d02cbf9c6"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Using device: cuda\n"
+ ]
+ }
+ ],
+ "source": [
+ "def set_seed(seed=HyperParameters.random_seed):\n",
+ " np.random.seed(seed)\n",
+ " \n",
+ " random.seed(seed)\n",
+ " \n",
+ " torch.manual_seed(seed)\n",
+ " torch.cuda.manual_seed(seed)\n",
+ " \n",
+ " torch.backends.cudnn.deterministic =True\n",
+ " torch.backends.cudnn.benchmark = False\n",
+ "\n",
+ "set_seed()\n",
+ "\n",
+ "# Set proper device\n",
+ "if torch.cuda.is_available():\n",
+ " device = torch.device('cuda')\n",
+ "else:\n",
+ " device = torch.device('cpu')\n",
+ "\n",
+ "print(f'Using device: {device}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IXW7QdTPUxuK"
+ },
+ "source": [
+ "# Data importing and preprocessing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Fh1Bdo7UUxuK"
+ },
+ "source": [
+ "Importing corrected data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "cDhwkU7MUxuK"
+ },
+ "outputs": [],
+ "source": [
+ "df_alltrain = pd.read_csv(f'{HyperParameters.data_dir}/corrected_train.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "O7hy7b6uUxuL"
+ },
+ "outputs": [],
+ "source": [
+ "def agg_essays(train_flg):\n",
+ " \"\"\"\n",
+ " Splits every word in an essay and adds the text of each essay to a dataframe.\n",
+ " \"\"\"\n",
+ " folder = 'train' if train_flg else 'test'\n",
+ " names, texts =[], []\n",
+ " for f in tqdm(list(os.listdir(f'{HyperParameters.data_dir}/{folder}'))):\n",
+ " names.append(f.replace('.txt', ''))\n",
+ " texts.append(open(f'{HyperParameters.data_dir}/{folder}/' + f, 'r').read())\n",
+ " df_texts = pd.DataFrame({'id': names, 'text': texts})\n",
+ "\n",
+ " df_texts['text_split'] = df_texts.text.str.split()\n",
+ " print('Completed tokenizing texts.')\n",
+ " return df_texts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "av_kvU8eUxuL"
+ },
+ "outputs": [],
+ "source": [
+ "def ner(df_texts, df_train):\n",
+ " \"\"\"\n",
+ " Maps discourse type to each word of the text, according to the train.csv file.\n",
+ " \"\"\"\n",
+ " all_entities = []\n",
+ " for _, row in tqdm(df_texts.iterrows(), total=len(df_texts)):\n",
+ " total = len(row['text_split'])\n",
+ " entities = ['O'] * total\n",
+ "\n",
+ " for _, row2 in df_train[df_train['id'] == row['id']].iterrows():\n",
+ " discourse = row2['discourse_type']\n",
+ " list_ix = [int(x) for x in row2['predictionstring'].split(' ')]\n",
+ " entities[list_ix[0]] = f'B-{discourse}'\n",
+ " for k in list_ix[1:]: entities[k] = f'I-{discourse}'\n",
+ " all_entities.append(entities)\n",
+ "\n",
+ " df_texts['entities'] = all_entities\n",
+ " print('Completed mapping discourse to each token.')\n",
+ " return df_texts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "Sq2Rco3eUxuL"
+ },
+ "outputs": [],
+ "source": [
+ "def preprocess(df_train = None):\n",
+ " \"\"\"\n",
+ " Generates the dataframe we will use for training.\n",
+ " Splits essays into words, assigns a token name to each word, and adds everything to a dataframe.\n",
+ " \"\"\"\n",
+ " if df_train is None:\n",
+ " train_flg = False\n",
+ " else:\n",
+ " train_flg = True\n",
+ " \n",
+ " df_texts = agg_essays(train_flg)\n",
+ "\n",
+ " if train_flg:\n",
+ " df_texts = ner(df_texts, df_train)\n",
+ " return df_texts\n",
+ "\n",
+ "# Make sure we only run pre-processing if we did not do it in the past:\n",
+ "\n",
+ "if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \n",
+ " alltrain_texts = preprocess(df_alltrain)\n",
+ " test_texts = preprocess()\n",
+ "else:\n",
+ " alltrain_texts = pd.read_csv(f\"{HyperParameters.data_dir}/train_folds.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "ZLa0fkXbUxuM",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 204
+ },
+ "outputId": "0d563dfc-8361-42b3-a647-29c2bd7d3caa"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
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+ " lr: 4e-05\n",
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+ " 77, 78, 79, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
+ " 90, 91, 92, 93, 94, 94, 95, 96, 97, 98, 99, 100, 101, 101,\n",
+ " 102, 102, 103, 104, 105, 106, 106, 107, 108, 108, 108, 109, 110, 111,\n",
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+ " 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,\n",
+ " 150, 151, 152, 153, 154, 155, 156, 157, 157, 158, 159, 160, 161, 162,\n",
+ " 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 175,\n",
+ " 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,\n",
+ " 190, 191, 191, 192, 192, 193, 194, 195, 196, 197, 197, 198, 198, 199,\n",
+ " 200, 201, 202, 203, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212,\n",
+ " 213, 214, 215, 215, 216, 216, 217, 218, 218, 219, 220, 220, 220, 220,\n",
+ " 220, 221, 221, 222, 223, 224, 225, 226, 227, 227, 228, 229, 230, 231,\n",
+ " 232, 233, 234, 235, 236, 236, 237, 238, 239, 240, 241, 242, 242, 242,\n",
+ " 243, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255,\n",
+ " 256, 257, 258, 259, 260, 261, 262, 263, 264, 264, 265, 266, 267, 268,\n",
+ " 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281,\n",
+ " 282, 283, 284, 285, 286, 287, 287, 288, 289, 290, 291, 292, 293, 294,\n",
+ " 295, 296, 297, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 306,\n",
+ " 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 319,\n",
+ " 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333,\n",
+ " 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346,\n",
+ " 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360,\n",
+ " 361, 362, 363, 363, 364, 365, 366, 367, 368, 369, 370, 370, 371, 372,\n",
+ " 373, 374, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385,\n",
+ " 386, 386, 387, 388, 388, 389, 390, 390, 391, 392, 393, 394, 394, 395,\n",
+ " 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 408,\n",
+ " 409, 410, 411, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421,\n",
+ " 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 432, 433,\n",
+ " 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447,\n",
+ " 448, 448, 449, 450, 451, 451, 452, 452, 453, 454, 455, 456, 457, 458,\n",
+ " 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469]],\n",
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+ " [-0.1190, -0.4741, 0.3333, ..., -0.7251, -0.8467, -0.0442],\n",
+ " [ 0.0159, -0.3042, -0.0317, ..., -0.4985, -0.5952, -0.0503],\n",
+ " ...,\n",
+ " [ 0.2512, -0.3347, -0.0521, ..., -0.2754, -0.3628, 0.0062],\n",
+ " [ 0.0347, -0.1438, 0.1104, ..., -0.2168, -0.0638, -0.1055],\n",
+ " [ 0.1670, -0.3379, 0.0158, ..., -0.3630, -0.3218, -0.1176]]],\n",
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+ " [ 0.1670, -0.3379, 0.0158, ..., -0.3630, -0.3218, -0.1176]]],\n",
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+ " 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
+ " 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2,\n",
+ " 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 11, 11,\n",
+ " 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 3, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 4, 4,\n",
+ " 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,\n",
+ " 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,\n",
+ " 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 3, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 6, 13, 13, 13, 13, 13, 13, 13, 13,\n",
+ " 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
+ " 13, 13, 3, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
+ " 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 5, 12, 12, 12, 12,\n",
+ " 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
+ " 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
+ " 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
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+ " [0.0630, 0.0442, 0.0991, ..., 0.0344, 0.0305, 0.0679],\n",
+ " [0.0731, 0.0531, 0.0697, ..., 0.0437, 0.0397, 0.0684],\n",
+ " ...,\n",
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+ " [0.0861, 0.0520, 0.0740, ..., 0.0507, 0.0528, 0.0648]]],\n",
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+ " 233]], device='cuda:0'), 'preds_prob': tensor([[0.1466, 0.0923, 0.1648, 0.1434, 0.2612, 0.1029, 0.0749, 0.1711, 0.2017,\n",
+ " 0.1486, 0.1722, 0.1117, 0.1091, 0.0998, 0.1423]], device='cuda:0',\n",
+ " dtype=torch.float16, grad_fn=)}, {'__name__': '__main__', '__doc__': 'Automatically created module for IPython interactive environment', '__package__': None, '__loader__': None, '__spec__': None, '__builtin__': , '__builtins__': , '_ih': ['', 'ON_COLAB = True\\nif ON_COLAB:\\n # Mount drive:\\n from google.colab import drive, files\\n # mount Google Drive\\n drive.mount(\"/content/drive\")', \"get_ipython().run_cell_magic('capture', '', '# if on Colab, we need to install missing stuff!\\\\nif ON_COLAB:\\\\n !pip install transformers\\\\n !pip install iterative-stratification\\\\n !pip install nvidia-ml-py3')\", 'import gc\\nimport os\\nimport torch\\nimport random\\nimport numpy as np\\nimport pandas as pd\\nimport torch.nn as nn\\nfrom pynvml import nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit\\n\\nfrom tqdm.notebook import tqdm\\nfrom sklearn.metrics import accuracy_score\\nfrom torch.cuda.amp import autocast, GradScaler\\nfrom torch.utils.data import Dataset, DataLoader\\nfrom transformers import AutoConfig, AutoModel, AutoTokenizer\\nfrom iterstrat.ml_stratifiers import MultilabelStratifiedKFold', 'def print_gpu_utilization():\\n print(f\"GPU memory occupied: {get_gpu_utilization()} MB.\")\\n\\ndef get_gpu_utilization():\\n nvmlInit()\\n handle = nvmlDeviceGetHandleByIndex(0)\\n info = nvmlDeviceGetMemoryInfo(handle)\\n return info.used//1024**2\\n\\ndef print_summary(result):\\n print(f\"Time: {result.metrics[\\'train_runtime\\']:.2f}\")\\n print(f\"Samples/second: {result.metrics[\\'train_samples_per_second\\']:.2f}\")\\n print_gpu_utilization()\\n\\nprint_gpu_utilization()', \"if ON_COLAB:\\n get_ipython().system('cd /content/drive/MyDrive/NLP_project')\\n\\n\\n# DATA DIR ---- TO CHANGE\\nDATA_DIR = 'drive/MyDrive/NLP_project/'\", \"class HyperParameters:\\n \\n # Here we choose model type. Can be changed for others\\n name = 'longformer'\\n model_savename = 'longformer'\\n model_name = 'allenai/longformer-base-4096' # this is the most important: determines what transformer is used in training\\n \\n # Directory hyperparameters: make sure to change with what you are using! Only needed to change here\\n base_dir = DATA_DIR\\n data_dir = os.path.join(base_dir, 'data')\\n pre_data_dir = os.path.join(base_dir, 'data/preprocessed')\\n model_dir = os.path.join(base_dir, f'model/{name}')\\n output_dir = os.path.join(base_dir, f'output/{name}')\\n \\n # Training hyperparameters\\n is_debug = False\\n n_epoch = 2 # not to exceed runtime limit\\n n_fold = 5\\n verbose_steps = 500\\n random_seed = 42\\n\\n # Model specific hyperparameters\\n max_length = 1024\\n inference_max_length = 4096\\n train_batch_size = 4\\n valid_batch_size = 4\\n lr = 4e-5\\n\\n # Task hyperparameters\\n num_labels = 15\\n label_subtokens = True\\n output_hidden_states = True\\n hidden_dropout_prob = 0.1\\n layer_norm_eps = 1e-7\\n add_pooling_layer = False\\n verbose_steps = 500\\n if is_debug:\\n debug_sample = 1000\\n verbose_steps = 16\\n n_epoch = 1\\n n_fold = 2\\n\\nif not os.path.exists(HyperParameters.model_dir):\\n get_ipython().system('mkdir $HyperParameters.model_dir')\", 'IGNORE_INDEX = -100\\nNON_LABEL = -1\\nOUTPUT_LABELS = [\\'O\\', \\'B-Lead\\', \\'I-Lead\\', \\'B-Position\\', \\'I-Position\\', \\'B-Claim\\', \\'I-Claim\\', \\'B-Counterclaim\\', \\'I-Counterclaim\\', \\n \\'B-Rebuttal\\', \\'I-Rebuttal\\', \\'B-Evidence\\', \\'I-Evidence\\', \\'B-Concluding Statement\\', \\'I-Concluding Statement\\']\\nLABELS_TO_IDS = {v:k for k,v in enumerate(OUTPUT_LABELS)}\\nIDS_TO_LABELS = {k:v for k,v in enumerate(OUTPUT_LABELS)}\\n\\nMIN_THRESH = {\\n \"I-Lead\": 9,\\n \"I-Position\": 5,\\n \"I-Evidence\": 14,\\n \"I-Claim\": 3,\\n \"I-Concluding Statement\": 11,\\n \"I-Counterclaim\": 6,\\n \"I-Rebuttal\": 4,\\n}\\n\\nPROB_THRESH = {\\n \"I-Lead\": 0.7,\\n \"I-Position\": 0.55,\\n \"I-Evidence\": 0.65,\\n \"I-Claim\": 0.55,\\n \"I-Concluding Statement\": 0.7,\\n \"I-Counterclaim\": 0.5,\\n \"I-Rebuttal\": 0.55,\\n}', \"def set_seed(seed=HyperParameters.random_seed):\\n np.random.seed(seed)\\n \\n random.seed(seed)\\n \\n torch.manual_seed(seed)\\n torch.cuda.manual_seed(seed)\\n \\n torch.backends.cudnn.deterministic =True\\n torch.backends.cudnn.benchmark = False\\n\\nset_seed()\\n\\n# Set proper device\\nif torch.cuda.is_available():\\n device = torch.device('cuda')\\nelse:\\n device = torch.device('cpu')\\n\\nprint(f'Using device: {device}')\", \"df_alltrain = pd.read_csv(f'{HyperParameters.data_dir}/corrected_train.csv')\", 'def agg_essays(train_flg):\\n \"\"\"\\n Splits every word in an essay and adds the text of each essay to a dataframe.\\n \"\"\"\\n folder = \\'train\\' if train_flg else \\'test\\'\\n names, texts =[], []\\n for f in tqdm(list(os.listdir(f\\'{HyperParameters.data_dir}/{folder}\\'))):\\n names.append(f.replace(\\'.txt\\', \\'\\'))\\n texts.append(open(f\\'{HyperParameters.data_dir}/{folder}/\\' + f, \\'r\\').read())\\n df_texts = pd.DataFrame({\\'id\\': names, \\'text\\': texts})\\n\\n df_texts[\\'text_split\\'] = df_texts.text.str.split()\\n print(\\'Completed tokenizing texts.\\')\\n return df_texts', 'def ner(df_texts, df_train):\\n \"\"\"\\n Maps discourse type to each word of the text, according to the train.csv file.\\n \"\"\"\\n all_entities = []\\n for _, row in tqdm(df_texts.iterrows(), total=len(df_texts)):\\n total = len(row[\\'text_split\\'])\\n entities = [\\'O\\'] * total\\n\\n for _, row2 in df_train[df_train[\\'id\\'] == row[\\'id\\']].iterrows():\\n discourse = row2[\\'discourse_type\\']\\n list_ix = [int(x) for x in row2[\\'predictionstring\\'].split(\\' \\')]\\n entities[list_ix[0]] = f\\'B-{discourse}\\'\\n for k in list_ix[1:]: entities[k] = f\\'I-{discourse}\\'\\n all_entities.append(entities)\\n\\n df_texts[\\'entities\\'] = all_entities\\n print(\\'Completed mapping discourse to each token.\\')\\n return df_texts', 'def preprocess(df_train = None):\\n \"\"\"\\n Generates the dataframe we will use for training.\\n Splits essays into words, assigns a token name to each word, and adds everything to a dataframe.\\n \"\"\"\\n if df_train is None:\\n train_flg = False\\n else:\\n train_flg = True\\n \\n df_texts = agg_essays(train_flg)\\n\\n if train_flg:\\n df_texts = ner(df_texts, df_train)\\n return df_texts\\n\\n# Make sure we only run pre-processing if we did not do it in the past:\\n\\nif not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n alltrain_texts = preprocess(df_alltrain)\\n test_texts = preprocess()\\nelse:\\n alltrain_texts = pd.read_csv(f\"{HyperParameters.data_dir}/train_folds.csv\")', \"# Visualize preprocessing result:\\nparse_string = lambda x: [string[1:-1] for string in x[1:-1].split(', ')]\\nalltrain_texts.entities = alltrain_texts.entities.apply(parse_string)\\nalltrain_texts.text_split = alltrain_texts.text_split.apply(parse_string)\\n\\nalltrain_texts.head()\", 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Transform categorical labels to dummy variables. Group by id. Sum over dummy. \\n dfx = pd.get_dummies(df_alltrain, columns=[\"discourse_type\"]).groupby([\"id\"], as_index=False).sum()\\n\\n # Generate name for the dummy columns\\n dummy_cols = [c for c in dfx.columns if c.startswith(\"discourse_type_\") or c == \"id\" and c != \"discourse_type_num\"]\\n # dfx is now only the dataset with dummy columns selected: don\\'t need to pass the data to do the splits\\n dfx = dfx[dummy_cols]', 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Generate cross validation object\\n mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)\\n\\n # Extract labels\\n labels = [c for c in dfx.columns if c != \"id\"]\\n dfx_labels = dfx[labels]\\n\\n # Dummy kfold assignment\\n dfx[\"kfold\"] = -1\\n\\n # Split\\n for fold, (trn_, val_) in enumerate(mskf.split(dfx, dfx_labels)):\\n print(len(trn_), len(val_))\\n \\n # Change the value of the kfold column at the validation index to the value of the fold\\n # This will tell us when to use the current entry in the validation set\\n dfx.loc[val_, \"kfold\"] = fold\\n\\n # merge back to original dataframe\\n alltrain_texts = alltrain_texts.merge(dfx[[\"id\", \"kfold\"]], on=\"id\", how=\"left\")\\n print(alltrain_texts.kfold.value_counts())\\n\\n # Save so next time we import it directly\\n alltrain_texts.to_csv(f\"{HyperParameters.data_dir}/train_folds.csv\", index=False)', \"# need help with this\\nclass FeedbackPrizeDataset(Dataset):\\n def __init__(self, dataframe, tokenizer, max_len, has_labels):\\n self.len = len(dataframe)\\n self.data = dataframe\\n self.tokenizer = tokenizer\\n self.max_len = max_len\\n self.has_labels = has_labels\\n \\n def __getitem__(self, index):\\n text = self.data.text[index]\\n encoding = self.tokenizer(\\n text.split(),\\n is_split_into_words = True,\\n padding = 'max_length',\\n truncation = True,\\n max_length = self.max_len\\n )\\n word_ids = encoding.word_ids()\\n\\n # targets\\n if self.has_labels:\\n word_labels = self.data.entities[index]\\n prev_word_idx = None\\n labels_ids = []\\n for word_idx in word_ids:\\n if word_idx is None:\\n labels_ids.append(IGNORE_INDEX)\\n elif word_idx != prev_word_idx:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n if HyperParameters.label_subtokens:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n labels_ids.append(IGNORE_INDEX)\\n prev_word_idx = word_idx\\n encoding['labels'] = labels_ids\\n # convert to torch.tensor\\n item = {k: torch.as_tensor(v) for k, v in encoding.items()}\\n word_ids2 = [w if w is not None else NON_LABEL for w in word_ids]\\n item['word_ids'] = torch.as_tensor(word_ids2)\\n return item\\n\\n def __len__(self):\\n return self.len\", \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", 'def build_model_tokenizer():\\n tokenizer = AutoTokenizer.from_pretrained(HyperParameters.model_name, add_prefix_space = True)\\n model = FeedbackModel()\\n return model, tokenizer', '# Need help with this: used in training to transform raw logits to labels needed\\ndef active_logits(raw_logits, word_ids):\\n word_ids = word_ids.view(-1)\\n active_mask = word_ids.unsqueeze(1).expand(word_ids.shape[0], HyperParameters.num_labels)\\n active_mask = active_mask != NON_LABEL\\n active_logits = raw_logits.view(-1, HyperParameters.num_labels)\\n active_logits = torch.masked_select(active_logits, active_mask) # return 1dTensor\\n active_logits = active_logits.view(-1, HyperParameters.num_labels) \\n return active_logits\\n\\ndef active_labels(labels):\\n active_mask = labels.view(-1) != IGNORE_INDEX\\n active_labels = torch.masked_select(labels.view(-1), active_mask)\\n return active_labels\\n\\ndef active_preds_prob(active_logits):\\n active_preds = torch.argmax(active_logits, axis = 1)\\n active_preds_prob, _ = torch.max(active_logits, axis = 1)\\n return active_preds, active_preds_prob', 'def calculate_overlap(set_pred, set_gt):\\n \"\"\"\\n Calculates if the overlap between prediction and\\n ground truth is enough fora potential True positive\\n \"\"\"\\n # Length of each and intersection\\n try:\\n len_gt = len(set_gt)\\n len_pred = len(set_pred)\\n inter = len(set_gt & set_pred)\\n overlap_1 = inter / len_gt\\n overlap_2 = inter/ len_pred\\n return overlap_1 >= 0.5 and overlap_2 >= 0.5\\n except: # at least one of the input is NaN\\n return False\\n\\ndef score_feedback_comp_micro(pred_df, gt_df, discourse_type):\\n \"\"\"\\n A function that scores for the kaggle\\n Student Writing Competition\\n \\n Uses the steps in the evaluation page here:\\n https://www.kaggle.com/c/feedback-prize-2021/overview/evaluation\\n \"\"\"\\n gt_df = gt_df.loc[gt_df[\\'discourse_type\\'] == discourse_type, \\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df = pred_df.loc[pred_df[\\'class\\'] == discourse_type,\\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df[\\'pred_id\\'] = pred_df.index\\n gt_df[\\'gt_id\\'] = gt_df.index\\n pred_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in pred_df[\\'predictionstring\\']]\\n gt_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in gt_df[\\'predictionstring\\']]\\n \\n # Step 1. all ground truths and predictions for a given class are compared.\\n joined = pred_df.merge(gt_df,\\n left_on=\\'id\\',\\n right_on=\\'id\\',\\n how=\\'outer\\',\\n suffixes=(\\'_pred\\',\\'_gt\\')\\n )\\n overlaps = [calculate_overlap(*args) for args in zip(joined.predictionstring_pred, \\n joined.predictionstring_gt)]\\n \\n # 2. If the overlap between the ground truth and prediction is >= 0.5, \\n # and the overlap between the prediction and the ground truth >= 0.5,\\n # the prediction is a match and considered a true positive.\\n # If multiple matches exist, the match with the highest pair of overlaps is taken.\\n # we don\\'t need to compute the match to compute the score\\n TP = joined.loc[overlaps][\\'gt_id\\'].nunique()\\n\\n # 3. Any unmatched ground truths are false negatives\\n # and any unmatched predictions are false positives.\\n TPandFP = len(pred_df)\\n TPandFN = len(gt_df)\\n \\n #calc microf1\\n my_f1_score = 2*TP / (TPandFP + TPandFN)\\n return my_f1_score\\n\\ndef score_feedback_comp(pred_df, gt_df, return_class_scores=False):\\n \"\"\"\\n Final helper function for model evaluation.\\n \\n Args:\\n pred_df (pandas.DataFrame): dataframe containing model predictions. Needs to have columns: [\\'id\\',\\'class\\',\\'predictionstring\\']\\n gt_df (pandas.DataFrame): dataframe of ground truth used for model training\\n return_class_scores (bool): Boolean indicating if we want to return the F1 score for each predicted class.\\n \\n Returns:\\n f1 (float): F1 score of the model\\n (optional) class_scores (dict): Dictionary of per-class F1 score\\n \"\"\"\\n class_scores = {}\\n for discourse_type in gt_df.discourse_type.unique():\\n class_score = score_feedback_comp_micro(pred_df, gt_df, discourse_type)\\n class_scores[discourse_type] = class_score\\n f1 = np.mean([v for v in class_scores.values()])\\n if return_class_scores:\\n return f1, class_scores\\n return f1', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n \\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'def valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion):\\n oof, valid_loss, valid_acc = get_preds_onefold(model, df_val, dl_val, criterion, valid_flg=True)\\n f1score =[]\\n # classes = oof[\\'class\\'].unique()\\n classes = [\\'Lead\\', \\'Position\\', \\'Claim\\',\\'Counterclaim\\', \\'Rebuttal\\',\\'Evidence\\',\\'Concluding Statement\\']\\n print(f\"Validation F1 scores\")\\n\\n for c in classes:\\n pred_df = oof.loc[oof[\\'class\\'] == c].copy()\\n gt_df = df_val_eval.loc[df_val_eval[\\'discourse_type\\'] == c].copy()\\n f1 = score_feedback_comp(pred_df, gt_df)\\n print(f\\' * {c:<10}: {f1:4f}\\')\\n f1score.append(f1)\\n f1avg = np.mean(f1score)\\n print(f\\'Overall Validation avg F1: {f1avg:.4f} val_loss:{valid_loss:.4f} val_accuracy:{valid_acc:.4f}\\')\\n return valid_loss, oof', 'def inference(model, data_loader, criterion, valid_flg):\\n stream = tqdm(data_loader)\\n model.eval()\\n \\n valid_loss = 0\\n valid_accuracy = 0\\n all_logits = None\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch[\\'input_ids\\'].to(device, dtype = torch.long)\\n mask = batch[\\'attention_mask\\'].to(device, dtype = torch.long)\\n with torch.no_grad():\\n raw_logits = model(input_ids=ids, mask = mask)\\n del ids, mask\\n \\n word_ids = batch[\\'word_ids\\'].to(device, dtype = torch.long)\\n logits = active_logits(raw_logits, word_ids)\\n sf_logits = torch.softmax(logits, dim= -1)\\n sf_raw_logits = torch.softmax(raw_logits, dim=-1)\\n if valid_flg: \\n raw_labels = batch[\\'labels\\'].to(device, dtype = torch.long)\\n labels = active_labels(raw_labels)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n valid_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n loss = criterion(logits, labels)\\n valid_loss += loss.item()\\n \\n if batch_idx == 1:\\n all_logits = sf_raw_logits.cpu().numpy()\\n else:\\n all_logits = np.append(all_logits, sf_raw_logits.cpu().numpy(), axis=0)\\n\\n \\n if valid_flg: \\n epoch_loss = valid_loss / batch_idx\\n epoch_accuracy = valid_accuracy / batch_idx\\n else:\\n epoch_loss, epoch_accuracy = 0, 0\\n return all_logits, epoch_loss, epoch_accuracy\\n\\n\\ndef preds_class_prob(all_logits, data_loader):\\n print(\"predict target class and its probabilty\")\\n final_predictions = []\\n final_predictions_score = []\\n stream = tqdm(data_loader)\\n len_sample = all_logits.shape[0]\\n\\n for batch_idx, batch in enumerate(stream, start=0):\\n for minibatch_idx in range(HyperParameters.valid_batch_size):\\n sample_idx = int(batch_idx * HyperParameters.valid_batch_size + minibatch_idx)\\n if sample_idx > len_sample - 1 : break\\n word_ids = batch[\\'word_ids\\'][minibatch_idx].numpy()\\n predictions =[]\\n predictions_prob = []\\n pred_class_id = np.argmax(all_logits[sample_idx], axis=1)\\n pred_score = np.max(all_logits[sample_idx], axis=1)\\n pred_class_labels = [IDS_TO_LABELS[i] for i in pred_class_id]\\n prev_word_idx = -1\\n for idx, word_idx in enumerate(word_ids):\\n if word_idx == -1:\\n pass\\n elif word_idx != prev_word_idx:\\n predictions.append(pred_class_labels[idx])\\n predictions_prob.append(pred_score[idx])\\n prev_word_idx = word_idx\\n final_predictions.append(predictions)\\n final_predictions_score.append(predictions_prob)\\n return final_predictions, final_predictions_score', 'def get_preds_onefold(model, df, dl, criterion, valid_flg):\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n all_preds, all_preds_prob = preds_class_prob(logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred, valid_loss, valid_acc\\n\\ndef get_preds_folds(model, df, dl, criterion, valid_flg=False):\\n for i_fold in range(HyperParameters.n_fold):\\n model_filename = os.path.join(HyperParameters.model_dir, f\"{HyperParameters.model_savename}_{i_fold}.bin\")\\n print(f\"{model_filename} inference\")\\n model = model.to(device)\\n model.load_state_dict(torch.load(model_filename))\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n if i_fold == 0:\\n avg_pred_logits = logits\\n else:\\n avg_pred_logits += logits\\n avg_pred_logits /= HyperParameters.n_fold\\n all_preds, all_preds_prob = preds_class_prob(avg_pred_logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred\\n\\ndef post_process_pred(df, all_preds, all_preds_prob):\\n final_preds = []\\n for i in range(len(df)):\\n idx = df.id.values[i]\\n pred = all_preds[i]\\n pred_prob = all_preds_prob[i]\\n j = 0\\n while j < len(pred):\\n cls = pred[j]\\n if cls == \\'O\\': j += 1\\n else: cls = cls.replace(\\'B\\', \\'I\\')\\n end = j + 1\\n while end < len(pred) and pred[end] == cls:\\n end += 1\\n if cls != \\'O\\' and cls !=\\'\\':\\n avg_score = np.mean(pred_prob[j:end])\\n if end - j > MIN_THRESH[cls] and avg_score > PROB_THRESH[cls]:\\n final_preds.append((idx, cls.replace(\\'I-\\', \\'\\'), \\' \\'.join(map(str, list(range(j, end))))))\\n j = end\\n df_pred = pd.DataFrame(final_preds)\\n df_pred.columns = [\\'id\\', \\'class\\', \\'new_predictionstring\\']\\n return df_pred', 'print_gpu_utilization()', 'def pretty_size(size):\\n\\t\"\"\"Pretty prints a torch.Size object\"\"\"\\n\\tassert(isinstance(size, torch.Size))\\n\\treturn \" × \".join(map(str, size))\\n\\ndef dump_tensors(gpu_only=True):\\n\\t\"\"\"Prints a list of the Tensors being tracked by the garbage collector.\"\"\"\\n\\timport gc\\n\\ttotal_size = 0\\n\\tfor obj in gc.get_objects():\\n\\t\\ttry:\\n\\t\\t\\tif torch.is_tensor(obj):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s:%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.numel()\\n\\t\\t\\telif hasattr(obj, \"data\") and torch.is_tensor(obj.data):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s → %s:%s%s%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t type(obj.data).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.data.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" grad\" if obj.requires_grad else \"\", \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" volatile\" if obj.volatile else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.data.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.data.numel()\\n\\t\\texcept Exception as e:\\n\\t\\t\\tpass \\n\\tprint(\"Total size:\", total_size)', 'dump_tensors()', 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', 'try:\\n print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n #if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n print(model_config)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(raw_logits.shape)\\n print(word_ids.shape)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n labels = raw_labels\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n print(gc.get_referrers(model))\\n raise Exception()\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])'], '_oh': {13: id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 }, '_dh': ['/content'], '_sh': , 'In': ['', 'ON_COLAB = True\\nif ON_COLAB:\\n # Mount drive:\\n from google.colab import drive, files\\n # mount Google Drive\\n drive.mount(\"/content/drive\")', \"get_ipython().run_cell_magic('capture', '', '# if on Colab, we need to install missing stuff!\\\\nif ON_COLAB:\\\\n !pip install transformers\\\\n !pip install iterative-stratification\\\\n !pip install nvidia-ml-py3')\", 'import gc\\nimport os\\nimport torch\\nimport random\\nimport numpy as np\\nimport pandas as pd\\nimport torch.nn as nn\\nfrom pynvml import nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit\\n\\nfrom tqdm.notebook import tqdm\\nfrom sklearn.metrics import accuracy_score\\nfrom torch.cuda.amp import autocast, GradScaler\\nfrom torch.utils.data import Dataset, DataLoader\\nfrom transformers import AutoConfig, AutoModel, AutoTokenizer\\nfrom iterstrat.ml_stratifiers import MultilabelStratifiedKFold', 'def print_gpu_utilization():\\n print(f\"GPU memory occupied: {get_gpu_utilization()} MB.\")\\n\\ndef get_gpu_utilization():\\n nvmlInit()\\n handle = nvmlDeviceGetHandleByIndex(0)\\n info = nvmlDeviceGetMemoryInfo(handle)\\n return info.used//1024**2\\n\\ndef print_summary(result):\\n print(f\"Time: {result.metrics[\\'train_runtime\\']:.2f}\")\\n print(f\"Samples/second: {result.metrics[\\'train_samples_per_second\\']:.2f}\")\\n print_gpu_utilization()\\n\\nprint_gpu_utilization()', \"if ON_COLAB:\\n get_ipython().system('cd /content/drive/MyDrive/NLP_project')\\n\\n\\n# DATA DIR ---- TO CHANGE\\nDATA_DIR = 'drive/MyDrive/NLP_project/'\", \"class HyperParameters:\\n \\n # Here we choose model type. Can be changed for others\\n name = 'longformer'\\n model_savename = 'longformer'\\n model_name = 'allenai/longformer-base-4096' # this is the most important: determines what transformer is used in training\\n \\n # Directory hyperparameters: make sure to change with what you are using! Only needed to change here\\n base_dir = DATA_DIR\\n data_dir = os.path.join(base_dir, 'data')\\n pre_data_dir = os.path.join(base_dir, 'data/preprocessed')\\n model_dir = os.path.join(base_dir, f'model/{name}')\\n output_dir = os.path.join(base_dir, f'output/{name}')\\n \\n # Training hyperparameters\\n is_debug = False\\n n_epoch = 2 # not to exceed runtime limit\\n n_fold = 5\\n verbose_steps = 500\\n random_seed = 42\\n\\n # Model specific hyperparameters\\n max_length = 1024\\n inference_max_length = 4096\\n train_batch_size = 4\\n valid_batch_size = 4\\n lr = 4e-5\\n\\n # Task hyperparameters\\n num_labels = 15\\n label_subtokens = True\\n output_hidden_states = True\\n hidden_dropout_prob = 0.1\\n layer_norm_eps = 1e-7\\n add_pooling_layer = False\\n verbose_steps = 500\\n if is_debug:\\n debug_sample = 1000\\n verbose_steps = 16\\n n_epoch = 1\\n n_fold = 2\\n\\nif not os.path.exists(HyperParameters.model_dir):\\n get_ipython().system('mkdir $HyperParameters.model_dir')\", 'IGNORE_INDEX = -100\\nNON_LABEL = -1\\nOUTPUT_LABELS = [\\'O\\', \\'B-Lead\\', \\'I-Lead\\', \\'B-Position\\', \\'I-Position\\', \\'B-Claim\\', \\'I-Claim\\', \\'B-Counterclaim\\', \\'I-Counterclaim\\', \\n \\'B-Rebuttal\\', \\'I-Rebuttal\\', \\'B-Evidence\\', \\'I-Evidence\\', \\'B-Concluding Statement\\', \\'I-Concluding Statement\\']\\nLABELS_TO_IDS = {v:k for k,v in enumerate(OUTPUT_LABELS)}\\nIDS_TO_LABELS = {k:v for k,v in enumerate(OUTPUT_LABELS)}\\n\\nMIN_THRESH = {\\n \"I-Lead\": 9,\\n \"I-Position\": 5,\\n \"I-Evidence\": 14,\\n \"I-Claim\": 3,\\n \"I-Concluding Statement\": 11,\\n \"I-Counterclaim\": 6,\\n \"I-Rebuttal\": 4,\\n}\\n\\nPROB_THRESH = {\\n \"I-Lead\": 0.7,\\n \"I-Position\": 0.55,\\n \"I-Evidence\": 0.65,\\n \"I-Claim\": 0.55,\\n \"I-Concluding Statement\": 0.7,\\n \"I-Counterclaim\": 0.5,\\n \"I-Rebuttal\": 0.55,\\n}', \"def set_seed(seed=HyperParameters.random_seed):\\n np.random.seed(seed)\\n \\n random.seed(seed)\\n \\n torch.manual_seed(seed)\\n torch.cuda.manual_seed(seed)\\n \\n torch.backends.cudnn.deterministic =True\\n torch.backends.cudnn.benchmark = False\\n\\nset_seed()\\n\\n# Set proper device\\nif torch.cuda.is_available():\\n device = torch.device('cuda')\\nelse:\\n device = torch.device('cpu')\\n\\nprint(f'Using device: {device}')\", \"df_alltrain = pd.read_csv(f'{HyperParameters.data_dir}/corrected_train.csv')\", 'def agg_essays(train_flg):\\n \"\"\"\\n Splits every word in an essay and adds the text of each essay to a dataframe.\\n \"\"\"\\n folder = \\'train\\' if train_flg else \\'test\\'\\n names, texts =[], []\\n for f in tqdm(list(os.listdir(f\\'{HyperParameters.data_dir}/{folder}\\'))):\\n names.append(f.replace(\\'.txt\\', \\'\\'))\\n texts.append(open(f\\'{HyperParameters.data_dir}/{folder}/\\' + f, \\'r\\').read())\\n df_texts = pd.DataFrame({\\'id\\': names, \\'text\\': texts})\\n\\n df_texts[\\'text_split\\'] = df_texts.text.str.split()\\n print(\\'Completed tokenizing texts.\\')\\n return df_texts', 'def ner(df_texts, df_train):\\n \"\"\"\\n Maps discourse type to each word of the text, according to the train.csv file.\\n \"\"\"\\n all_entities = []\\n for _, row in tqdm(df_texts.iterrows(), total=len(df_texts)):\\n total = len(row[\\'text_split\\'])\\n entities = [\\'O\\'] * total\\n\\n for _, row2 in df_train[df_train[\\'id\\'] == row[\\'id\\']].iterrows():\\n discourse = row2[\\'discourse_type\\']\\n list_ix = [int(x) for x in row2[\\'predictionstring\\'].split(\\' \\')]\\n entities[list_ix[0]] = f\\'B-{discourse}\\'\\n for k in list_ix[1:]: entities[k] = f\\'I-{discourse}\\'\\n all_entities.append(entities)\\n\\n df_texts[\\'entities\\'] = all_entities\\n print(\\'Completed mapping discourse to each token.\\')\\n return df_texts', 'def preprocess(df_train = None):\\n \"\"\"\\n Generates the dataframe we will use for training.\\n Splits essays into words, assigns a token name to each word, and adds everything to a dataframe.\\n \"\"\"\\n if df_train is None:\\n train_flg = False\\n else:\\n train_flg = True\\n \\n df_texts = agg_essays(train_flg)\\n\\n if train_flg:\\n df_texts = ner(df_texts, df_train)\\n return df_texts\\n\\n# Make sure we only run pre-processing if we did not do it in the past:\\n\\nif not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n alltrain_texts = preprocess(df_alltrain)\\n test_texts = preprocess()\\nelse:\\n alltrain_texts = pd.read_csv(f\"{HyperParameters.data_dir}/train_folds.csv\")', \"# Visualize preprocessing result:\\nparse_string = lambda x: [string[1:-1] for string in x[1:-1].split(', ')]\\nalltrain_texts.entities = alltrain_texts.entities.apply(parse_string)\\nalltrain_texts.text_split = alltrain_texts.text_split.apply(parse_string)\\n\\nalltrain_texts.head()\", 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Transform categorical labels to dummy variables. Group by id. Sum over dummy. \\n dfx = pd.get_dummies(df_alltrain, columns=[\"discourse_type\"]).groupby([\"id\"], as_index=False).sum()\\n\\n # Generate name for the dummy columns\\n dummy_cols = [c for c in dfx.columns if c.startswith(\"discourse_type_\") or c == \"id\" and c != \"discourse_type_num\"]\\n # dfx is now only the dataset with dummy columns selected: don\\'t need to pass the data to do the splits\\n dfx = dfx[dummy_cols]', 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Generate cross validation object\\n mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)\\n\\n # Extract labels\\n labels = [c for c in dfx.columns if c != \"id\"]\\n dfx_labels = dfx[labels]\\n\\n # Dummy kfold assignment\\n dfx[\"kfold\"] = -1\\n\\n # Split\\n for fold, (trn_, val_) in enumerate(mskf.split(dfx, dfx_labels)):\\n print(len(trn_), len(val_))\\n \\n # Change the value of the kfold column at the validation index to the value of the fold\\n # This will tell us when to use the current entry in the validation set\\n dfx.loc[val_, \"kfold\"] = fold\\n\\n # merge back to original dataframe\\n alltrain_texts = alltrain_texts.merge(dfx[[\"id\", \"kfold\"]], on=\"id\", how=\"left\")\\n print(alltrain_texts.kfold.value_counts())\\n\\n # Save so next time we import it directly\\n alltrain_texts.to_csv(f\"{HyperParameters.data_dir}/train_folds.csv\", index=False)', \"# need help with this\\nclass FeedbackPrizeDataset(Dataset):\\n def __init__(self, dataframe, tokenizer, max_len, has_labels):\\n self.len = len(dataframe)\\n self.data = dataframe\\n self.tokenizer = tokenizer\\n self.max_len = max_len\\n self.has_labels = has_labels\\n \\n def __getitem__(self, index):\\n text = self.data.text[index]\\n encoding = self.tokenizer(\\n text.split(),\\n is_split_into_words = True,\\n padding = 'max_length',\\n truncation = True,\\n max_length = self.max_len\\n )\\n word_ids = encoding.word_ids()\\n\\n # targets\\n if self.has_labels:\\n word_labels = self.data.entities[index]\\n prev_word_idx = None\\n labels_ids = []\\n for word_idx in word_ids:\\n if word_idx is None:\\n labels_ids.append(IGNORE_INDEX)\\n elif word_idx != prev_word_idx:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n if HyperParameters.label_subtokens:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n labels_ids.append(IGNORE_INDEX)\\n prev_word_idx = word_idx\\n encoding['labels'] = labels_ids\\n # convert to torch.tensor\\n item = {k: torch.as_tensor(v) for k, v in encoding.items()}\\n word_ids2 = [w if w is not None else NON_LABEL for w in word_ids]\\n item['word_ids'] = torch.as_tensor(word_ids2)\\n return item\\n\\n def __len__(self):\\n return self.len\", \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", 'def build_model_tokenizer():\\n tokenizer = AutoTokenizer.from_pretrained(HyperParameters.model_name, add_prefix_space = True)\\n model = FeedbackModel()\\n return model, tokenizer', '# Need help with this: used in training to transform raw logits to labels needed\\ndef active_logits(raw_logits, word_ids):\\n word_ids = word_ids.view(-1)\\n active_mask = word_ids.unsqueeze(1).expand(word_ids.shape[0], HyperParameters.num_labels)\\n active_mask = active_mask != NON_LABEL\\n active_logits = raw_logits.view(-1, HyperParameters.num_labels)\\n active_logits = torch.masked_select(active_logits, active_mask) # return 1dTensor\\n active_logits = active_logits.view(-1, HyperParameters.num_labels) \\n return active_logits\\n\\ndef active_labels(labels):\\n active_mask = labels.view(-1) != IGNORE_INDEX\\n active_labels = torch.masked_select(labels.view(-1), active_mask)\\n return active_labels\\n\\ndef active_preds_prob(active_logits):\\n active_preds = torch.argmax(active_logits, axis = 1)\\n active_preds_prob, _ = torch.max(active_logits, axis = 1)\\n return active_preds, active_preds_prob', 'def calculate_overlap(set_pred, set_gt):\\n \"\"\"\\n Calculates if the overlap between prediction and\\n ground truth is enough fora potential True positive\\n \"\"\"\\n # Length of each and intersection\\n try:\\n len_gt = len(set_gt)\\n len_pred = len(set_pred)\\n inter = len(set_gt & set_pred)\\n overlap_1 = inter / len_gt\\n overlap_2 = inter/ len_pred\\n return overlap_1 >= 0.5 and overlap_2 >= 0.5\\n except: # at least one of the input is NaN\\n return False\\n\\ndef score_feedback_comp_micro(pred_df, gt_df, discourse_type):\\n \"\"\"\\n A function that scores for the kaggle\\n Student Writing Competition\\n \\n Uses the steps in the evaluation page here:\\n https://www.kaggle.com/c/feedback-prize-2021/overview/evaluation\\n \"\"\"\\n gt_df = gt_df.loc[gt_df[\\'discourse_type\\'] == discourse_type, \\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df = pred_df.loc[pred_df[\\'class\\'] == discourse_type,\\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df[\\'pred_id\\'] = pred_df.index\\n gt_df[\\'gt_id\\'] = gt_df.index\\n pred_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in pred_df[\\'predictionstring\\']]\\n gt_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in gt_df[\\'predictionstring\\']]\\n \\n # Step 1. all ground truths and predictions for a given class are compared.\\n joined = pred_df.merge(gt_df,\\n left_on=\\'id\\',\\n right_on=\\'id\\',\\n how=\\'outer\\',\\n suffixes=(\\'_pred\\',\\'_gt\\')\\n )\\n overlaps = [calculate_overlap(*args) for args in zip(joined.predictionstring_pred, \\n joined.predictionstring_gt)]\\n \\n # 2. If the overlap between the ground truth and prediction is >= 0.5, \\n # and the overlap between the prediction and the ground truth >= 0.5,\\n # the prediction is a match and considered a true positive.\\n # If multiple matches exist, the match with the highest pair of overlaps is taken.\\n # we don\\'t need to compute the match to compute the score\\n TP = joined.loc[overlaps][\\'gt_id\\'].nunique()\\n\\n # 3. Any unmatched ground truths are false negatives\\n # and any unmatched predictions are false positives.\\n TPandFP = len(pred_df)\\n TPandFN = len(gt_df)\\n \\n #calc microf1\\n my_f1_score = 2*TP / (TPandFP + TPandFN)\\n return my_f1_score\\n\\ndef score_feedback_comp(pred_df, gt_df, return_class_scores=False):\\n \"\"\"\\n Final helper function for model evaluation.\\n \\n Args:\\n pred_df (pandas.DataFrame): dataframe containing model predictions. Needs to have columns: [\\'id\\',\\'class\\',\\'predictionstring\\']\\n gt_df (pandas.DataFrame): dataframe of ground truth used for model training\\n return_class_scores (bool): Boolean indicating if we want to return the F1 score for each predicted class.\\n \\n Returns:\\n f1 (float): F1 score of the model\\n (optional) class_scores (dict): Dictionary of per-class F1 score\\n \"\"\"\\n class_scores = {}\\n for discourse_type in gt_df.discourse_type.unique():\\n class_score = score_feedback_comp_micro(pred_df, gt_df, discourse_type)\\n class_scores[discourse_type] = class_score\\n f1 = np.mean([v for v in class_scores.values()])\\n if return_class_scores:\\n return f1, class_scores\\n return f1', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n \\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'def valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion):\\n oof, valid_loss, valid_acc = get_preds_onefold(model, df_val, dl_val, criterion, valid_flg=True)\\n f1score =[]\\n # classes = oof[\\'class\\'].unique()\\n classes = [\\'Lead\\', \\'Position\\', \\'Claim\\',\\'Counterclaim\\', \\'Rebuttal\\',\\'Evidence\\',\\'Concluding Statement\\']\\n print(f\"Validation F1 scores\")\\n\\n for c in classes:\\n pred_df = oof.loc[oof[\\'class\\'] == c].copy()\\n gt_df = df_val_eval.loc[df_val_eval[\\'discourse_type\\'] == c].copy()\\n f1 = score_feedback_comp(pred_df, gt_df)\\n print(f\\' * {c:<10}: {f1:4f}\\')\\n f1score.append(f1)\\n f1avg = np.mean(f1score)\\n print(f\\'Overall Validation avg F1: {f1avg:.4f} val_loss:{valid_loss:.4f} val_accuracy:{valid_acc:.4f}\\')\\n return valid_loss, oof', 'def inference(model, data_loader, criterion, valid_flg):\\n stream = tqdm(data_loader)\\n model.eval()\\n \\n valid_loss = 0\\n valid_accuracy = 0\\n all_logits = None\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch[\\'input_ids\\'].to(device, dtype = torch.long)\\n mask = batch[\\'attention_mask\\'].to(device, dtype = torch.long)\\n with torch.no_grad():\\n raw_logits = model(input_ids=ids, mask = mask)\\n del ids, mask\\n \\n word_ids = batch[\\'word_ids\\'].to(device, dtype = torch.long)\\n logits = active_logits(raw_logits, word_ids)\\n sf_logits = torch.softmax(logits, dim= -1)\\n sf_raw_logits = torch.softmax(raw_logits, dim=-1)\\n if valid_flg: \\n raw_labels = batch[\\'labels\\'].to(device, dtype = torch.long)\\n labels = active_labels(raw_labels)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n valid_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n loss = criterion(logits, labels)\\n valid_loss += loss.item()\\n \\n if batch_idx == 1:\\n all_logits = sf_raw_logits.cpu().numpy()\\n else:\\n all_logits = np.append(all_logits, sf_raw_logits.cpu().numpy(), axis=0)\\n\\n \\n if valid_flg: \\n epoch_loss = valid_loss / batch_idx\\n epoch_accuracy = valid_accuracy / batch_idx\\n else:\\n epoch_loss, epoch_accuracy = 0, 0\\n return all_logits, epoch_loss, epoch_accuracy\\n\\n\\ndef preds_class_prob(all_logits, data_loader):\\n print(\"predict target class and its probabilty\")\\n final_predictions = []\\n final_predictions_score = []\\n stream = tqdm(data_loader)\\n len_sample = all_logits.shape[0]\\n\\n for batch_idx, batch in enumerate(stream, start=0):\\n for minibatch_idx in range(HyperParameters.valid_batch_size):\\n sample_idx = int(batch_idx * HyperParameters.valid_batch_size + minibatch_idx)\\n if sample_idx > len_sample - 1 : break\\n word_ids = batch[\\'word_ids\\'][minibatch_idx].numpy()\\n predictions =[]\\n predictions_prob = []\\n pred_class_id = np.argmax(all_logits[sample_idx], axis=1)\\n pred_score = np.max(all_logits[sample_idx], axis=1)\\n pred_class_labels = [IDS_TO_LABELS[i] for i in pred_class_id]\\n prev_word_idx = -1\\n for idx, word_idx in enumerate(word_ids):\\n if word_idx == -1:\\n pass\\n elif word_idx != prev_word_idx:\\n predictions.append(pred_class_labels[idx])\\n predictions_prob.append(pred_score[idx])\\n prev_word_idx = word_idx\\n final_predictions.append(predictions)\\n final_predictions_score.append(predictions_prob)\\n return final_predictions, final_predictions_score', 'def get_preds_onefold(model, df, dl, criterion, valid_flg):\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n all_preds, all_preds_prob = preds_class_prob(logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred, valid_loss, valid_acc\\n\\ndef get_preds_folds(model, df, dl, criterion, valid_flg=False):\\n for i_fold in range(HyperParameters.n_fold):\\n model_filename = os.path.join(HyperParameters.model_dir, f\"{HyperParameters.model_savename}_{i_fold}.bin\")\\n print(f\"{model_filename} inference\")\\n model = model.to(device)\\n model.load_state_dict(torch.load(model_filename))\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n if i_fold == 0:\\n avg_pred_logits = logits\\n else:\\n avg_pred_logits += logits\\n avg_pred_logits /= HyperParameters.n_fold\\n all_preds, all_preds_prob = preds_class_prob(avg_pred_logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred\\n\\ndef post_process_pred(df, all_preds, all_preds_prob):\\n final_preds = []\\n for i in range(len(df)):\\n idx = df.id.values[i]\\n pred = all_preds[i]\\n pred_prob = all_preds_prob[i]\\n j = 0\\n while j < len(pred):\\n cls = pred[j]\\n if cls == \\'O\\': j += 1\\n else: cls = cls.replace(\\'B\\', \\'I\\')\\n end = j + 1\\n while end < len(pred) and pred[end] == cls:\\n end += 1\\n if cls != \\'O\\' and cls !=\\'\\':\\n avg_score = np.mean(pred_prob[j:end])\\n if end - j > MIN_THRESH[cls] and avg_score > PROB_THRESH[cls]:\\n final_preds.append((idx, cls.replace(\\'I-\\', \\'\\'), \\' \\'.join(map(str, list(range(j, end))))))\\n j = end\\n df_pred = pd.DataFrame(final_preds)\\n df_pred.columns = [\\'id\\', \\'class\\', \\'new_predictionstring\\']\\n return df_pred', 'print_gpu_utilization()', 'def pretty_size(size):\\n\\t\"\"\"Pretty prints a torch.Size object\"\"\"\\n\\tassert(isinstance(size, torch.Size))\\n\\treturn \" × \".join(map(str, size))\\n\\ndef dump_tensors(gpu_only=True):\\n\\t\"\"\"Prints a list of the Tensors being tracked by the garbage collector.\"\"\"\\n\\timport gc\\n\\ttotal_size = 0\\n\\tfor obj in gc.get_objects():\\n\\t\\ttry:\\n\\t\\t\\tif torch.is_tensor(obj):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s:%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.numel()\\n\\t\\t\\telif hasattr(obj, \"data\") and torch.is_tensor(obj.data):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s → %s:%s%s%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t type(obj.data).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.data.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" grad\" if obj.requires_grad else \"\", \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" volatile\" if obj.volatile else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.data.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.data.numel()\\n\\t\\texcept Exception as e:\\n\\t\\t\\tpass \\n\\tprint(\"Total size:\", total_size)', 'dump_tensors()', 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', 'try:\\n print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n #if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n print(model_config)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(raw_logits.shape)\\n print(word_ids.shape)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n labels = raw_labels\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n print(gc.get_referrers(model))\\n raise Exception()\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])'], 'Out': {13: id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 }, 'get_ipython': >, 'exit': , 'quit': , '_': id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 , '__': '', '___': '', '_i': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_ii': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n labels = raw_labels\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", '_iii': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i1': 'ON_COLAB = True\\nif ON_COLAB:\\n # Mount drive:\\n from google.colab import drive, files\\n # mount Google Drive\\n drive.mount(\"/content/drive\")', 'ON_COLAB': True, 'drive': , 'files': , '_i2': '%%capture\\n# if on Colab, we need to install missing stuff!\\nif ON_COLAB:\\n !pip install transformers\\n !pip install iterative-stratification\\n !pip install nvidia-ml-py3', '_exit_code': 0, '_i3': 'import gc\\nimport os\\nimport torch\\nimport random\\nimport numpy as np\\nimport pandas as pd\\nimport torch.nn as nn\\nfrom pynvml import nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit\\n\\nfrom tqdm.notebook import tqdm\\nfrom sklearn.metrics import accuracy_score\\nfrom torch.cuda.amp import autocast, GradScaler\\nfrom torch.utils.data import Dataset, DataLoader\\nfrom transformers import AutoConfig, AutoModel, AutoTokenizer\\nfrom iterstrat.ml_stratifiers import MultilabelStratifiedKFold', 'gc': , 'os': , 'torch': , 'random': , 'np': , 'pd': , 'nn': , 'nvmlDeviceGetHandleByIndex': , 'nvmlDeviceGetMemoryInfo': , 'nvmlInit': , 'tqdm': , 'accuracy_score': , 'autocast': , 'GradScaler': , 'Dataset': , 'DataLoader': , 'AutoConfig': , 'AutoModel': , 'AutoTokenizer': , 'MultilabelStratifiedKFold': , '_i4': 'def print_gpu_utilization():\\n print(f\"GPU memory occupied: {get_gpu_utilization()} MB.\")\\n\\ndef get_gpu_utilization():\\n nvmlInit()\\n handle = nvmlDeviceGetHandleByIndex(0)\\n info = nvmlDeviceGetMemoryInfo(handle)\\n return info.used//1024**2\\n\\ndef print_summary(result):\\n print(f\"Time: {result.metrics[\\'train_runtime\\']:.2f}\")\\n print(f\"Samples/second: {result.metrics[\\'train_samples_per_second\\']:.2f}\")\\n print_gpu_utilization()\\n\\nprint_gpu_utilization()', 'print_gpu_utilization': , 'get_gpu_utilization': , 'print_summary': , '_i5': \"if ON_COLAB:\\n !cd /content/drive/MyDrive/NLP_project\\n\\n\\n# DATA DIR ---- TO CHANGE\\nDATA_DIR = 'drive/MyDrive/NLP_project/'\", 'DATA_DIR': 'drive/MyDrive/NLP_project/', '_i6': \"class HyperParameters:\\n \\n # Here we choose model type. Can be changed for others\\n name = 'longformer'\\n model_savename = 'longformer'\\n model_name = 'allenai/longformer-base-4096' # this is the most important: determines what transformer is used in training\\n \\n # Directory hyperparameters: make sure to change with what you are using! Only needed to change here\\n base_dir = DATA_DIR\\n data_dir = os.path.join(base_dir, 'data')\\n pre_data_dir = os.path.join(base_dir, 'data/preprocessed')\\n model_dir = os.path.join(base_dir, f'model/{name}')\\n output_dir = os.path.join(base_dir, f'output/{name}')\\n \\n # Training hyperparameters\\n is_debug = False\\n n_epoch = 2 # not to exceed runtime limit\\n n_fold = 5\\n verbose_steps = 500\\n random_seed = 42\\n\\n # Model specific hyperparameters\\n max_length = 1024\\n inference_max_length = 4096\\n train_batch_size = 4\\n valid_batch_size = 4\\n lr = 4e-5\\n\\n # Task hyperparameters\\n num_labels = 15\\n label_subtokens = True\\n output_hidden_states = True\\n hidden_dropout_prob = 0.1\\n layer_norm_eps = 1e-7\\n add_pooling_layer = False\\n verbose_steps = 500\\n if is_debug:\\n debug_sample = 1000\\n verbose_steps = 16\\n n_epoch = 1\\n n_fold = 2\\n\\nif not os.path.exists(HyperParameters.model_dir):\\n !mkdir $HyperParameters.model_dir\", 'HyperParameters': , '_i7': 'IGNORE_INDEX = -100\\nNON_LABEL = -1\\nOUTPUT_LABELS = [\\'O\\', \\'B-Lead\\', \\'I-Lead\\', \\'B-Position\\', \\'I-Position\\', \\'B-Claim\\', \\'I-Claim\\', \\'B-Counterclaim\\', \\'I-Counterclaim\\', \\n \\'B-Rebuttal\\', \\'I-Rebuttal\\', \\'B-Evidence\\', \\'I-Evidence\\', \\'B-Concluding Statement\\', \\'I-Concluding Statement\\']\\nLABELS_TO_IDS = {v:k for k,v in enumerate(OUTPUT_LABELS)}\\nIDS_TO_LABELS = {k:v for k,v in enumerate(OUTPUT_LABELS)}\\n\\nMIN_THRESH = {\\n \"I-Lead\": 9,\\n \"I-Position\": 5,\\n \"I-Evidence\": 14,\\n \"I-Claim\": 3,\\n \"I-Concluding Statement\": 11,\\n \"I-Counterclaim\": 6,\\n \"I-Rebuttal\": 4,\\n}\\n\\nPROB_THRESH = {\\n \"I-Lead\": 0.7,\\n \"I-Position\": 0.55,\\n \"I-Evidence\": 0.65,\\n \"I-Claim\": 0.55,\\n \"I-Concluding Statement\": 0.7,\\n \"I-Counterclaim\": 0.5,\\n \"I-Rebuttal\": 0.55,\\n}', 'IGNORE_INDEX': -100, 'NON_LABEL': -1, 'OUTPUT_LABELS': ['O', 'B-Lead', 'I-Lead', 'B-Position', 'I-Position', 'B-Claim', 'I-Claim', 'B-Counterclaim', 'I-Counterclaim', 'B-Rebuttal', 'I-Rebuttal', 'B-Evidence', 'I-Evidence', 'B-Concluding Statement', 'I-Concluding Statement'], 'LABELS_TO_IDS': {'O': 0, 'B-Lead': 1, 'I-Lead': 2, 'B-Position': 3, 'I-Position': 4, 'B-Claim': 5, 'I-Claim': 6, 'B-Counterclaim': 7, 'I-Counterclaim': 8, 'B-Rebuttal': 9, 'I-Rebuttal': 10, 'B-Evidence': 11, 'I-Evidence': 12, 'B-Concluding Statement': 13, 'I-Concluding Statement': 14}, 'IDS_TO_LABELS': {0: 'O', 1: 'B-Lead', 2: 'I-Lead', 3: 'B-Position', 4: 'I-Position', 5: 'B-Claim', 6: 'I-Claim', 7: 'B-Counterclaim', 8: 'I-Counterclaim', 9: 'B-Rebuttal', 10: 'I-Rebuttal', 11: 'B-Evidence', 12: 'I-Evidence', 13: 'B-Concluding Statement', 14: 'I-Concluding Statement'}, 'MIN_THRESH': {'I-Lead': 9, 'I-Position': 5, 'I-Evidence': 14, 'I-Claim': 3, 'I-Concluding Statement': 11, 'I-Counterclaim': 6, 'I-Rebuttal': 4}, 'PROB_THRESH': {'I-Lead': 0.7, 'I-Position': 0.55, 'I-Evidence': 0.65, 'I-Claim': 0.55, 'I-Concluding Statement': 0.7, 'I-Counterclaim': 0.5, 'I-Rebuttal': 0.55}, '_i8': \"def set_seed(seed=HyperParameters.random_seed):\\n np.random.seed(seed)\\n \\n random.seed(seed)\\n \\n torch.manual_seed(seed)\\n torch.cuda.manual_seed(seed)\\n \\n torch.backends.cudnn.deterministic =True\\n torch.backends.cudnn.benchmark = False\\n\\nset_seed()\\n\\n# Set proper device\\nif torch.cuda.is_available():\\n device = torch.device('cuda')\\nelse:\\n device = torch.device('cpu')\\n\\nprint(f'Using device: {device}')\", 'set_seed': , 'device': device(type='cuda'), '_i9': \"df_alltrain = pd.read_csv(f'{HyperParameters.data_dir}/corrected_train.csv')\", 'df_alltrain': id discourse_id discourse_start discourse_end \\\n",
+ "0 423A1CA112E2 1.622628e+12 8.0 229.0 \n",
+ "1 423A1CA112E2 1.622628e+12 230.0 312.0 \n",
+ "2 423A1CA112E2 1.622628e+12 313.0 401.0 \n",
+ "3 423A1CA112E2 1.622628e+12 402.0 758.0 \n",
+ "4 423A1CA112E2 1.622628e+12 759.0 886.0 \n",
+ "... ... ... ... ... \n",
+ "144288 4C471936CD75 1.618153e+12 2234.0 3203.0 \n",
+ "144289 4C471936CD75 1.618153e+12 3221.0 4509.0 \n",
+ "144290 4C471936CD75 1.618025e+12 4510.0 4570.0 \n",
+ "144291 4C471936CD75 1.618025e+12 4570.0 4922.0 \n",
+ "144292 4C471936CD75 1.618025e+12 4935.0 5825.0 \n",
+ "\n",
+ " discourse_text \\\n",
+ "0 Modern humans today are always on their phone.... \n",
+ "1 They are some really bad consequences when stu... \n",
+ "2 Some certain areas in the United States ban ph... \n",
+ "3 When people have phones, they know about certa... \n",
+ "4 Driving is one of the way how to get around. P... \n",
+ "... ... \n",
+ "144288 if I'm not sure what college I want to attend... \n",
+ "144289 seeking multiple opinions before making a har... \n",
+ "144290 it is better to seek multiple opinions instead... \n",
+ "144291 The impact of asking people to help you make a... \n",
+ "144292 there are many other reasons one might want to... \n",
+ "\n",
+ " discourse_type discourse_type_num \\\n",
+ "0 Lead Lead 1 \n",
+ "1 Position Position 1 \n",
+ "2 Evidence Evidence 1 \n",
+ "3 Evidence Evidence 2 \n",
+ "4 Claim Claim 1 \n",
+ "... ... ... \n",
+ "144288 Evidence Evidence 2 \n",
+ "144289 Evidence Evidence 3 \n",
+ "144290 Position Position 1 \n",
+ "144291 Evidence Evidence 4 \n",
+ "144292 Concluding Statement Concluding Statement 1 \n",
+ "\n",
+ " predictionstring \\\n",
+ "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... \n",
+ "1 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 \n",
+ "2 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 \n",
+ "3 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9... \n",
+ "4 139 140 141 142 143 144 145 146 147 148 149 15... \n",
+ "... ... \n",
+ "144288 386 387 388 389 390 391 392 393 394 395 396 39... \n",
+ "144289 576 577 578 579 580 581 582 583 584 585 586 58... \n",
+ "144290 828 829 830 831 832 833 834 835 836 837 838 \n",
+ "144291 839 840 841 842 843 844 845 846 847 848 849 85... \n",
+ "144292 905 906 907 908 909 910 911 912 913 914 915 91... \n",
+ "\n",
+ " text_by_index new_start new_end \\\n",
+ "0 Modern humans today are always on their phone.... 8 230 \n",
+ "1 They are some really bad consequences when stu... 230 313 \n",
+ "2 Some certain areas in the United States ban ph... 313 401 \n",
+ "3 When people have phones, they know about certa... 402 758 \n",
+ "4 Driving is one of the way how to get around. P... 759 887 \n",
+ "... ... ... ... \n",
+ "144288 if I'm not sure what college I want to attend... 2235 3203 \n",
+ "144289 seeking multiple opinions before making a har... 3222 4510 \n",
+ "144290 it is better to seek multiple opinions instead... 4510 4570 \n",
+ "144291 The impact of asking people to help you make a... 4570 4923 \n",
+ "144292 there are many other reasons one might want to... 4935 5748 \n",
+ "\n",
+ " text_by_new_index \\\n",
+ "0 Modern humans today are always on their phone.... \n",
+ "1 They are some really bad consequences when stu... \n",
+ "2 Some certain areas in the United States ban ph... \n",
+ "3 When people have phones, they know about certa... \n",
+ "4 Driving is one of the way how to get around. P... \n",
+ "... ... \n",
+ "144288 if I'm not sure what college I want to attend,... \n",
+ "144289 seeking multiple opinions before making a hard... \n",
+ "144290 it is better to seek multiple opinions instead... \n",
+ "144291 The impact of asking people to help you make a... \n",
+ "144292 there are many other reasons one might want to... \n",
+ "\n",
+ " new_predictionstring \n",
+ "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... \n",
+ "1 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 \n",
+ "2 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 \n",
+ "3 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9... \n",
+ "4 139 140 141 142 143 144 145 146 147 148 149 15... \n",
+ "... ... \n",
+ "144288 386 387 388 389 390 391 392 393 394 395 396 39... \n",
+ "144289 576 577 578 579 580 581 582 583 584 585 586 58... \n",
+ "144290 828 829 830 831 832 833 834 835 836 837 838 \n",
+ "144291 839 840 841 842 843 844 845 846 847 848 849 85... \n",
+ "144292 905 906 907 908 909 910 911 912 913 914 915 91... \n",
+ "\n",
+ "[144293 rows x 13 columns], '_i10': 'def agg_essays(train_flg):\\n \"\"\"\\n Splits every word in an essay and adds the text of each essay to a dataframe.\\n \"\"\"\\n folder = \\'train\\' if train_flg else \\'test\\'\\n names, texts =[], []\\n for f in tqdm(list(os.listdir(f\\'{HyperParameters.data_dir}/{folder}\\'))):\\n names.append(f.replace(\\'.txt\\', \\'\\'))\\n texts.append(open(f\\'{HyperParameters.data_dir}/{folder}/\\' + f, \\'r\\').read())\\n df_texts = pd.DataFrame({\\'id\\': names, \\'text\\': texts})\\n\\n df_texts[\\'text_split\\'] = df_texts.text.str.split()\\n print(\\'Completed tokenizing texts.\\')\\n return df_texts', 'agg_essays': , '_i11': 'def ner(df_texts, df_train):\\n \"\"\"\\n Maps discourse type to each word of the text, according to the train.csv file.\\n \"\"\"\\n all_entities = []\\n for _, row in tqdm(df_texts.iterrows(), total=len(df_texts)):\\n total = len(row[\\'text_split\\'])\\n entities = [\\'O\\'] * total\\n\\n for _, row2 in df_train[df_train[\\'id\\'] == row[\\'id\\']].iterrows():\\n discourse = row2[\\'discourse_type\\']\\n list_ix = [int(x) for x in row2[\\'predictionstring\\'].split(\\' \\')]\\n entities[list_ix[0]] = f\\'B-{discourse}\\'\\n for k in list_ix[1:]: entities[k] = f\\'I-{discourse}\\'\\n all_entities.append(entities)\\n\\n df_texts[\\'entities\\'] = all_entities\\n print(\\'Completed mapping discourse to each token.\\')\\n return df_texts', 'ner': , '_i12': 'def preprocess(df_train = None):\\n \"\"\"\\n Generates the dataframe we will use for training.\\n Splits essays into words, assigns a token name to each word, and adds everything to a dataframe.\\n \"\"\"\\n if df_train is None:\\n train_flg = False\\n else:\\n train_flg = True\\n \\n df_texts = agg_essays(train_flg)\\n\\n if train_flg:\\n df_texts = ner(df_texts, df_train)\\n return df_texts\\n\\n# Make sure we only run pre-processing if we did not do it in the past:\\n\\nif not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n alltrain_texts = preprocess(df_alltrain)\\n test_texts = preprocess()\\nelse:\\n alltrain_texts = pd.read_csv(f\"{HyperParameters.data_dir}/train_folds.csv\")', 'preprocess': , 'alltrain_texts': id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "... ... ... \n",
+ "15589 1C899F124FEB While some students may think it's a beneficia... \n",
+ "15590 4453444AF383 There has been a strong arguement going on wea... \n",
+ "15591 EF0D75BF48DA I favor in to changing election by popular vot... \n",
+ "15592 8FFDA5B9D359 Do you think students would benefit from being... \n",
+ "15593 ACAB1FCA0A30 I would like to change the election for the pr... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "... ... \n",
+ "15589 [While, some, students, may, think, it's, a, b... \n",
+ "15590 [There, has, been, a, strong, arguement, going... \n",
+ "15591 [I, favor, in, to, changing, election, by, pop... \n",
+ "15592 [Do, you, think, students, would, benefit, fro... \n",
+ "15593 [I, would, like, to, change, the, election, fo... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 \n",
+ "... ... ... \n",
+ "15589 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "15590 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "15591 [B-Position, I-Position, I-Position, I-Positio... 3 \n",
+ "15592 [B-Position, I-Position, I-Position, I-Positio... 2 \n",
+ "15593 [B-Position, I-Position, I-Position, I-Positio... 4 \n",
+ "\n",
+ "[15594 rows x 5 columns], '_i13': \"# Visualize preprocessing result:\\nparse_string = lambda x: [string[1:-1] for string in x[1:-1].split(', ')]\\nalltrain_texts.entities = alltrain_texts.entities.apply(parse_string)\\nalltrain_texts.text_split = alltrain_texts.text_split.apply(parse_string)\\n\\nalltrain_texts.head()\", 'parse_string': at 0x7fb010272830>, '_13': id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 , '_i14': 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Transform categorical labels to dummy variables. Group by id. Sum over dummy. \\n dfx = pd.get_dummies(df_alltrain, columns=[\"discourse_type\"]).groupby([\"id\"], as_index=False).sum()\\n\\n # Generate name for the dummy columns\\n dummy_cols = [c for c in dfx.columns if c.startswith(\"discourse_type_\") or c == \"id\" and c != \"discourse_type_num\"]\\n # dfx is now only the dataset with dummy columns selected: don\\'t need to pass the data to do the splits\\n dfx = dfx[dummy_cols]', '_i15': 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Generate cross validation object\\n mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)\\n\\n # Extract labels\\n labels = [c for c in dfx.columns if c != \"id\"]\\n dfx_labels = dfx[labels]\\n\\n # Dummy kfold assignment\\n dfx[\"kfold\"] = -1\\n\\n # Split\\n for fold, (trn_, val_) in enumerate(mskf.split(dfx, dfx_labels)):\\n print(len(trn_), len(val_))\\n \\n # Change the value of the kfold column at the validation index to the value of the fold\\n # This will tell us when to use the current entry in the validation set\\n dfx.loc[val_, \"kfold\"] = fold\\n\\n # merge back to original dataframe\\n alltrain_texts = alltrain_texts.merge(dfx[[\"id\", \"kfold\"]], on=\"id\", how=\"left\")\\n print(alltrain_texts.kfold.value_counts())\\n\\n # Save so next time we import it directly\\n alltrain_texts.to_csv(f\"{HyperParameters.data_dir}/train_folds.csv\", index=False)', '_i16': \"# need help with this\\nclass FeedbackPrizeDataset(Dataset):\\n def __init__(self, dataframe, tokenizer, max_len, has_labels):\\n self.len = len(dataframe)\\n self.data = dataframe\\n self.tokenizer = tokenizer\\n self.max_len = max_len\\n self.has_labels = has_labels\\n \\n def __getitem__(self, index):\\n text = self.data.text[index]\\n encoding = self.tokenizer(\\n text.split(),\\n is_split_into_words = True,\\n padding = 'max_length',\\n truncation = True,\\n max_length = self.max_len\\n )\\n word_ids = encoding.word_ids()\\n\\n # targets\\n if self.has_labels:\\n word_labels = self.data.entities[index]\\n prev_word_idx = None\\n labels_ids = []\\n for word_idx in word_ids:\\n if word_idx is None:\\n labels_ids.append(IGNORE_INDEX)\\n elif word_idx != prev_word_idx:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n if HyperParameters.label_subtokens:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n labels_ids.append(IGNORE_INDEX)\\n prev_word_idx = word_idx\\n encoding['labels'] = labels_ids\\n # convert to torch.tensor\\n item = {k: torch.as_tensor(v) for k, v in encoding.items()}\\n word_ids2 = [w if w is not None else NON_LABEL for w in word_ids]\\n item['word_ids'] = torch.as_tensor(word_ids2)\\n return item\\n\\n def __len__(self):\\n return self.len\", 'FeedbackPrizeDataset': , '_i17': \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", 'FeedbackModel': , '_i18': 'def build_model_tokenizer():\\n tokenizer = AutoTokenizer.from_pretrained(HyperParameters.model_name, add_prefix_space = True)\\n model = FeedbackModel()\\n return model, tokenizer', 'build_model_tokenizer': , '_i19': '# Need help with this: used in training to transform raw logits to labels needed\\ndef active_logits(raw_logits, word_ids):\\n word_ids = word_ids.view(-1)\\n active_mask = word_ids.unsqueeze(1).expand(word_ids.shape[0], HyperParameters.num_labels)\\n active_mask = active_mask != NON_LABEL\\n active_logits = raw_logits.view(-1, HyperParameters.num_labels)\\n active_logits = torch.masked_select(active_logits, active_mask) # return 1dTensor\\n active_logits = active_logits.view(-1, HyperParameters.num_labels) \\n return active_logits\\n\\ndef active_labels(labels):\\n active_mask = labels.view(-1) != IGNORE_INDEX\\n active_labels = torch.masked_select(labels.view(-1), active_mask)\\n return active_labels\\n\\ndef active_preds_prob(active_logits):\\n active_preds = torch.argmax(active_logits, axis = 1)\\n active_preds_prob, _ = torch.max(active_logits, axis = 1)\\n return active_preds, active_preds_prob', 'active_logits': , 'active_labels': , 'active_preds_prob': , '_i20': 'def calculate_overlap(set_pred, set_gt):\\n \"\"\"\\n Calculates if the overlap between prediction and\\n ground truth is enough fora potential True positive\\n \"\"\"\\n # Length of each and intersection\\n try:\\n len_gt = len(set_gt)\\n len_pred = len(set_pred)\\n inter = len(set_gt & set_pred)\\n overlap_1 = inter / len_gt\\n overlap_2 = inter/ len_pred\\n return overlap_1 >= 0.5 and overlap_2 >= 0.5\\n except: # at least one of the input is NaN\\n return False\\n\\ndef score_feedback_comp_micro(pred_df, gt_df, discourse_type):\\n \"\"\"\\n A function that scores for the kaggle\\n Student Writing Competition\\n \\n Uses the steps in the evaluation page here:\\n https://www.kaggle.com/c/feedback-prize-2021/overview/evaluation\\n \"\"\"\\n gt_df = gt_df.loc[gt_df[\\'discourse_type\\'] == discourse_type, \\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df = pred_df.loc[pred_df[\\'class\\'] == discourse_type,\\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df[\\'pred_id\\'] = pred_df.index\\n gt_df[\\'gt_id\\'] = gt_df.index\\n pred_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in pred_df[\\'predictionstring\\']]\\n gt_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in gt_df[\\'predictionstring\\']]\\n \\n # Step 1. all ground truths and predictions for a given class are compared.\\n joined = pred_df.merge(gt_df,\\n left_on=\\'id\\',\\n right_on=\\'id\\',\\n how=\\'outer\\',\\n suffixes=(\\'_pred\\',\\'_gt\\')\\n )\\n overlaps = [calculate_overlap(*args) for args in zip(joined.predictionstring_pred, \\n joined.predictionstring_gt)]\\n \\n # 2. If the overlap between the ground truth and prediction is >= 0.5, \\n # and the overlap between the prediction and the ground truth >= 0.5,\\n # the prediction is a match and considered a true positive.\\n # If multiple matches exist, the match with the highest pair of overlaps is taken.\\n # we don\\'t need to compute the match to compute the score\\n TP = joined.loc[overlaps][\\'gt_id\\'].nunique()\\n\\n # 3. Any unmatched ground truths are false negatives\\n # and any unmatched predictions are false positives.\\n TPandFP = len(pred_df)\\n TPandFN = len(gt_df)\\n \\n #calc microf1\\n my_f1_score = 2*TP / (TPandFP + TPandFN)\\n return my_f1_score\\n\\ndef score_feedback_comp(pred_df, gt_df, return_class_scores=False):\\n \"\"\"\\n Final helper function for model evaluation.\\n \\n Args:\\n pred_df (pandas.DataFrame): dataframe containing model predictions. Needs to have columns: [\\'id\\',\\'class\\',\\'predictionstring\\']\\n gt_df (pandas.DataFrame): dataframe of ground truth used for model training\\n return_class_scores (bool): Boolean indicating if we want to return the F1 score for each predicted class.\\n \\n Returns:\\n f1 (float): F1 score of the model\\n (optional) class_scores (dict): Dictionary of per-class F1 score\\n \"\"\"\\n class_scores = {}\\n for discourse_type in gt_df.discourse_type.unique():\\n class_score = score_feedback_comp_micro(pred_df, gt_df, discourse_type)\\n class_scores[discourse_type] = class_score\\n f1 = np.mean([v for v in class_scores.values()])\\n if return_class_scores:\\n return f1, class_scores\\n return f1', 'calculate_overlap': , 'score_feedback_comp_micro': , 'score_feedback_comp': , '_i21': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n \\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'train_fn': , '_i22': 'def valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion):\\n oof, valid_loss, valid_acc = get_preds_onefold(model, df_val, dl_val, criterion, valid_flg=True)\\n f1score =[]\\n # classes = oof[\\'class\\'].unique()\\n classes = [\\'Lead\\', \\'Position\\', \\'Claim\\',\\'Counterclaim\\', \\'Rebuttal\\',\\'Evidence\\',\\'Concluding Statement\\']\\n print(f\"Validation F1 scores\")\\n\\n for c in classes:\\n pred_df = oof.loc[oof[\\'class\\'] == c].copy()\\n gt_df = df_val_eval.loc[df_val_eval[\\'discourse_type\\'] == c].copy()\\n f1 = score_feedback_comp(pred_df, gt_df)\\n print(f\\' * {c:<10}: {f1:4f}\\')\\n f1score.append(f1)\\n f1avg = np.mean(f1score)\\n print(f\\'Overall Validation avg F1: {f1avg:.4f} val_loss:{valid_loss:.4f} val_accuracy:{valid_acc:.4f}\\')\\n return valid_loss, oof', 'valid_fn': , '_i23': 'def inference(model, data_loader, criterion, valid_flg):\\n stream = tqdm(data_loader)\\n model.eval()\\n \\n valid_loss = 0\\n valid_accuracy = 0\\n all_logits = None\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch[\\'input_ids\\'].to(device, dtype = torch.long)\\n mask = batch[\\'attention_mask\\'].to(device, dtype = torch.long)\\n with torch.no_grad():\\n raw_logits = model(input_ids=ids, mask = mask)\\n del ids, mask\\n \\n word_ids = batch[\\'word_ids\\'].to(device, dtype = torch.long)\\n logits = active_logits(raw_logits, word_ids)\\n sf_logits = torch.softmax(logits, dim= -1)\\n sf_raw_logits = torch.softmax(raw_logits, dim=-1)\\n if valid_flg: \\n raw_labels = batch[\\'labels\\'].to(device, dtype = torch.long)\\n labels = active_labels(raw_labels)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n valid_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n loss = criterion(logits, labels)\\n valid_loss += loss.item()\\n \\n if batch_idx == 1:\\n all_logits = sf_raw_logits.cpu().numpy()\\n else:\\n all_logits = np.append(all_logits, sf_raw_logits.cpu().numpy(), axis=0)\\n\\n \\n if valid_flg: \\n epoch_loss = valid_loss / batch_idx\\n epoch_accuracy = valid_accuracy / batch_idx\\n else:\\n epoch_loss, epoch_accuracy = 0, 0\\n return all_logits, epoch_loss, epoch_accuracy\\n\\n\\ndef preds_class_prob(all_logits, data_loader):\\n print(\"predict target class and its probabilty\")\\n final_predictions = []\\n final_predictions_score = []\\n stream = tqdm(data_loader)\\n len_sample = all_logits.shape[0]\\n\\n for batch_idx, batch in enumerate(stream, start=0):\\n for minibatch_idx in range(HyperParameters.valid_batch_size):\\n sample_idx = int(batch_idx * HyperParameters.valid_batch_size + minibatch_idx)\\n if sample_idx > len_sample - 1 : break\\n word_ids = batch[\\'word_ids\\'][minibatch_idx].numpy()\\n predictions =[]\\n predictions_prob = []\\n pred_class_id = np.argmax(all_logits[sample_idx], axis=1)\\n pred_score = np.max(all_logits[sample_idx], axis=1)\\n pred_class_labels = [IDS_TO_LABELS[i] for i in pred_class_id]\\n prev_word_idx = -1\\n for idx, word_idx in enumerate(word_ids):\\n if word_idx == -1:\\n pass\\n elif word_idx != prev_word_idx:\\n predictions.append(pred_class_labels[idx])\\n predictions_prob.append(pred_score[idx])\\n prev_word_idx = word_idx\\n final_predictions.append(predictions)\\n final_predictions_score.append(predictions_prob)\\n return final_predictions, final_predictions_score', 'inference': , 'preds_class_prob': , '_i24': 'def get_preds_onefold(model, df, dl, criterion, valid_flg):\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n all_preds, all_preds_prob = preds_class_prob(logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred, valid_loss, valid_acc\\n\\ndef get_preds_folds(model, df, dl, criterion, valid_flg=False):\\n for i_fold in range(HyperParameters.n_fold):\\n model_filename = os.path.join(HyperParameters.model_dir, f\"{HyperParameters.model_savename}_{i_fold}.bin\")\\n print(f\"{model_filename} inference\")\\n model = model.to(device)\\n model.load_state_dict(torch.load(model_filename))\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n if i_fold == 0:\\n avg_pred_logits = logits\\n else:\\n avg_pred_logits += logits\\n avg_pred_logits /= HyperParameters.n_fold\\n all_preds, all_preds_prob = preds_class_prob(avg_pred_logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred\\n\\ndef post_process_pred(df, all_preds, all_preds_prob):\\n final_preds = []\\n for i in range(len(df)):\\n idx = df.id.values[i]\\n pred = all_preds[i]\\n pred_prob = all_preds_prob[i]\\n j = 0\\n while j < len(pred):\\n cls = pred[j]\\n if cls == \\'O\\': j += 1\\n else: cls = cls.replace(\\'B\\', \\'I\\')\\n end = j + 1\\n while end < len(pred) and pred[end] == cls:\\n end += 1\\n if cls != \\'O\\' and cls !=\\'\\':\\n avg_score = np.mean(pred_prob[j:end])\\n if end - j > MIN_THRESH[cls] and avg_score > PROB_THRESH[cls]:\\n final_preds.append((idx, cls.replace(\\'I-\\', \\'\\'), \\' \\'.join(map(str, list(range(j, end))))))\\n j = end\\n df_pred = pd.DataFrame(final_preds)\\n df_pred.columns = [\\'id\\', \\'class\\', \\'new_predictionstring\\']\\n return df_pred', 'get_preds_onefold': , 'get_preds_folds': , 'post_process_pred': , '_i25': 'print_gpu_utilization()', '_i26': 'def pretty_size(size):\\n\\t\"\"\"Pretty prints a torch.Size object\"\"\"\\n\\tassert(isinstance(size, torch.Size))\\n\\treturn \" × \".join(map(str, size))\\n\\ndef dump_tensors(gpu_only=True):\\n\\t\"\"\"Prints a list of the Tensors being tracked by the garbage collector.\"\"\"\\n\\timport gc\\n\\ttotal_size = 0\\n\\tfor obj in gc.get_objects():\\n\\t\\ttry:\\n\\t\\t\\tif torch.is_tensor(obj):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s:%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.numel()\\n\\t\\t\\telif hasattr(obj, \"data\") and torch.is_tensor(obj.data):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s → %s:%s%s%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t type(obj.data).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.data.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" grad\" if obj.requires_grad else \"\", \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" volatile\" if obj.volatile else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.data.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.data.numel()\\n\\t\\texcept Exception as e:\\n\\t\\t\\tpass \\n\\tprint(\"Total size:\", total_size)', 'pretty_size': , 'dump_tensors': , '_i27': 'dump_tensors()', '_i28': 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'oof': Empty DataFrame\n",
+ "Columns: []\n",
+ "Index: [], 'i_fold': 0, 'tokenizer': PreTrainedTokenizerFast(name_or_path='allenai/longformer-base-4096', vocab_size=50265, model_max_len=4096, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '', 'sep_token': '', 'pad_token': '', 'cls_token': '', 'mask_token': AddedToken(\"\", rstrip=False, lstrip=True, single_word=False, normalized=False)}), 'optimizer': Adam (\n",
+ "Parameter Group 0\n",
+ " amsgrad: False\n",
+ " betas: (0.9, 0.999)\n",
+ " eps: 1e-08\n",
+ " lr: 4e-05\n",
+ " weight_decay: 0\n",
+ "), 'df_train': id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "3 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "4 616F8E0EFABF \"Can you imagine a time in the future when no ... \n",
+ "... ... ... \n",
+ "12472 1C899F124FEB While some students may think it's a beneficia... \n",
+ "12473 4453444AF383 There has been a strong arguement going on wea... \n",
+ "12474 EF0D75BF48DA I favor in to changing election by popular vot... \n",
+ "12475 8FFDA5B9D359 Do you think students would benefit from being... \n",
+ "12476 ACAB1FCA0A30 I would like to change the election for the pr... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [People, sometimes, have, a, different, opinio... \n",
+ "3 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "4 [\"Can, you, imagine, a, time, in, the, future,... \n",
+ "... ... \n",
+ "12472 [While, some, students, may, think, it's, a, b... \n",
+ "12473 [There, has, been, a, strong, arguement, going... \n",
+ "12474 [I, favor, in, to, changing, election, by, pop... \n",
+ "12475 [Do, you, think, students, would, benefit, fro... \n",
+ "12476 [I, would, like, to, change, the, election, fo... \n",
+ "\n",
+ " entities kfold \\\n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "3 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 \n",
+ "4 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 1 \n",
+ "... ... ... \n",
+ "12472 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "12473 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "12474 [B-Position, I-Position, I-Position, I-Positio... 3 \n",
+ "12475 [B-Position, I-Position, I-Position, I-Positio... 2 \n",
+ "12476 [B-Position, I-Position, I-Position, I-Positio... 4 \n",
+ "\n",
+ " labels \n",
+ "0 [1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "1 [0, 0, 0, 0, 0, 0, 0, 0, 2, 9, 9, 9, 9, 9, 9, ... \n",
+ "2 [1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "3 [0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "4 [1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "... ... \n",
+ "12472 [1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "12473 [1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "12474 [2, 9, 9, 9, 9, 9, 9, 9, 9, 4, 11, 11, 11, 11,... \n",
+ "12475 [2, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, ... \n",
+ "12476 [2, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, ... \n",
+ "\n",
+ "[12477 rows x 6 columns], 'ds_train': <__main__.FeedbackPrizeDataset object at 0x7faf5a68b510>, 'df_val': id text \\\n",
+ "0 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "1 F9368F6BAB62 You should join the Seagoing Cowboys program. ... \n",
+ "2 617D56A15483 Do you want to go on daily trips and help peop... \n",
+ "3 D4AA8EC389C6 School Grades:\\n\\nI don't think that it is fai... \n",
+ "4 D92FAEE355E1 Dear Mrs. Principal:\\n\\nI think this new schoo... \n",
+ "... ... ... \n",
+ "3112 3BEFA845FC27 There are people all around the world that mak... \n",
+ "3113 F166DA36A4A3 In this essay im going to prove to you why tha... \n",
+ "3114 8F33151F0334 He joined the programe for many reasons to hel... \n",
+ "3115 DDA2A181F486 I'm against this technology that reads your em... \n",
+ "3116 ACCD71550365 Do you think it is a good idea to succeed? I b... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "1 [You, should, join, the, Seagoing, Cowboys, pr... \n",
+ "2 [Do, you, want, to, go, on, daily, trips, and,... \n",
+ "3 [School, Grades:, I, don't, think, that, it, i... \n",
+ "4 [Dear, Mrs., Principal:, I, think, this, new, ... \n",
+ "... ... \n",
+ "3112 [There, are, people, all, around, the, world, ... \n",
+ "3113 [In, this, essay, im, going, to, prove, to, yo... \n",
+ "3114 [He, joined, the, programe, for, many, reasons... \n",
+ "3115 [I'm, against, this, technology, that, reads, ... \n",
+ "3116 [Do, you, think, it, is, a, good, idea, to, su... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "1 [B-Position, I-Position, I-Position, I-Positio... 0 \n",
+ "2 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 0 \n",
+ "3 [O, O, B-Position, I-Position, I-Position, I-P... 0 \n",
+ "4 [O, O, O, B-Position, I-Position, I-Position, ... 0 \n",
+ "... ... ... \n",
+ "3112 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 0 \n",
+ "3113 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 0 \n",
+ "3114 [B-Evidence, I-Evidence, I-Evidence, I-Evidenc... 0 \n",
+ "3115 [B-Position, I-Position, I-Position, I-Positio... 0 \n",
+ "3116 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 0 \n",
+ "\n",
+ "[3117 rows x 5 columns], 'val_idlist': ['2E4AFCD3987F', 'F9368F6BAB62', '617D56A15483', 'D4AA8EC389C6', 'D92FAEE355E1', '58EE193B3F7D', '4A2693C68175', '3741D275F8BC', 'FC9E2A38AFBB', '8B5CB2AA72E9', '2DEC8D3BA891', '217A7429341D', '7E558B4E7009', 'CC38587E9634', '8DD596F5F5B5', '632C8DA9E2B1', '76C8FE4FB3D8', 'D7D66E258830', 'C20011D6FA9D', '508DB90F68C7', '1655FBD1D22A', '0DE6D016C7FC', '1D07A65F9B7F', '0257D9E42247', '31DB1BDE89A1', '13FB62D377E3', '33C081EC2A59', '90691C2B3D5A', '6D6215C23989', '7FB898313EA9', 'BCAE5B0783CF', 'C5C81327D129', '5942FB27D5C3', '185FD725317F', 'ACBC86CD65B4', '027616F7ED1D', '6068A54ED99D', 'B75E90DC14B3', 'BA545022DFCD', '2D654BBA9310', 'F53C55BBD326', '0821EB1AEDED', '7F74864D0865', 'DCDBC5C7873F', 'EA9FA5DA65F3', '486B53D5F8A0', '1CCEB6CD629D', '1F8DDCFCA766', 'DBCC1F8F92B5', '51E211998DF0', 'D447CD5A0C67', 'C50FE04C4D29', '32B2D5592E1D', '2962D1BB17A2', '59E8C8BE4ACA', '155F508445E2', 'C3F35CEC4B5A', 'B5113469D13C', 'E09064F496DF', 'E5EF52219F63', 'C43C4EE10535', '2DFFB145BE07', '2F3210D23414', '084DC646D8E3', '2B002FEE337E', '8DB4751C05A1', '8C7464101130', '3752D33FDE11', 'F3CB6601CF24', 'FE1DEACABE37', 'BF0241C10324', '8F91417B8A7D', '9DBB8B0C3367', '69891F080D3A', 'EE63F23F10B3', 'D7C33D289E26', '325B922888A7', '3A08788DCA7D', 'CFA4E76C506A', 'B3C2E6274311', '7F77FE73B338', 'E1130AA34EE8', 'CFB99F0D0D31', '3712A5E56380', '1B1FBDE25C2C', '6D6B05E5478D', 'D1673363C56A', '1BA91BC3D89A', 'D5724EDF0DBA', '5FFC25FE5E00', '6ED00F0E1BFE', '4C0EB60473E1', 'F605023B87A7', '2617310033A4', 'D284AB2165C8', '29B80B7D1092', 'EA205A51D32C', '5137514F130A', '0C0A8EBDC931', 'BF5D82D486F5', '4A8AC603C9C5', '3188A9E2D162', '92887B4B5F2A', 'AE844BA96314', '21A162D4B0CB', '4390794B2751', '46E97FDCA469', 'B5838ACCDBA5', '318B9B177D68', '41A159819E12', '29BE597866EE', '9396FCEB118A', '48FB3005F4DC', '8134AD7791FC', '8E938A38BA43', '220A26E7E802', '04ACF5873DE9', 'ED4186119E60', '1CC9A9598942', '9A5D8C898877', 'D6466B4502D5', 'B60A2DC4B93C', '7ECB7FDA805A', '3AFA78FFFC15', '611228818239', '619D1D7FDC04', '85255F76A261', '94CCB4234EAE', '971D0C1C75F0', '710A2280E20F', '7B64BC831CD9', 'C9DA3BE5EB24', '479F6A965C8C', 'D24777F39289', 'D7946F0C2A83', '65590D44B68A', '2B4A794ED0D8', '242E9BA55C0C', 'E3F3C192B3A2', '61671FCB6624', '3F8C4D2CA16E', '2E9B5CD4716D', 'D022656B1A36', 'D5CB4FD63313', 'D66B89FC3757', 'D7401AE3A016', 'AC7C84C2A13E', '727DE3CD0AEC', '7C1FF9F7C5C8', '2A7C4A1CBA41', 'BA18C6F80423', '23C6ED53A179', 'C05809232F0E', 'C8D3EB90EB29', '4AD3DB40F5D3', '46284DF2F31C', 'AF34B1CDD152', 'A6CC10322C3A', '0DBE82E9933F', 'F5A0BD46D3AF', '3CA61DE9B5C6', '899E6252BD6F', 'EA5E9342C547', '89CD5C5D9180', 'FE47325AD93F', '6CD4FAAB7653', 'F503C7DC334F', '870D9B35F20A', '37FC9DB2D1DB', '8C9A286F5351', '7AD4CB4029C0', 'BAEE45819FA1', '7BBC565EFE33', 'F44F49940938', 'E7576760695E', '116FB053BEC7', '5EF4174A5264', 'B651AEF954C3', '06E3595C94BF', '0054850878E3', '7091F3705B94', 'FC90D46FBE1F', 'DE1EF5A9F3AA', 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'5B1604AE437E', '8A1A6161F47C', '27DE9583F170', '07C7A31386A0', 'B7C3128E0973', '39B6908F453E', 'D2E2D4CCBF8C', 'CFE773F1600B', '527EB3C89F2F', 'A66B88F03338', 'B936994E140D', '497079209629', '102F13317092', 'C4ED4217DA09', '45BF01B7EF4D', '3BEFA845FC27', 'F166DA36A4A3', '8F33151F0334', 'DDA2A181F486', 'ACCD71550365'], 'df_val_eval': id discourse_id discourse_start discourse_end \\\n",
+ "0 A8445CABFECE 1.622576e+12 18.0 85.0 \n",
+ "1 A8445CABFECE 1.622576e+12 86.0 202.0 \n",
+ "2 A8445CABFECE 1.622576e+12 203.0 1030.0 \n",
+ "3 A8445CABFECE 1.622576e+12 1031.0 1243.0 \n",
+ "4 A97DE0D49AEA 1.622645e+12 63.0 129.0 \n",
+ "... ... ... ... ... \n",
+ "28992 0814426B27DF 1.617896e+12 1440.0 1563.0 \n",
+ "28993 0814426B27DF 1.617896e+12 1564.0 1955.0 \n",
+ "28994 0814426B27DF 1.617896e+12 1956.0 2003.0 \n",
+ "28995 0814426B27DF 1.617896e+12 2004.0 2075.0 \n",
+ "28996 0814426B27DF 1.617896e+12 2076.0 2359.0 \n",
+ "\n",
+ " discourse_text \\\n",
+ "0 Drivers should not be able to use phones while... \n",
+ "1 Drivers who used their phone while operating a... \n",
+ "2 According to an article by the Edgar Snyder Fi... \n",
+ "3 In conclusion, drivers should not able to work... \n",
+ "4 Driver's should desist from using their Cell ... \n",
+ "... ... \n",
+ "28992 that one of the people you asked for advice ma... \n",
+ "28993 Some don't think all the same things if you we... \n",
+ "28994 Some people will disagree with my three reasons \n",
+ "28995 but I don't like to listen to the people who d... \n",
+ "28996 Maybe this helped you maybe it didn't.\\n\\nTo s... \n",
+ "\n",
+ " discourse_type discourse_type_num \\\n",
+ "0 Position Position 1 \n",
+ "1 Claim Claim 1 \n",
+ "2 Evidence Evidence 1 \n",
+ "3 Concluding Statement Concluding Statement 1 \n",
+ "4 Position Position 1 \n",
+ "... ... ... \n",
+ "28992 Claim Claim 5 \n",
+ "28993 Evidence Evidence 3 \n",
+ "28994 Counterclaim Counterclaim 1 \n",
+ "28995 Rebuttal Rebuttal 1 \n",
+ "28996 Concluding Statement Concluding Statement 1 \n",
+ "\n",
+ " predictionstring \\\n",
+ "0 3 4 5 6 7 8 9 10 11 12 13 14 \n",
+ "1 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 3... \n",
+ "2 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 5... \n",
+ "3 177 178 179 180 181 182 183 184 185 186 187 18... \n",
+ "4 11 12 13 14 15 16 17 18 19 20 \n",
+ "... ... \n",
+ "28992 290 291 292 293 294 295 296 297 298 299 300 30... \n",
+ "28993 315 316 317 318 319 320 321 322 323 324 325 32... \n",
+ "28994 391 392 393 394 395 396 397 398 \n",
+ "28995 399 400 401 402 403 404 405 406 407 408 409 41... \n",
+ "28996 413 414 415 416 417 418 419 420 421 422 423 42... \n",
+ "\n",
+ " text_by_index new_start new_end \\\n",
+ "0 Drivers should not be able to use phones while... 18 86 \n",
+ "1 Drivers who used their phone while operating a... 86 202 \n",
+ "2 According to an article by the Edgar Snyder Fi... 203 1030 \n",
+ "3 In conclusion, drivers should not able to work... 1031 1231 \n",
+ "4 Driver's should desist from using their Cell ... 64 129 \n",
+ "... ... ... ... \n",
+ "28992 that one of the people you asked for advice ma... 1440 1563 \n",
+ "28993 Some don't think all the same things if you we... 1564 1955 \n",
+ "28994 Some people will disagree with my three reasons 1956 2004 \n",
+ "28995 but I don't like to listen to the people who d... 2004 2075 \n",
+ "28996 Maybe this helped you maybe it didn't.\\n\\nTo s... 2076 2359 \n",
+ "\n",
+ " text_by_new_index \\\n",
+ "0 Drivers should not be able to use phones while... \n",
+ "1 Drivers who used their phone while operating a... \n",
+ "2 According to an article by the Edgar Snyder Fi... \n",
+ "3 In conclusion, drivers should not able to work... \n",
+ "4 Driver's should desist from using their Cell P... \n",
+ "... ... \n",
+ "28992 that one of the people you asked for advice ma... \n",
+ "28993 Some don't think all the same things if you we... \n",
+ "28994 Some people will disagree with my three reasons \n",
+ "28995 but I don't like to listen to the people who d... \n",
+ "28996 Maybe this helped you maybe it didn't.\\n\\nTo s... \n",
+ "\n",
+ " new_predictionstring \n",
+ "0 3 4 5 6 7 8 9 10 11 12 13 14 \n",
+ "1 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 3... \n",
+ "2 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 5... \n",
+ "3 177 178 179 180 181 182 183 184 185 186 187 18... \n",
+ "4 11 12 13 14 15 16 17 18 19 20 \n",
+ "... ... \n",
+ "28992 290 291 292 293 294 295 296 297 298 299 300 30... \n",
+ "28993 315 316 317 318 319 320 321 322 323 324 325 32... \n",
+ "28994 391 392 393 394 395 396 397 398 \n",
+ "28995 399 400 401 402 403 404 405 406 407 408 409 41... \n",
+ "28996 413 414 415 416 417 418 419 420 421 422 423 42... \n",
+ "\n",
+ "[28997 rows x 13 columns], 'ds_val': <__main__.FeedbackPrizeDataset object at 0x7faf5a76b410>, 'dl_train': , 'dl_val': , 'best_val_loss': inf, 'criterion': CrossEntropyLoss(), 'epoch': 1, '_i29': 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i30': 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', 'KaggleDataset': , '_i31': 'try:\\n print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i32': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i33': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'MAPPING': {'O': 0, 'B-Lead': 1, 'B-Position': 2, 'B-Evidence': 3, 'B-Claim': 4, 'B-Concluding Statement': 5, 'B-Counterclaim': 6, 'B-Rebuttal': 7, 'I-Lead': 8, 'I-Position': 9, 'I-Evidence': 10, 'I-Claim': 11, 'I-Concluding Statement': 12, 'I-Counterclaim': 13, 'I-Rebuttal': 14}, '_i34': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i35': 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n #if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', '_i36': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i37': \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n print(model_config)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", '_i38': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i39': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i40': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(raw_logits.shape)\\n print(word_ids.shape)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", '_i41': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", '_i42': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i43': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", '_i44': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i45': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n labels = raw_labels\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", '_i46': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'model': FeedbackModel(\n",
+ " (backbone): LongformerModel(\n",
+ " (embeddings): LongformerEmbeddings(\n",
+ " (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
+ " (position_embeddings): Embedding(4098, 768, padding_idx=1)\n",
+ " (token_type_embeddings): Embedding(1, 768)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (encoder): LongformerEncoder(\n",
+ " (layer): ModuleList(\n",
+ " (0): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (1): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (2): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
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+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (3): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
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+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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+ " )\n",
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+ " (4): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
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+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
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+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
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+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (5): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (6): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (7): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (8): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (9): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (10): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (11): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (pooler): LongformerPooler(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (activation): Tanh()\n",
+ " )\n",
+ " )\n",
+ " (dropout1): Dropout(p=0.1, inplace=False)\n",
+ " (dropout2): Dropout(p=0.2, inplace=False)\n",
+ " (dropout3): Dropout(p=0.3, inplace=False)\n",
+ " (dropout4): Dropout(p=0.4, inplace=False)\n",
+ " (dropout5): Dropout(p=0.5, inplace=False)\n",
+ " (head): Linear(in_features=768, out_features=15, bias=True)\n",
+ "), '_i47': 'try:\\n print(gc.get_referrers(model))\\n raise Exception()\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])'}, {'__name__': '__main__', '__doc__': 'Automatically created module for IPython interactive environment', '__package__': None, '__loader__': None, '__spec__': None, '__builtin__': , '__builtins__': , '_ih': ['', 'ON_COLAB = True\\nif ON_COLAB:\\n # Mount drive:\\n from google.colab import drive, files\\n # mount Google Drive\\n drive.mount(\"/content/drive\")', \"get_ipython().run_cell_magic('capture', '', '# if on Colab, we need to install missing stuff!\\\\nif ON_COLAB:\\\\n !pip install transformers\\\\n !pip install iterative-stratification\\\\n !pip install nvidia-ml-py3')\", 'import gc\\nimport os\\nimport torch\\nimport random\\nimport numpy as np\\nimport pandas as pd\\nimport torch.nn as nn\\nfrom pynvml import nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit\\n\\nfrom tqdm.notebook import tqdm\\nfrom sklearn.metrics import accuracy_score\\nfrom torch.cuda.amp import autocast, GradScaler\\nfrom torch.utils.data import Dataset, DataLoader\\nfrom transformers import AutoConfig, AutoModel, AutoTokenizer\\nfrom iterstrat.ml_stratifiers import MultilabelStratifiedKFold', 'def print_gpu_utilization():\\n print(f\"GPU memory occupied: {get_gpu_utilization()} MB.\")\\n\\ndef get_gpu_utilization():\\n nvmlInit()\\n handle = nvmlDeviceGetHandleByIndex(0)\\n info = nvmlDeviceGetMemoryInfo(handle)\\n return info.used//1024**2\\n\\ndef print_summary(result):\\n print(f\"Time: {result.metrics[\\'train_runtime\\']:.2f}\")\\n print(f\"Samples/second: {result.metrics[\\'train_samples_per_second\\']:.2f}\")\\n print_gpu_utilization()\\n\\nprint_gpu_utilization()', \"if ON_COLAB:\\n get_ipython().system('cd /content/drive/MyDrive/NLP_project')\\n\\n\\n# DATA DIR ---- TO CHANGE\\nDATA_DIR = 'drive/MyDrive/NLP_project/'\", \"class HyperParameters:\\n \\n # Here we choose model type. Can be changed for others\\n name = 'longformer'\\n model_savename = 'longformer'\\n model_name = 'allenai/longformer-base-4096' # this is the most important: determines what transformer is used in training\\n \\n # Directory hyperparameters: make sure to change with what you are using! Only needed to change here\\n base_dir = DATA_DIR\\n data_dir = os.path.join(base_dir, 'data')\\n pre_data_dir = os.path.join(base_dir, 'data/preprocessed')\\n model_dir = os.path.join(base_dir, f'model/{name}')\\n output_dir = os.path.join(base_dir, f'output/{name}')\\n \\n # Training hyperparameters\\n is_debug = False\\n n_epoch = 2 # not to exceed runtime limit\\n n_fold = 5\\n verbose_steps = 500\\n random_seed = 42\\n\\n # Model specific hyperparameters\\n max_length = 1024\\n inference_max_length = 4096\\n train_batch_size = 4\\n valid_batch_size = 4\\n lr = 4e-5\\n\\n # Task hyperparameters\\n num_labels = 15\\n label_subtokens = True\\n output_hidden_states = True\\n hidden_dropout_prob = 0.1\\n layer_norm_eps = 1e-7\\n add_pooling_layer = False\\n verbose_steps = 500\\n if is_debug:\\n debug_sample = 1000\\n verbose_steps = 16\\n n_epoch = 1\\n n_fold = 2\\n\\nif not os.path.exists(HyperParameters.model_dir):\\n get_ipython().system('mkdir $HyperParameters.model_dir')\", 'IGNORE_INDEX = -100\\nNON_LABEL = -1\\nOUTPUT_LABELS = [\\'O\\', \\'B-Lead\\', \\'I-Lead\\', \\'B-Position\\', \\'I-Position\\', \\'B-Claim\\', \\'I-Claim\\', \\'B-Counterclaim\\', \\'I-Counterclaim\\', \\n \\'B-Rebuttal\\', \\'I-Rebuttal\\', \\'B-Evidence\\', \\'I-Evidence\\', \\'B-Concluding Statement\\', \\'I-Concluding Statement\\']\\nLABELS_TO_IDS = {v:k for k,v in enumerate(OUTPUT_LABELS)}\\nIDS_TO_LABELS = {k:v for k,v in enumerate(OUTPUT_LABELS)}\\n\\nMIN_THRESH = {\\n \"I-Lead\": 9,\\n \"I-Position\": 5,\\n \"I-Evidence\": 14,\\n \"I-Claim\": 3,\\n \"I-Concluding Statement\": 11,\\n \"I-Counterclaim\": 6,\\n \"I-Rebuttal\": 4,\\n}\\n\\nPROB_THRESH = {\\n \"I-Lead\": 0.7,\\n \"I-Position\": 0.55,\\n \"I-Evidence\": 0.65,\\n \"I-Claim\": 0.55,\\n \"I-Concluding Statement\": 0.7,\\n \"I-Counterclaim\": 0.5,\\n \"I-Rebuttal\": 0.55,\\n}', \"def set_seed(seed=HyperParameters.random_seed):\\n np.random.seed(seed)\\n \\n random.seed(seed)\\n \\n torch.manual_seed(seed)\\n torch.cuda.manual_seed(seed)\\n \\n torch.backends.cudnn.deterministic =True\\n torch.backends.cudnn.benchmark = False\\n\\nset_seed()\\n\\n# Set proper device\\nif torch.cuda.is_available():\\n device = torch.device('cuda')\\nelse:\\n device = torch.device('cpu')\\n\\nprint(f'Using device: {device}')\", \"df_alltrain = pd.read_csv(f'{HyperParameters.data_dir}/corrected_train.csv')\", 'def agg_essays(train_flg):\\n \"\"\"\\n Splits every word in an essay and adds the text of each essay to a dataframe.\\n \"\"\"\\n folder = \\'train\\' if train_flg else \\'test\\'\\n names, texts =[], []\\n for f in tqdm(list(os.listdir(f\\'{HyperParameters.data_dir}/{folder}\\'))):\\n names.append(f.replace(\\'.txt\\', \\'\\'))\\n texts.append(open(f\\'{HyperParameters.data_dir}/{folder}/\\' + f, \\'r\\').read())\\n df_texts = pd.DataFrame({\\'id\\': names, \\'text\\': texts})\\n\\n df_texts[\\'text_split\\'] = df_texts.text.str.split()\\n print(\\'Completed tokenizing texts.\\')\\n return df_texts', 'def ner(df_texts, df_train):\\n \"\"\"\\n Maps discourse type to each word of the text, according to the train.csv file.\\n \"\"\"\\n all_entities = []\\n for _, row in tqdm(df_texts.iterrows(), total=len(df_texts)):\\n total = len(row[\\'text_split\\'])\\n entities = [\\'O\\'] * total\\n\\n for _, row2 in df_train[df_train[\\'id\\'] == row[\\'id\\']].iterrows():\\n discourse = row2[\\'discourse_type\\']\\n list_ix = [int(x) for x in row2[\\'predictionstring\\'].split(\\' \\')]\\n entities[list_ix[0]] = f\\'B-{discourse}\\'\\n for k in list_ix[1:]: entities[k] = f\\'I-{discourse}\\'\\n all_entities.append(entities)\\n\\n df_texts[\\'entities\\'] = all_entities\\n print(\\'Completed mapping discourse to each token.\\')\\n return df_texts', 'def preprocess(df_train = None):\\n \"\"\"\\n Generates the dataframe we will use for training.\\n Splits essays into words, assigns a token name to each word, and adds everything to a dataframe.\\n \"\"\"\\n if df_train is None:\\n train_flg = False\\n else:\\n train_flg = True\\n \\n df_texts = agg_essays(train_flg)\\n\\n if train_flg:\\n df_texts = ner(df_texts, df_train)\\n return df_texts\\n\\n# Make sure we only run pre-processing if we did not do it in the past:\\n\\nif not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n alltrain_texts = preprocess(df_alltrain)\\n test_texts = preprocess()\\nelse:\\n alltrain_texts = pd.read_csv(f\"{HyperParameters.data_dir}/train_folds.csv\")', \"# Visualize preprocessing result:\\nparse_string = lambda x: [string[1:-1] for string in x[1:-1].split(', ')]\\nalltrain_texts.entities = alltrain_texts.entities.apply(parse_string)\\nalltrain_texts.text_split = alltrain_texts.text_split.apply(parse_string)\\n\\nalltrain_texts.head()\", 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Transform categorical labels to dummy variables. Group by id. Sum over dummy. \\n dfx = pd.get_dummies(df_alltrain, columns=[\"discourse_type\"]).groupby([\"id\"], as_index=False).sum()\\n\\n # Generate name for the dummy columns\\n dummy_cols = [c for c in dfx.columns if c.startswith(\"discourse_type_\") or c == \"id\" and c != \"discourse_type_num\"]\\n # dfx is now only the dataset with dummy columns selected: don\\'t need to pass the data to do the splits\\n dfx = dfx[dummy_cols]', 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Generate cross validation object\\n mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)\\n\\n # Extract labels\\n labels = [c for c in dfx.columns if c != \"id\"]\\n dfx_labels = dfx[labels]\\n\\n # Dummy kfold assignment\\n dfx[\"kfold\"] = -1\\n\\n # Split\\n for fold, (trn_, val_) in enumerate(mskf.split(dfx, dfx_labels)):\\n print(len(trn_), len(val_))\\n \\n # Change the value of the kfold column at the validation index to the value of the fold\\n # This will tell us when to use the current entry in the validation set\\n dfx.loc[val_, \"kfold\"] = fold\\n\\n # merge back to original dataframe\\n alltrain_texts = alltrain_texts.merge(dfx[[\"id\", \"kfold\"]], on=\"id\", how=\"left\")\\n print(alltrain_texts.kfold.value_counts())\\n\\n # Save so next time we import it directly\\n alltrain_texts.to_csv(f\"{HyperParameters.data_dir}/train_folds.csv\", index=False)', \"# need help with this\\nclass FeedbackPrizeDataset(Dataset):\\n def __init__(self, dataframe, tokenizer, max_len, has_labels):\\n self.len = len(dataframe)\\n self.data = dataframe\\n self.tokenizer = tokenizer\\n self.max_len = max_len\\n self.has_labels = has_labels\\n \\n def __getitem__(self, index):\\n text = self.data.text[index]\\n encoding = self.tokenizer(\\n text.split(),\\n is_split_into_words = True,\\n padding = 'max_length',\\n truncation = True,\\n max_length = self.max_len\\n )\\n word_ids = encoding.word_ids()\\n\\n # targets\\n if self.has_labels:\\n word_labels = self.data.entities[index]\\n prev_word_idx = None\\n labels_ids = []\\n for word_idx in word_ids:\\n if word_idx is None:\\n labels_ids.append(IGNORE_INDEX)\\n elif word_idx != prev_word_idx:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n if HyperParameters.label_subtokens:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n labels_ids.append(IGNORE_INDEX)\\n prev_word_idx = word_idx\\n encoding['labels'] = labels_ids\\n # convert to torch.tensor\\n item = {k: torch.as_tensor(v) for k, v in encoding.items()}\\n word_ids2 = [w if w is not None else NON_LABEL for w in word_ids]\\n item['word_ids'] = torch.as_tensor(word_ids2)\\n return item\\n\\n def __len__(self):\\n return self.len\", \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", 'def build_model_tokenizer():\\n tokenizer = AutoTokenizer.from_pretrained(HyperParameters.model_name, add_prefix_space = True)\\n model = FeedbackModel()\\n return model, tokenizer', '# Need help with this: used in training to transform raw logits to labels needed\\ndef active_logits(raw_logits, word_ids):\\n word_ids = word_ids.view(-1)\\n active_mask = word_ids.unsqueeze(1).expand(word_ids.shape[0], HyperParameters.num_labels)\\n active_mask = active_mask != NON_LABEL\\n active_logits = raw_logits.view(-1, HyperParameters.num_labels)\\n active_logits = torch.masked_select(active_logits, active_mask) # return 1dTensor\\n active_logits = active_logits.view(-1, HyperParameters.num_labels) \\n return active_logits\\n\\ndef active_labels(labels):\\n active_mask = labels.view(-1) != IGNORE_INDEX\\n active_labels = torch.masked_select(labels.view(-1), active_mask)\\n return active_labels\\n\\ndef active_preds_prob(active_logits):\\n active_preds = torch.argmax(active_logits, axis = 1)\\n active_preds_prob, _ = torch.max(active_logits, axis = 1)\\n return active_preds, active_preds_prob', 'def calculate_overlap(set_pred, set_gt):\\n \"\"\"\\n Calculates if the overlap between prediction and\\n ground truth is enough fora potential True positive\\n \"\"\"\\n # Length of each and intersection\\n try:\\n len_gt = len(set_gt)\\n len_pred = len(set_pred)\\n inter = len(set_gt & set_pred)\\n overlap_1 = inter / len_gt\\n overlap_2 = inter/ len_pred\\n return overlap_1 >= 0.5 and overlap_2 >= 0.5\\n except: # at least one of the input is NaN\\n return False\\n\\ndef score_feedback_comp_micro(pred_df, gt_df, discourse_type):\\n \"\"\"\\n A function that scores for the kaggle\\n Student Writing Competition\\n \\n Uses the steps in the evaluation page here:\\n https://www.kaggle.com/c/feedback-prize-2021/overview/evaluation\\n \"\"\"\\n gt_df = gt_df.loc[gt_df[\\'discourse_type\\'] == discourse_type, \\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df = pred_df.loc[pred_df[\\'class\\'] == discourse_type,\\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df[\\'pred_id\\'] = pred_df.index\\n gt_df[\\'gt_id\\'] = gt_df.index\\n pred_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in pred_df[\\'predictionstring\\']]\\n gt_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in gt_df[\\'predictionstring\\']]\\n \\n # Step 1. all ground truths and predictions for a given class are compared.\\n joined = pred_df.merge(gt_df,\\n left_on=\\'id\\',\\n right_on=\\'id\\',\\n how=\\'outer\\',\\n suffixes=(\\'_pred\\',\\'_gt\\')\\n )\\n overlaps = [calculate_overlap(*args) for args in zip(joined.predictionstring_pred, \\n joined.predictionstring_gt)]\\n \\n # 2. If the overlap between the ground truth and prediction is >= 0.5, \\n # and the overlap between the prediction and the ground truth >= 0.5,\\n # the prediction is a match and considered a true positive.\\n # If multiple matches exist, the match with the highest pair of overlaps is taken.\\n # we don\\'t need to compute the match to compute the score\\n TP = joined.loc[overlaps][\\'gt_id\\'].nunique()\\n\\n # 3. Any unmatched ground truths are false negatives\\n # and any unmatched predictions are false positives.\\n TPandFP = len(pred_df)\\n TPandFN = len(gt_df)\\n \\n #calc microf1\\n my_f1_score = 2*TP / (TPandFP + TPandFN)\\n return my_f1_score\\n\\ndef score_feedback_comp(pred_df, gt_df, return_class_scores=False):\\n \"\"\"\\n Final helper function for model evaluation.\\n \\n Args:\\n pred_df (pandas.DataFrame): dataframe containing model predictions. Needs to have columns: [\\'id\\',\\'class\\',\\'predictionstring\\']\\n gt_df (pandas.DataFrame): dataframe of ground truth used for model training\\n return_class_scores (bool): Boolean indicating if we want to return the F1 score for each predicted class.\\n \\n Returns:\\n f1 (float): F1 score of the model\\n (optional) class_scores (dict): Dictionary of per-class F1 score\\n \"\"\"\\n class_scores = {}\\n for discourse_type in gt_df.discourse_type.unique():\\n class_score = score_feedback_comp_micro(pred_df, gt_df, discourse_type)\\n class_scores[discourse_type] = class_score\\n f1 = np.mean([v for v in class_scores.values()])\\n if return_class_scores:\\n return f1, class_scores\\n return f1', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n \\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'def valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion):\\n oof, valid_loss, valid_acc = get_preds_onefold(model, df_val, dl_val, criterion, valid_flg=True)\\n f1score =[]\\n # classes = oof[\\'class\\'].unique()\\n classes = [\\'Lead\\', \\'Position\\', \\'Claim\\',\\'Counterclaim\\', \\'Rebuttal\\',\\'Evidence\\',\\'Concluding Statement\\']\\n print(f\"Validation F1 scores\")\\n\\n for c in classes:\\n pred_df = oof.loc[oof[\\'class\\'] == c].copy()\\n gt_df = df_val_eval.loc[df_val_eval[\\'discourse_type\\'] == c].copy()\\n f1 = score_feedback_comp(pred_df, gt_df)\\n print(f\\' * {c:<10}: {f1:4f}\\')\\n f1score.append(f1)\\n f1avg = np.mean(f1score)\\n print(f\\'Overall Validation avg F1: {f1avg:.4f} val_loss:{valid_loss:.4f} val_accuracy:{valid_acc:.4f}\\')\\n return valid_loss, oof', 'def inference(model, data_loader, criterion, valid_flg):\\n stream = tqdm(data_loader)\\n model.eval()\\n \\n valid_loss = 0\\n valid_accuracy = 0\\n all_logits = None\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch[\\'input_ids\\'].to(device, dtype = torch.long)\\n mask = batch[\\'attention_mask\\'].to(device, dtype = torch.long)\\n with torch.no_grad():\\n raw_logits = model(input_ids=ids, mask = mask)\\n del ids, mask\\n \\n word_ids = batch[\\'word_ids\\'].to(device, dtype = torch.long)\\n logits = active_logits(raw_logits, word_ids)\\n sf_logits = torch.softmax(logits, dim= -1)\\n sf_raw_logits = torch.softmax(raw_logits, dim=-1)\\n if valid_flg: \\n raw_labels = batch[\\'labels\\'].to(device, dtype = torch.long)\\n labels = active_labels(raw_labels)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n valid_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n loss = criterion(logits, labels)\\n valid_loss += loss.item()\\n \\n if batch_idx == 1:\\n all_logits = sf_raw_logits.cpu().numpy()\\n else:\\n all_logits = np.append(all_logits, sf_raw_logits.cpu().numpy(), axis=0)\\n\\n \\n if valid_flg: \\n epoch_loss = valid_loss / batch_idx\\n epoch_accuracy = valid_accuracy / batch_idx\\n else:\\n epoch_loss, epoch_accuracy = 0, 0\\n return all_logits, epoch_loss, epoch_accuracy\\n\\n\\ndef preds_class_prob(all_logits, data_loader):\\n print(\"predict target class and its probabilty\")\\n final_predictions = []\\n final_predictions_score = []\\n stream = tqdm(data_loader)\\n len_sample = all_logits.shape[0]\\n\\n for batch_idx, batch in enumerate(stream, start=0):\\n for minibatch_idx in range(HyperParameters.valid_batch_size):\\n sample_idx = int(batch_idx * HyperParameters.valid_batch_size + minibatch_idx)\\n if sample_idx > len_sample - 1 : break\\n word_ids = batch[\\'word_ids\\'][minibatch_idx].numpy()\\n predictions =[]\\n predictions_prob = []\\n pred_class_id = np.argmax(all_logits[sample_idx], axis=1)\\n pred_score = np.max(all_logits[sample_idx], axis=1)\\n pred_class_labels = [IDS_TO_LABELS[i] for i in pred_class_id]\\n prev_word_idx = -1\\n for idx, word_idx in enumerate(word_ids):\\n if word_idx == -1:\\n pass\\n elif word_idx != prev_word_idx:\\n predictions.append(pred_class_labels[idx])\\n predictions_prob.append(pred_score[idx])\\n prev_word_idx = word_idx\\n final_predictions.append(predictions)\\n final_predictions_score.append(predictions_prob)\\n return final_predictions, final_predictions_score', 'def get_preds_onefold(model, df, dl, criterion, valid_flg):\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n all_preds, all_preds_prob = preds_class_prob(logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred, valid_loss, valid_acc\\n\\ndef get_preds_folds(model, df, dl, criterion, valid_flg=False):\\n for i_fold in range(HyperParameters.n_fold):\\n model_filename = os.path.join(HyperParameters.model_dir, f\"{HyperParameters.model_savename}_{i_fold}.bin\")\\n print(f\"{model_filename} inference\")\\n model = model.to(device)\\n model.load_state_dict(torch.load(model_filename))\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n if i_fold == 0:\\n avg_pred_logits = logits\\n else:\\n avg_pred_logits += logits\\n avg_pred_logits /= HyperParameters.n_fold\\n all_preds, all_preds_prob = preds_class_prob(avg_pred_logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred\\n\\ndef post_process_pred(df, all_preds, all_preds_prob):\\n final_preds = []\\n for i in range(len(df)):\\n idx = df.id.values[i]\\n pred = all_preds[i]\\n pred_prob = all_preds_prob[i]\\n j = 0\\n while j < len(pred):\\n cls = pred[j]\\n if cls == \\'O\\': j += 1\\n else: cls = cls.replace(\\'B\\', \\'I\\')\\n end = j + 1\\n while end < len(pred) and pred[end] == cls:\\n end += 1\\n if cls != \\'O\\' and cls !=\\'\\':\\n avg_score = np.mean(pred_prob[j:end])\\n if end - j > MIN_THRESH[cls] and avg_score > PROB_THRESH[cls]:\\n final_preds.append((idx, cls.replace(\\'I-\\', \\'\\'), \\' \\'.join(map(str, list(range(j, end))))))\\n j = end\\n df_pred = pd.DataFrame(final_preds)\\n df_pred.columns = [\\'id\\', \\'class\\', \\'new_predictionstring\\']\\n return df_pred', 'print_gpu_utilization()', 'def pretty_size(size):\\n\\t\"\"\"Pretty prints a torch.Size object\"\"\"\\n\\tassert(isinstance(size, torch.Size))\\n\\treturn \" × \".join(map(str, size))\\n\\ndef dump_tensors(gpu_only=True):\\n\\t\"\"\"Prints a list of the Tensors being tracked by the garbage collector.\"\"\"\\n\\timport gc\\n\\ttotal_size = 0\\n\\tfor obj in gc.get_objects():\\n\\t\\ttry:\\n\\t\\t\\tif torch.is_tensor(obj):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s:%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.numel()\\n\\t\\t\\telif hasattr(obj, \"data\") and torch.is_tensor(obj.data):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s → %s:%s%s%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t type(obj.data).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.data.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" grad\" if obj.requires_grad else \"\", \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" volatile\" if obj.volatile else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.data.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.data.numel()\\n\\t\\texcept Exception as e:\\n\\t\\t\\tpass \\n\\tprint(\"Total size:\", total_size)', 'dump_tensors()', 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', 'try:\\n print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n #if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n print(model_config)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(raw_logits.shape)\\n print(word_ids.shape)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n labels = raw_labels\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n print(gc.get_referrers(model))\\n raise Exception()\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])'], '_oh': {13: id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 }, '_dh': ['/content'], '_sh': , 'In': ['', 'ON_COLAB = True\\nif ON_COLAB:\\n # Mount drive:\\n from google.colab import drive, files\\n # mount Google Drive\\n drive.mount(\"/content/drive\")', \"get_ipython().run_cell_magic('capture', '', '# if on Colab, we need to install missing stuff!\\\\nif ON_COLAB:\\\\n !pip install transformers\\\\n !pip install iterative-stratification\\\\n !pip install nvidia-ml-py3')\", 'import gc\\nimport os\\nimport torch\\nimport random\\nimport numpy as np\\nimport pandas as pd\\nimport torch.nn as nn\\nfrom pynvml import nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit\\n\\nfrom tqdm.notebook import tqdm\\nfrom sklearn.metrics import accuracy_score\\nfrom torch.cuda.amp import autocast, GradScaler\\nfrom torch.utils.data import Dataset, DataLoader\\nfrom transformers import AutoConfig, AutoModel, AutoTokenizer\\nfrom iterstrat.ml_stratifiers import MultilabelStratifiedKFold', 'def print_gpu_utilization():\\n print(f\"GPU memory occupied: {get_gpu_utilization()} MB.\")\\n\\ndef get_gpu_utilization():\\n nvmlInit()\\n handle = nvmlDeviceGetHandleByIndex(0)\\n info = nvmlDeviceGetMemoryInfo(handle)\\n return info.used//1024**2\\n\\ndef print_summary(result):\\n print(f\"Time: {result.metrics[\\'train_runtime\\']:.2f}\")\\n print(f\"Samples/second: {result.metrics[\\'train_samples_per_second\\']:.2f}\")\\n print_gpu_utilization()\\n\\nprint_gpu_utilization()', \"if ON_COLAB:\\n get_ipython().system('cd /content/drive/MyDrive/NLP_project')\\n\\n\\n# DATA DIR ---- TO CHANGE\\nDATA_DIR = 'drive/MyDrive/NLP_project/'\", \"class HyperParameters:\\n \\n # Here we choose model type. Can be changed for others\\n name = 'longformer'\\n model_savename = 'longformer'\\n model_name = 'allenai/longformer-base-4096' # this is the most important: determines what transformer is used in training\\n \\n # Directory hyperparameters: make sure to change with what you are using! Only needed to change here\\n base_dir = DATA_DIR\\n data_dir = os.path.join(base_dir, 'data')\\n pre_data_dir = os.path.join(base_dir, 'data/preprocessed')\\n model_dir = os.path.join(base_dir, f'model/{name}')\\n output_dir = os.path.join(base_dir, f'output/{name}')\\n \\n # Training hyperparameters\\n is_debug = False\\n n_epoch = 2 # not to exceed runtime limit\\n n_fold = 5\\n verbose_steps = 500\\n random_seed = 42\\n\\n # Model specific hyperparameters\\n max_length = 1024\\n inference_max_length = 4096\\n train_batch_size = 4\\n valid_batch_size = 4\\n lr = 4e-5\\n\\n # Task hyperparameters\\n num_labels = 15\\n label_subtokens = True\\n output_hidden_states = True\\n hidden_dropout_prob = 0.1\\n layer_norm_eps = 1e-7\\n add_pooling_layer = False\\n verbose_steps = 500\\n if is_debug:\\n debug_sample = 1000\\n verbose_steps = 16\\n n_epoch = 1\\n n_fold = 2\\n\\nif not os.path.exists(HyperParameters.model_dir):\\n get_ipython().system('mkdir $HyperParameters.model_dir')\", 'IGNORE_INDEX = -100\\nNON_LABEL = -1\\nOUTPUT_LABELS = [\\'O\\', \\'B-Lead\\', \\'I-Lead\\', \\'B-Position\\', \\'I-Position\\', \\'B-Claim\\', \\'I-Claim\\', \\'B-Counterclaim\\', \\'I-Counterclaim\\', \\n \\'B-Rebuttal\\', \\'I-Rebuttal\\', \\'B-Evidence\\', \\'I-Evidence\\', \\'B-Concluding Statement\\', \\'I-Concluding Statement\\']\\nLABELS_TO_IDS = {v:k for k,v in enumerate(OUTPUT_LABELS)}\\nIDS_TO_LABELS = {k:v for k,v in enumerate(OUTPUT_LABELS)}\\n\\nMIN_THRESH = {\\n \"I-Lead\": 9,\\n \"I-Position\": 5,\\n \"I-Evidence\": 14,\\n \"I-Claim\": 3,\\n \"I-Concluding Statement\": 11,\\n \"I-Counterclaim\": 6,\\n \"I-Rebuttal\": 4,\\n}\\n\\nPROB_THRESH = {\\n \"I-Lead\": 0.7,\\n \"I-Position\": 0.55,\\n \"I-Evidence\": 0.65,\\n \"I-Claim\": 0.55,\\n \"I-Concluding Statement\": 0.7,\\n \"I-Counterclaim\": 0.5,\\n \"I-Rebuttal\": 0.55,\\n}', \"def set_seed(seed=HyperParameters.random_seed):\\n np.random.seed(seed)\\n \\n random.seed(seed)\\n \\n torch.manual_seed(seed)\\n torch.cuda.manual_seed(seed)\\n \\n torch.backends.cudnn.deterministic =True\\n torch.backends.cudnn.benchmark = False\\n\\nset_seed()\\n\\n# Set proper device\\nif torch.cuda.is_available():\\n device = torch.device('cuda')\\nelse:\\n device = torch.device('cpu')\\n\\nprint(f'Using device: {device}')\", \"df_alltrain = pd.read_csv(f'{HyperParameters.data_dir}/corrected_train.csv')\", 'def agg_essays(train_flg):\\n \"\"\"\\n Splits every word in an essay and adds the text of each essay to a dataframe.\\n \"\"\"\\n folder = \\'train\\' if train_flg else \\'test\\'\\n names, texts =[], []\\n for f in tqdm(list(os.listdir(f\\'{HyperParameters.data_dir}/{folder}\\'))):\\n names.append(f.replace(\\'.txt\\', \\'\\'))\\n texts.append(open(f\\'{HyperParameters.data_dir}/{folder}/\\' + f, \\'r\\').read())\\n df_texts = pd.DataFrame({\\'id\\': names, \\'text\\': texts})\\n\\n df_texts[\\'text_split\\'] = df_texts.text.str.split()\\n print(\\'Completed tokenizing texts.\\')\\n return df_texts', 'def ner(df_texts, df_train):\\n \"\"\"\\n Maps discourse type to each word of the text, according to the train.csv file.\\n \"\"\"\\n all_entities = []\\n for _, row in tqdm(df_texts.iterrows(), total=len(df_texts)):\\n total = len(row[\\'text_split\\'])\\n entities = [\\'O\\'] * total\\n\\n for _, row2 in df_train[df_train[\\'id\\'] == row[\\'id\\']].iterrows():\\n discourse = row2[\\'discourse_type\\']\\n list_ix = [int(x) for x in row2[\\'predictionstring\\'].split(\\' \\')]\\n entities[list_ix[0]] = f\\'B-{discourse}\\'\\n for k in list_ix[1:]: entities[k] = f\\'I-{discourse}\\'\\n all_entities.append(entities)\\n\\n df_texts[\\'entities\\'] = all_entities\\n print(\\'Completed mapping discourse to each token.\\')\\n return df_texts', 'def preprocess(df_train = None):\\n \"\"\"\\n Generates the dataframe we will use for training.\\n Splits essays into words, assigns a token name to each word, and adds everything to a dataframe.\\n \"\"\"\\n if df_train is None:\\n train_flg = False\\n else:\\n train_flg = True\\n \\n df_texts = agg_essays(train_flg)\\n\\n if train_flg:\\n df_texts = ner(df_texts, df_train)\\n return df_texts\\n\\n# Make sure we only run pre-processing if we did not do it in the past:\\n\\nif not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n alltrain_texts = preprocess(df_alltrain)\\n test_texts = preprocess()\\nelse:\\n alltrain_texts = pd.read_csv(f\"{HyperParameters.data_dir}/train_folds.csv\")', \"# Visualize preprocessing result:\\nparse_string = lambda x: [string[1:-1] for string in x[1:-1].split(', ')]\\nalltrain_texts.entities = alltrain_texts.entities.apply(parse_string)\\nalltrain_texts.text_split = alltrain_texts.text_split.apply(parse_string)\\n\\nalltrain_texts.head()\", 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Transform categorical labels to dummy variables. Group by id. Sum over dummy. \\n dfx = pd.get_dummies(df_alltrain, columns=[\"discourse_type\"]).groupby([\"id\"], as_index=False).sum()\\n\\n # Generate name for the dummy columns\\n dummy_cols = [c for c in dfx.columns if c.startswith(\"discourse_type_\") or c == \"id\" and c != \"discourse_type_num\"]\\n # dfx is now only the dataset with dummy columns selected: don\\'t need to pass the data to do the splits\\n dfx = dfx[dummy_cols]', 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Generate cross validation object\\n mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)\\n\\n # Extract labels\\n labels = [c for c in dfx.columns if c != \"id\"]\\n dfx_labels = dfx[labels]\\n\\n # Dummy kfold assignment\\n dfx[\"kfold\"] = -1\\n\\n # Split\\n for fold, (trn_, val_) in enumerate(mskf.split(dfx, dfx_labels)):\\n print(len(trn_), len(val_))\\n \\n # Change the value of the kfold column at the validation index to the value of the fold\\n # This will tell us when to use the current entry in the validation set\\n dfx.loc[val_, \"kfold\"] = fold\\n\\n # merge back to original dataframe\\n alltrain_texts = alltrain_texts.merge(dfx[[\"id\", \"kfold\"]], on=\"id\", how=\"left\")\\n print(alltrain_texts.kfold.value_counts())\\n\\n # Save so next time we import it directly\\n alltrain_texts.to_csv(f\"{HyperParameters.data_dir}/train_folds.csv\", index=False)', \"# need help with this\\nclass FeedbackPrizeDataset(Dataset):\\n def __init__(self, dataframe, tokenizer, max_len, has_labels):\\n self.len = len(dataframe)\\n self.data = dataframe\\n self.tokenizer = tokenizer\\n self.max_len = max_len\\n self.has_labels = has_labels\\n \\n def __getitem__(self, index):\\n text = self.data.text[index]\\n encoding = self.tokenizer(\\n text.split(),\\n is_split_into_words = True,\\n padding = 'max_length',\\n truncation = True,\\n max_length = self.max_len\\n )\\n word_ids = encoding.word_ids()\\n\\n # targets\\n if self.has_labels:\\n word_labels = self.data.entities[index]\\n prev_word_idx = None\\n labels_ids = []\\n for word_idx in word_ids:\\n if word_idx is None:\\n labels_ids.append(IGNORE_INDEX)\\n elif word_idx != prev_word_idx:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n if HyperParameters.label_subtokens:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n labels_ids.append(IGNORE_INDEX)\\n prev_word_idx = word_idx\\n encoding['labels'] = labels_ids\\n # convert to torch.tensor\\n item = {k: torch.as_tensor(v) for k, v in encoding.items()}\\n word_ids2 = [w if w is not None else NON_LABEL for w in word_ids]\\n item['word_ids'] = torch.as_tensor(word_ids2)\\n return item\\n\\n def __len__(self):\\n return self.len\", \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", 'def build_model_tokenizer():\\n tokenizer = AutoTokenizer.from_pretrained(HyperParameters.model_name, add_prefix_space = True)\\n model = FeedbackModel()\\n return model, tokenizer', '# Need help with this: used in training to transform raw logits to labels needed\\ndef active_logits(raw_logits, word_ids):\\n word_ids = word_ids.view(-1)\\n active_mask = word_ids.unsqueeze(1).expand(word_ids.shape[0], HyperParameters.num_labels)\\n active_mask = active_mask != NON_LABEL\\n active_logits = raw_logits.view(-1, HyperParameters.num_labels)\\n active_logits = torch.masked_select(active_logits, active_mask) # return 1dTensor\\n active_logits = active_logits.view(-1, HyperParameters.num_labels) \\n return active_logits\\n\\ndef active_labels(labels):\\n active_mask = labels.view(-1) != IGNORE_INDEX\\n active_labels = torch.masked_select(labels.view(-1), active_mask)\\n return active_labels\\n\\ndef active_preds_prob(active_logits):\\n active_preds = torch.argmax(active_logits, axis = 1)\\n active_preds_prob, _ = torch.max(active_logits, axis = 1)\\n return active_preds, active_preds_prob', 'def calculate_overlap(set_pred, set_gt):\\n \"\"\"\\n Calculates if the overlap between prediction and\\n ground truth is enough fora potential True positive\\n \"\"\"\\n # Length of each and intersection\\n try:\\n len_gt = len(set_gt)\\n len_pred = len(set_pred)\\n inter = len(set_gt & set_pred)\\n overlap_1 = inter / len_gt\\n overlap_2 = inter/ len_pred\\n return overlap_1 >= 0.5 and overlap_2 >= 0.5\\n except: # at least one of the input is NaN\\n return False\\n\\ndef score_feedback_comp_micro(pred_df, gt_df, discourse_type):\\n \"\"\"\\n A function that scores for the kaggle\\n Student Writing Competition\\n \\n Uses the steps in the evaluation page here:\\n https://www.kaggle.com/c/feedback-prize-2021/overview/evaluation\\n \"\"\"\\n gt_df = gt_df.loc[gt_df[\\'discourse_type\\'] == discourse_type, \\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df = pred_df.loc[pred_df[\\'class\\'] == discourse_type,\\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df[\\'pred_id\\'] = pred_df.index\\n gt_df[\\'gt_id\\'] = gt_df.index\\n pred_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in pred_df[\\'predictionstring\\']]\\n gt_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in gt_df[\\'predictionstring\\']]\\n \\n # Step 1. all ground truths and predictions for a given class are compared.\\n joined = pred_df.merge(gt_df,\\n left_on=\\'id\\',\\n right_on=\\'id\\',\\n how=\\'outer\\',\\n suffixes=(\\'_pred\\',\\'_gt\\')\\n )\\n overlaps = [calculate_overlap(*args) for args in zip(joined.predictionstring_pred, \\n joined.predictionstring_gt)]\\n \\n # 2. If the overlap between the ground truth and prediction is >= 0.5, \\n # and the overlap between the prediction and the ground truth >= 0.5,\\n # the prediction is a match and considered a true positive.\\n # If multiple matches exist, the match with the highest pair of overlaps is taken.\\n # we don\\'t need to compute the match to compute the score\\n TP = joined.loc[overlaps][\\'gt_id\\'].nunique()\\n\\n # 3. Any unmatched ground truths are false negatives\\n # and any unmatched predictions are false positives.\\n TPandFP = len(pred_df)\\n TPandFN = len(gt_df)\\n \\n #calc microf1\\n my_f1_score = 2*TP / (TPandFP + TPandFN)\\n return my_f1_score\\n\\ndef score_feedback_comp(pred_df, gt_df, return_class_scores=False):\\n \"\"\"\\n Final helper function for model evaluation.\\n \\n Args:\\n pred_df (pandas.DataFrame): dataframe containing model predictions. Needs to have columns: [\\'id\\',\\'class\\',\\'predictionstring\\']\\n gt_df (pandas.DataFrame): dataframe of ground truth used for model training\\n return_class_scores (bool): Boolean indicating if we want to return the F1 score for each predicted class.\\n \\n Returns:\\n f1 (float): F1 score of the model\\n (optional) class_scores (dict): Dictionary of per-class F1 score\\n \"\"\"\\n class_scores = {}\\n for discourse_type in gt_df.discourse_type.unique():\\n class_score = score_feedback_comp_micro(pred_df, gt_df, discourse_type)\\n class_scores[discourse_type] = class_score\\n f1 = np.mean([v for v in class_scores.values()])\\n if return_class_scores:\\n return f1, class_scores\\n return f1', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n \\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'def valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion):\\n oof, valid_loss, valid_acc = get_preds_onefold(model, df_val, dl_val, criterion, valid_flg=True)\\n f1score =[]\\n # classes = oof[\\'class\\'].unique()\\n classes = [\\'Lead\\', \\'Position\\', \\'Claim\\',\\'Counterclaim\\', \\'Rebuttal\\',\\'Evidence\\',\\'Concluding Statement\\']\\n print(f\"Validation F1 scores\")\\n\\n for c in classes:\\n pred_df = oof.loc[oof[\\'class\\'] == c].copy()\\n gt_df = df_val_eval.loc[df_val_eval[\\'discourse_type\\'] == c].copy()\\n f1 = score_feedback_comp(pred_df, gt_df)\\n print(f\\' * {c:<10}: {f1:4f}\\')\\n f1score.append(f1)\\n f1avg = np.mean(f1score)\\n print(f\\'Overall Validation avg F1: {f1avg:.4f} val_loss:{valid_loss:.4f} val_accuracy:{valid_acc:.4f}\\')\\n return valid_loss, oof', 'def inference(model, data_loader, criterion, valid_flg):\\n stream = tqdm(data_loader)\\n model.eval()\\n \\n valid_loss = 0\\n valid_accuracy = 0\\n all_logits = None\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch[\\'input_ids\\'].to(device, dtype = torch.long)\\n mask = batch[\\'attention_mask\\'].to(device, dtype = torch.long)\\n with torch.no_grad():\\n raw_logits = model(input_ids=ids, mask = mask)\\n del ids, mask\\n \\n word_ids = batch[\\'word_ids\\'].to(device, dtype = torch.long)\\n logits = active_logits(raw_logits, word_ids)\\n sf_logits = torch.softmax(logits, dim= -1)\\n sf_raw_logits = torch.softmax(raw_logits, dim=-1)\\n if valid_flg: \\n raw_labels = batch[\\'labels\\'].to(device, dtype = torch.long)\\n labels = active_labels(raw_labels)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n valid_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n loss = criterion(logits, labels)\\n valid_loss += loss.item()\\n \\n if batch_idx == 1:\\n all_logits = sf_raw_logits.cpu().numpy()\\n else:\\n all_logits = np.append(all_logits, sf_raw_logits.cpu().numpy(), axis=0)\\n\\n \\n if valid_flg: \\n epoch_loss = valid_loss / batch_idx\\n epoch_accuracy = valid_accuracy / batch_idx\\n else:\\n epoch_loss, epoch_accuracy = 0, 0\\n return all_logits, epoch_loss, epoch_accuracy\\n\\n\\ndef preds_class_prob(all_logits, data_loader):\\n print(\"predict target class and its probabilty\")\\n final_predictions = []\\n final_predictions_score = []\\n stream = tqdm(data_loader)\\n len_sample = all_logits.shape[0]\\n\\n for batch_idx, batch in enumerate(stream, start=0):\\n for minibatch_idx in range(HyperParameters.valid_batch_size):\\n sample_idx = int(batch_idx * HyperParameters.valid_batch_size + minibatch_idx)\\n if sample_idx > len_sample - 1 : break\\n word_ids = batch[\\'word_ids\\'][minibatch_idx].numpy()\\n predictions =[]\\n predictions_prob = []\\n pred_class_id = np.argmax(all_logits[sample_idx], axis=1)\\n pred_score = np.max(all_logits[sample_idx], axis=1)\\n pred_class_labels = [IDS_TO_LABELS[i] for i in pred_class_id]\\n prev_word_idx = -1\\n for idx, word_idx in enumerate(word_ids):\\n if word_idx == -1:\\n pass\\n elif word_idx != prev_word_idx:\\n predictions.append(pred_class_labels[idx])\\n predictions_prob.append(pred_score[idx])\\n prev_word_idx = word_idx\\n final_predictions.append(predictions)\\n final_predictions_score.append(predictions_prob)\\n return final_predictions, final_predictions_score', 'def get_preds_onefold(model, df, dl, criterion, valid_flg):\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n all_preds, all_preds_prob = preds_class_prob(logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred, valid_loss, valid_acc\\n\\ndef get_preds_folds(model, df, dl, criterion, valid_flg=False):\\n for i_fold in range(HyperParameters.n_fold):\\n model_filename = os.path.join(HyperParameters.model_dir, f\"{HyperParameters.model_savename}_{i_fold}.bin\")\\n print(f\"{model_filename} inference\")\\n model = model.to(device)\\n model.load_state_dict(torch.load(model_filename))\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n if i_fold == 0:\\n avg_pred_logits = logits\\n else:\\n avg_pred_logits += logits\\n avg_pred_logits /= HyperParameters.n_fold\\n all_preds, all_preds_prob = preds_class_prob(avg_pred_logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred\\n\\ndef post_process_pred(df, all_preds, all_preds_prob):\\n final_preds = []\\n for i in range(len(df)):\\n idx = df.id.values[i]\\n pred = all_preds[i]\\n pred_prob = all_preds_prob[i]\\n j = 0\\n while j < len(pred):\\n cls = pred[j]\\n if cls == \\'O\\': j += 1\\n else: cls = cls.replace(\\'B\\', \\'I\\')\\n end = j + 1\\n while end < len(pred) and pred[end] == cls:\\n end += 1\\n if cls != \\'O\\' and cls !=\\'\\':\\n avg_score = np.mean(pred_prob[j:end])\\n if end - j > MIN_THRESH[cls] and avg_score > PROB_THRESH[cls]:\\n final_preds.append((idx, cls.replace(\\'I-\\', \\'\\'), \\' \\'.join(map(str, list(range(j, end))))))\\n j = end\\n df_pred = pd.DataFrame(final_preds)\\n df_pred.columns = [\\'id\\', \\'class\\', \\'new_predictionstring\\']\\n return df_pred', 'print_gpu_utilization()', 'def pretty_size(size):\\n\\t\"\"\"Pretty prints a torch.Size object\"\"\"\\n\\tassert(isinstance(size, torch.Size))\\n\\treturn \" × \".join(map(str, size))\\n\\ndef dump_tensors(gpu_only=True):\\n\\t\"\"\"Prints a list of the Tensors being tracked by the garbage collector.\"\"\"\\n\\timport gc\\n\\ttotal_size = 0\\n\\tfor obj in gc.get_objects():\\n\\t\\ttry:\\n\\t\\t\\tif torch.is_tensor(obj):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s:%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.numel()\\n\\t\\t\\telif hasattr(obj, \"data\") and torch.is_tensor(obj.data):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s → %s:%s%s%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t type(obj.data).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.data.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" grad\" if obj.requires_grad else \"\", \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" volatile\" if obj.volatile else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.data.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.data.numel()\\n\\t\\texcept Exception as e:\\n\\t\\t\\tpass \\n\\tprint(\"Total size:\", total_size)', 'dump_tensors()', 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', 'try:\\n print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n #if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n print(model_config)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(raw_logits.shape)\\n print(word_ids.shape)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n labels = raw_labels\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'try:\\n print(gc.get_referrers(model))\\n raise Exception()\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])'], 'Out': {13: id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 }, 'get_ipython': >, 'exit': , 'quit': , '_': id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 , '__': '', '___': '', '_i': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_ii': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n labels = raw_labels\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", '_iii': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i1': 'ON_COLAB = True\\nif ON_COLAB:\\n # Mount drive:\\n from google.colab import drive, files\\n # mount Google Drive\\n drive.mount(\"/content/drive\")', 'ON_COLAB': True, 'drive': , 'files': , '_i2': '%%capture\\n# if on Colab, we need to install missing stuff!\\nif ON_COLAB:\\n !pip install transformers\\n !pip install iterative-stratification\\n !pip install nvidia-ml-py3', '_exit_code': 0, '_i3': 'import gc\\nimport os\\nimport torch\\nimport random\\nimport numpy as np\\nimport pandas as pd\\nimport torch.nn as nn\\nfrom pynvml import nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit\\n\\nfrom tqdm.notebook import tqdm\\nfrom sklearn.metrics import accuracy_score\\nfrom torch.cuda.amp import autocast, GradScaler\\nfrom torch.utils.data import Dataset, DataLoader\\nfrom transformers import AutoConfig, AutoModel, AutoTokenizer\\nfrom iterstrat.ml_stratifiers import MultilabelStratifiedKFold', 'gc': , 'os': , 'torch': , 'random': , 'np': , 'pd': , 'nn': , 'nvmlDeviceGetHandleByIndex': , 'nvmlDeviceGetMemoryInfo': , 'nvmlInit': , 'tqdm': , 'accuracy_score': , 'autocast': , 'GradScaler': , 'Dataset': , 'DataLoader': , 'AutoConfig': , 'AutoModel': , 'AutoTokenizer': , 'MultilabelStratifiedKFold': , '_i4': 'def print_gpu_utilization():\\n print(f\"GPU memory occupied: {get_gpu_utilization()} MB.\")\\n\\ndef get_gpu_utilization():\\n nvmlInit()\\n handle = nvmlDeviceGetHandleByIndex(0)\\n info = nvmlDeviceGetMemoryInfo(handle)\\n return info.used//1024**2\\n\\ndef print_summary(result):\\n print(f\"Time: {result.metrics[\\'train_runtime\\']:.2f}\")\\n print(f\"Samples/second: {result.metrics[\\'train_samples_per_second\\']:.2f}\")\\n print_gpu_utilization()\\n\\nprint_gpu_utilization()', 'print_gpu_utilization': , 'get_gpu_utilization': , 'print_summary': , '_i5': \"if ON_COLAB:\\n !cd /content/drive/MyDrive/NLP_project\\n\\n\\n# DATA DIR ---- TO CHANGE\\nDATA_DIR = 'drive/MyDrive/NLP_project/'\", 'DATA_DIR': 'drive/MyDrive/NLP_project/', '_i6': \"class HyperParameters:\\n \\n # Here we choose model type. Can be changed for others\\n name = 'longformer'\\n model_savename = 'longformer'\\n model_name = 'allenai/longformer-base-4096' # this is the most important: determines what transformer is used in training\\n \\n # Directory hyperparameters: make sure to change with what you are using! Only needed to change here\\n base_dir = DATA_DIR\\n data_dir = os.path.join(base_dir, 'data')\\n pre_data_dir = os.path.join(base_dir, 'data/preprocessed')\\n model_dir = os.path.join(base_dir, f'model/{name}')\\n output_dir = os.path.join(base_dir, f'output/{name}')\\n \\n # Training hyperparameters\\n is_debug = False\\n n_epoch = 2 # not to exceed runtime limit\\n n_fold = 5\\n verbose_steps = 500\\n random_seed = 42\\n\\n # Model specific hyperparameters\\n max_length = 1024\\n inference_max_length = 4096\\n train_batch_size = 4\\n valid_batch_size = 4\\n lr = 4e-5\\n\\n # Task hyperparameters\\n num_labels = 15\\n label_subtokens = True\\n output_hidden_states = True\\n hidden_dropout_prob = 0.1\\n layer_norm_eps = 1e-7\\n add_pooling_layer = False\\n verbose_steps = 500\\n if is_debug:\\n debug_sample = 1000\\n verbose_steps = 16\\n n_epoch = 1\\n n_fold = 2\\n\\nif not os.path.exists(HyperParameters.model_dir):\\n !mkdir $HyperParameters.model_dir\", 'HyperParameters': , '_i7': 'IGNORE_INDEX = -100\\nNON_LABEL = -1\\nOUTPUT_LABELS = [\\'O\\', \\'B-Lead\\', \\'I-Lead\\', \\'B-Position\\', \\'I-Position\\', \\'B-Claim\\', \\'I-Claim\\', \\'B-Counterclaim\\', \\'I-Counterclaim\\', \\n \\'B-Rebuttal\\', \\'I-Rebuttal\\', \\'B-Evidence\\', \\'I-Evidence\\', \\'B-Concluding Statement\\', \\'I-Concluding Statement\\']\\nLABELS_TO_IDS = {v:k for k,v in enumerate(OUTPUT_LABELS)}\\nIDS_TO_LABELS = {k:v for k,v in enumerate(OUTPUT_LABELS)}\\n\\nMIN_THRESH = {\\n \"I-Lead\": 9,\\n \"I-Position\": 5,\\n \"I-Evidence\": 14,\\n \"I-Claim\": 3,\\n \"I-Concluding Statement\": 11,\\n \"I-Counterclaim\": 6,\\n \"I-Rebuttal\": 4,\\n}\\n\\nPROB_THRESH = {\\n \"I-Lead\": 0.7,\\n \"I-Position\": 0.55,\\n \"I-Evidence\": 0.65,\\n \"I-Claim\": 0.55,\\n \"I-Concluding Statement\": 0.7,\\n \"I-Counterclaim\": 0.5,\\n \"I-Rebuttal\": 0.55,\\n}', 'IGNORE_INDEX': -100, 'NON_LABEL': -1, 'OUTPUT_LABELS': ['O', 'B-Lead', 'I-Lead', 'B-Position', 'I-Position', 'B-Claim', 'I-Claim', 'B-Counterclaim', 'I-Counterclaim', 'B-Rebuttal', 'I-Rebuttal', 'B-Evidence', 'I-Evidence', 'B-Concluding Statement', 'I-Concluding Statement'], 'LABELS_TO_IDS': {'O': 0, 'B-Lead': 1, 'I-Lead': 2, 'B-Position': 3, 'I-Position': 4, 'B-Claim': 5, 'I-Claim': 6, 'B-Counterclaim': 7, 'I-Counterclaim': 8, 'B-Rebuttal': 9, 'I-Rebuttal': 10, 'B-Evidence': 11, 'I-Evidence': 12, 'B-Concluding Statement': 13, 'I-Concluding Statement': 14}, 'IDS_TO_LABELS': {0: 'O', 1: 'B-Lead', 2: 'I-Lead', 3: 'B-Position', 4: 'I-Position', 5: 'B-Claim', 6: 'I-Claim', 7: 'B-Counterclaim', 8: 'I-Counterclaim', 9: 'B-Rebuttal', 10: 'I-Rebuttal', 11: 'B-Evidence', 12: 'I-Evidence', 13: 'B-Concluding Statement', 14: 'I-Concluding Statement'}, 'MIN_THRESH': {'I-Lead': 9, 'I-Position': 5, 'I-Evidence': 14, 'I-Claim': 3, 'I-Concluding Statement': 11, 'I-Counterclaim': 6, 'I-Rebuttal': 4}, 'PROB_THRESH': {'I-Lead': 0.7, 'I-Position': 0.55, 'I-Evidence': 0.65, 'I-Claim': 0.55, 'I-Concluding Statement': 0.7, 'I-Counterclaim': 0.5, 'I-Rebuttal': 0.55}, '_i8': \"def set_seed(seed=HyperParameters.random_seed):\\n np.random.seed(seed)\\n \\n random.seed(seed)\\n \\n torch.manual_seed(seed)\\n torch.cuda.manual_seed(seed)\\n \\n torch.backends.cudnn.deterministic =True\\n torch.backends.cudnn.benchmark = False\\n\\nset_seed()\\n\\n# Set proper device\\nif torch.cuda.is_available():\\n device = torch.device('cuda')\\nelse:\\n device = torch.device('cpu')\\n\\nprint(f'Using device: {device}')\", 'set_seed': , 'device': device(type='cuda'), '_i9': \"df_alltrain = pd.read_csv(f'{HyperParameters.data_dir}/corrected_train.csv')\", 'df_alltrain': id discourse_id discourse_start discourse_end \\\n",
+ "0 423A1CA112E2 1.622628e+12 8.0 229.0 \n",
+ "1 423A1CA112E2 1.622628e+12 230.0 312.0 \n",
+ "2 423A1CA112E2 1.622628e+12 313.0 401.0 \n",
+ "3 423A1CA112E2 1.622628e+12 402.0 758.0 \n",
+ "4 423A1CA112E2 1.622628e+12 759.0 886.0 \n",
+ "... ... ... ... ... \n",
+ "144288 4C471936CD75 1.618153e+12 2234.0 3203.0 \n",
+ "144289 4C471936CD75 1.618153e+12 3221.0 4509.0 \n",
+ "144290 4C471936CD75 1.618025e+12 4510.0 4570.0 \n",
+ "144291 4C471936CD75 1.618025e+12 4570.0 4922.0 \n",
+ "144292 4C471936CD75 1.618025e+12 4935.0 5825.0 \n",
+ "\n",
+ " discourse_text \\\n",
+ "0 Modern humans today are always on their phone.... \n",
+ "1 They are some really bad consequences when stu... \n",
+ "2 Some certain areas in the United States ban ph... \n",
+ "3 When people have phones, they know about certa... \n",
+ "4 Driving is one of the way how to get around. P... \n",
+ "... ... \n",
+ "144288 if I'm not sure what college I want to attend... \n",
+ "144289 seeking multiple opinions before making a har... \n",
+ "144290 it is better to seek multiple opinions instead... \n",
+ "144291 The impact of asking people to help you make a... \n",
+ "144292 there are many other reasons one might want to... \n",
+ "\n",
+ " discourse_type discourse_type_num \\\n",
+ "0 Lead Lead 1 \n",
+ "1 Position Position 1 \n",
+ "2 Evidence Evidence 1 \n",
+ "3 Evidence Evidence 2 \n",
+ "4 Claim Claim 1 \n",
+ "... ... ... \n",
+ "144288 Evidence Evidence 2 \n",
+ "144289 Evidence Evidence 3 \n",
+ "144290 Position Position 1 \n",
+ "144291 Evidence Evidence 4 \n",
+ "144292 Concluding Statement Concluding Statement 1 \n",
+ "\n",
+ " predictionstring \\\n",
+ "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... \n",
+ "1 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 \n",
+ "2 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 \n",
+ "3 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9... \n",
+ "4 139 140 141 142 143 144 145 146 147 148 149 15... \n",
+ "... ... \n",
+ "144288 386 387 388 389 390 391 392 393 394 395 396 39... \n",
+ "144289 576 577 578 579 580 581 582 583 584 585 586 58... \n",
+ "144290 828 829 830 831 832 833 834 835 836 837 838 \n",
+ "144291 839 840 841 842 843 844 845 846 847 848 849 85... \n",
+ "144292 905 906 907 908 909 910 911 912 913 914 915 91... \n",
+ "\n",
+ " text_by_index new_start new_end \\\n",
+ "0 Modern humans today are always on their phone.... 8 230 \n",
+ "1 They are some really bad consequences when stu... 230 313 \n",
+ "2 Some certain areas in the United States ban ph... 313 401 \n",
+ "3 When people have phones, they know about certa... 402 758 \n",
+ "4 Driving is one of the way how to get around. P... 759 887 \n",
+ "... ... ... ... \n",
+ "144288 if I'm not sure what college I want to attend... 2235 3203 \n",
+ "144289 seeking multiple opinions before making a har... 3222 4510 \n",
+ "144290 it is better to seek multiple opinions instead... 4510 4570 \n",
+ "144291 The impact of asking people to help you make a... 4570 4923 \n",
+ "144292 there are many other reasons one might want to... 4935 5748 \n",
+ "\n",
+ " text_by_new_index \\\n",
+ "0 Modern humans today are always on their phone.... \n",
+ "1 They are some really bad consequences when stu... \n",
+ "2 Some certain areas in the United States ban ph... \n",
+ "3 When people have phones, they know about certa... \n",
+ "4 Driving is one of the way how to get around. P... \n",
+ "... ... \n",
+ "144288 if I'm not sure what college I want to attend,... \n",
+ "144289 seeking multiple opinions before making a hard... \n",
+ "144290 it is better to seek multiple opinions instead... \n",
+ "144291 The impact of asking people to help you make a... \n",
+ "144292 there are many other reasons one might want to... \n",
+ "\n",
+ " new_predictionstring \n",
+ "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... \n",
+ "1 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 \n",
+ "2 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 \n",
+ "3 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9... \n",
+ "4 139 140 141 142 143 144 145 146 147 148 149 15... \n",
+ "... ... \n",
+ "144288 386 387 388 389 390 391 392 393 394 395 396 39... \n",
+ "144289 576 577 578 579 580 581 582 583 584 585 586 58... \n",
+ "144290 828 829 830 831 832 833 834 835 836 837 838 \n",
+ "144291 839 840 841 842 843 844 845 846 847 848 849 85... \n",
+ "144292 905 906 907 908 909 910 911 912 913 914 915 91... \n",
+ "\n",
+ "[144293 rows x 13 columns], '_i10': 'def agg_essays(train_flg):\\n \"\"\"\\n Splits every word in an essay and adds the text of each essay to a dataframe.\\n \"\"\"\\n folder = \\'train\\' if train_flg else \\'test\\'\\n names, texts =[], []\\n for f in tqdm(list(os.listdir(f\\'{HyperParameters.data_dir}/{folder}\\'))):\\n names.append(f.replace(\\'.txt\\', \\'\\'))\\n texts.append(open(f\\'{HyperParameters.data_dir}/{folder}/\\' + f, \\'r\\').read())\\n df_texts = pd.DataFrame({\\'id\\': names, \\'text\\': texts})\\n\\n df_texts[\\'text_split\\'] = df_texts.text.str.split()\\n print(\\'Completed tokenizing texts.\\')\\n return df_texts', 'agg_essays': , '_i11': 'def ner(df_texts, df_train):\\n \"\"\"\\n Maps discourse type to each word of the text, according to the train.csv file.\\n \"\"\"\\n all_entities = []\\n for _, row in tqdm(df_texts.iterrows(), total=len(df_texts)):\\n total = len(row[\\'text_split\\'])\\n entities = [\\'O\\'] * total\\n\\n for _, row2 in df_train[df_train[\\'id\\'] == row[\\'id\\']].iterrows():\\n discourse = row2[\\'discourse_type\\']\\n list_ix = [int(x) for x in row2[\\'predictionstring\\'].split(\\' \\')]\\n entities[list_ix[0]] = f\\'B-{discourse}\\'\\n for k in list_ix[1:]: entities[k] = f\\'I-{discourse}\\'\\n all_entities.append(entities)\\n\\n df_texts[\\'entities\\'] = all_entities\\n print(\\'Completed mapping discourse to each token.\\')\\n return df_texts', 'ner': , '_i12': 'def preprocess(df_train = None):\\n \"\"\"\\n Generates the dataframe we will use for training.\\n Splits essays into words, assigns a token name to each word, and adds everything to a dataframe.\\n \"\"\"\\n if df_train is None:\\n train_flg = False\\n else:\\n train_flg = True\\n \\n df_texts = agg_essays(train_flg)\\n\\n if train_flg:\\n df_texts = ner(df_texts, df_train)\\n return df_texts\\n\\n# Make sure we only run pre-processing if we did not do it in the past:\\n\\nif not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n alltrain_texts = preprocess(df_alltrain)\\n test_texts = preprocess()\\nelse:\\n alltrain_texts = pd.read_csv(f\"{HyperParameters.data_dir}/train_folds.csv\")', 'preprocess': , 'alltrain_texts': id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "... ... ... \n",
+ "15589 1C899F124FEB While some students may think it's a beneficia... \n",
+ "15590 4453444AF383 There has been a strong arguement going on wea... \n",
+ "15591 EF0D75BF48DA I favor in to changing election by popular vot... \n",
+ "15592 8FFDA5B9D359 Do you think students would benefit from being... \n",
+ "15593 ACAB1FCA0A30 I would like to change the election for the pr... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "... ... \n",
+ "15589 [While, some, students, may, think, it's, a, b... \n",
+ "15590 [There, has, been, a, strong, arguement, going... \n",
+ "15591 [I, favor, in, to, changing, election, by, pop... \n",
+ "15592 [Do, you, think, students, would, benefit, fro... \n",
+ "15593 [I, would, like, to, change, the, election, fo... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 \n",
+ "... ... ... \n",
+ "15589 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "15590 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "15591 [B-Position, I-Position, I-Position, I-Positio... 3 \n",
+ "15592 [B-Position, I-Position, I-Position, I-Positio... 2 \n",
+ "15593 [B-Position, I-Position, I-Position, I-Positio... 4 \n",
+ "\n",
+ "[15594 rows x 5 columns], '_i13': \"# Visualize preprocessing result:\\nparse_string = lambda x: [string[1:-1] for string in x[1:-1].split(', ')]\\nalltrain_texts.entities = alltrain_texts.entities.apply(parse_string)\\nalltrain_texts.text_split = alltrain_texts.text_split.apply(parse_string)\\n\\nalltrain_texts.head()\", 'parse_string': at 0x7fb010272830>, '_13': id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 , '_i14': 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Transform categorical labels to dummy variables. Group by id. Sum over dummy. \\n dfx = pd.get_dummies(df_alltrain, columns=[\"discourse_type\"]).groupby([\"id\"], as_index=False).sum()\\n\\n # Generate name for the dummy columns\\n dummy_cols = [c for c in dfx.columns if c.startswith(\"discourse_type_\") or c == \"id\" and c != \"discourse_type_num\"]\\n # dfx is now only the dataset with dummy columns selected: don\\'t need to pass the data to do the splits\\n dfx = dfx[dummy_cols]', '_i15': 'if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \\n # Generate cross validation object\\n mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)\\n\\n # Extract labels\\n labels = [c for c in dfx.columns if c != \"id\"]\\n dfx_labels = dfx[labels]\\n\\n # Dummy kfold assignment\\n dfx[\"kfold\"] = -1\\n\\n # Split\\n for fold, (trn_, val_) in enumerate(mskf.split(dfx, dfx_labels)):\\n print(len(trn_), len(val_))\\n \\n # Change the value of the kfold column at the validation index to the value of the fold\\n # This will tell us when to use the current entry in the validation set\\n dfx.loc[val_, \"kfold\"] = fold\\n\\n # merge back to original dataframe\\n alltrain_texts = alltrain_texts.merge(dfx[[\"id\", \"kfold\"]], on=\"id\", how=\"left\")\\n print(alltrain_texts.kfold.value_counts())\\n\\n # Save so next time we import it directly\\n alltrain_texts.to_csv(f\"{HyperParameters.data_dir}/train_folds.csv\", index=False)', '_i16': \"# need help with this\\nclass FeedbackPrizeDataset(Dataset):\\n def __init__(self, dataframe, tokenizer, max_len, has_labels):\\n self.len = len(dataframe)\\n self.data = dataframe\\n self.tokenizer = tokenizer\\n self.max_len = max_len\\n self.has_labels = has_labels\\n \\n def __getitem__(self, index):\\n text = self.data.text[index]\\n encoding = self.tokenizer(\\n text.split(),\\n is_split_into_words = True,\\n padding = 'max_length',\\n truncation = True,\\n max_length = self.max_len\\n )\\n word_ids = encoding.word_ids()\\n\\n # targets\\n if self.has_labels:\\n word_labels = self.data.entities[index]\\n prev_word_idx = None\\n labels_ids = []\\n for word_idx in word_ids:\\n if word_idx is None:\\n labels_ids.append(IGNORE_INDEX)\\n elif word_idx != prev_word_idx:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n if HyperParameters.label_subtokens:\\n labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\\n else:\\n labels_ids.append(IGNORE_INDEX)\\n prev_word_idx = word_idx\\n encoding['labels'] = labels_ids\\n # convert to torch.tensor\\n item = {k: torch.as_tensor(v) for k, v in encoding.items()}\\n word_ids2 = [w if w is not None else NON_LABEL for w in word_ids]\\n item['word_ids'] = torch.as_tensor(word_ids2)\\n return item\\n\\n def __len__(self):\\n return self.len\", 'FeedbackPrizeDataset': , '_i17': \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", 'FeedbackModel': , '_i18': 'def build_model_tokenizer():\\n tokenizer = AutoTokenizer.from_pretrained(HyperParameters.model_name, add_prefix_space = True)\\n model = FeedbackModel()\\n return model, tokenizer', 'build_model_tokenizer': , '_i19': '# Need help with this: used in training to transform raw logits to labels needed\\ndef active_logits(raw_logits, word_ids):\\n word_ids = word_ids.view(-1)\\n active_mask = word_ids.unsqueeze(1).expand(word_ids.shape[0], HyperParameters.num_labels)\\n active_mask = active_mask != NON_LABEL\\n active_logits = raw_logits.view(-1, HyperParameters.num_labels)\\n active_logits = torch.masked_select(active_logits, active_mask) # return 1dTensor\\n active_logits = active_logits.view(-1, HyperParameters.num_labels) \\n return active_logits\\n\\ndef active_labels(labels):\\n active_mask = labels.view(-1) != IGNORE_INDEX\\n active_labels = torch.masked_select(labels.view(-1), active_mask)\\n return active_labels\\n\\ndef active_preds_prob(active_logits):\\n active_preds = torch.argmax(active_logits, axis = 1)\\n active_preds_prob, _ = torch.max(active_logits, axis = 1)\\n return active_preds, active_preds_prob', 'active_logits': , 'active_labels': , 'active_preds_prob': , '_i20': 'def calculate_overlap(set_pred, set_gt):\\n \"\"\"\\n Calculates if the overlap between prediction and\\n ground truth is enough fora potential True positive\\n \"\"\"\\n # Length of each and intersection\\n try:\\n len_gt = len(set_gt)\\n len_pred = len(set_pred)\\n inter = len(set_gt & set_pred)\\n overlap_1 = inter / len_gt\\n overlap_2 = inter/ len_pred\\n return overlap_1 >= 0.5 and overlap_2 >= 0.5\\n except: # at least one of the input is NaN\\n return False\\n\\ndef score_feedback_comp_micro(pred_df, gt_df, discourse_type):\\n \"\"\"\\n A function that scores for the kaggle\\n Student Writing Competition\\n \\n Uses the steps in the evaluation page here:\\n https://www.kaggle.com/c/feedback-prize-2021/overview/evaluation\\n \"\"\"\\n gt_df = gt_df.loc[gt_df[\\'discourse_type\\'] == discourse_type, \\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df = pred_df.loc[pred_df[\\'class\\'] == discourse_type,\\n [\\'id\\', \\'predictionstring\\']].reset_index(drop=True)\\n pred_df[\\'pred_id\\'] = pred_df.index\\n gt_df[\\'gt_id\\'] = gt_df.index\\n pred_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in pred_df[\\'predictionstring\\']]\\n gt_df[\\'predictionstring\\'] = [set(pred.split(\\' \\')) for pred in gt_df[\\'predictionstring\\']]\\n \\n # Step 1. all ground truths and predictions for a given class are compared.\\n joined = pred_df.merge(gt_df,\\n left_on=\\'id\\',\\n right_on=\\'id\\',\\n how=\\'outer\\',\\n suffixes=(\\'_pred\\',\\'_gt\\')\\n )\\n overlaps = [calculate_overlap(*args) for args in zip(joined.predictionstring_pred, \\n joined.predictionstring_gt)]\\n \\n # 2. If the overlap between the ground truth and prediction is >= 0.5, \\n # and the overlap between the prediction and the ground truth >= 0.5,\\n # the prediction is a match and considered a true positive.\\n # If multiple matches exist, the match with the highest pair of overlaps is taken.\\n # we don\\'t need to compute the match to compute the score\\n TP = joined.loc[overlaps][\\'gt_id\\'].nunique()\\n\\n # 3. Any unmatched ground truths are false negatives\\n # and any unmatched predictions are false positives.\\n TPandFP = len(pred_df)\\n TPandFN = len(gt_df)\\n \\n #calc microf1\\n my_f1_score = 2*TP / (TPandFP + TPandFN)\\n return my_f1_score\\n\\ndef score_feedback_comp(pred_df, gt_df, return_class_scores=False):\\n \"\"\"\\n Final helper function for model evaluation.\\n \\n Args:\\n pred_df (pandas.DataFrame): dataframe containing model predictions. Needs to have columns: [\\'id\\',\\'class\\',\\'predictionstring\\']\\n gt_df (pandas.DataFrame): dataframe of ground truth used for model training\\n return_class_scores (bool): Boolean indicating if we want to return the F1 score for each predicted class.\\n \\n Returns:\\n f1 (float): F1 score of the model\\n (optional) class_scores (dict): Dictionary of per-class F1 score\\n \"\"\"\\n class_scores = {}\\n for discourse_type in gt_df.discourse_type.unique():\\n class_score = score_feedback_comp_micro(pred_df, gt_df, discourse_type)\\n class_scores[discourse_type] = class_score\\n f1 = np.mean([v for v in class_scores.values()])\\n if return_class_scores:\\n return f1, class_scores\\n return f1', 'calculate_overlap': , 'score_feedback_comp_micro': , 'score_feedback_comp': , '_i21': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n \\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", 'train_fn': , '_i22': 'def valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion):\\n oof, valid_loss, valid_acc = get_preds_onefold(model, df_val, dl_val, criterion, valid_flg=True)\\n f1score =[]\\n # classes = oof[\\'class\\'].unique()\\n classes = [\\'Lead\\', \\'Position\\', \\'Claim\\',\\'Counterclaim\\', \\'Rebuttal\\',\\'Evidence\\',\\'Concluding Statement\\']\\n print(f\"Validation F1 scores\")\\n\\n for c in classes:\\n pred_df = oof.loc[oof[\\'class\\'] == c].copy()\\n gt_df = df_val_eval.loc[df_val_eval[\\'discourse_type\\'] == c].copy()\\n f1 = score_feedback_comp(pred_df, gt_df)\\n print(f\\' * {c:<10}: {f1:4f}\\')\\n f1score.append(f1)\\n f1avg = np.mean(f1score)\\n print(f\\'Overall Validation avg F1: {f1avg:.4f} val_loss:{valid_loss:.4f} val_accuracy:{valid_acc:.4f}\\')\\n return valid_loss, oof', 'valid_fn': , '_i23': 'def inference(model, data_loader, criterion, valid_flg):\\n stream = tqdm(data_loader)\\n model.eval()\\n \\n valid_loss = 0\\n valid_accuracy = 0\\n all_logits = None\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch[\\'input_ids\\'].to(device, dtype = torch.long)\\n mask = batch[\\'attention_mask\\'].to(device, dtype = torch.long)\\n with torch.no_grad():\\n raw_logits = model(input_ids=ids, mask = mask)\\n del ids, mask\\n \\n word_ids = batch[\\'word_ids\\'].to(device, dtype = torch.long)\\n logits = active_logits(raw_logits, word_ids)\\n sf_logits = torch.softmax(logits, dim= -1)\\n sf_raw_logits = torch.softmax(raw_logits, dim=-1)\\n if valid_flg: \\n raw_labels = batch[\\'labels\\'].to(device, dtype = torch.long)\\n labels = active_labels(raw_labels)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n valid_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n loss = criterion(logits, labels)\\n valid_loss += loss.item()\\n \\n if batch_idx == 1:\\n all_logits = sf_raw_logits.cpu().numpy()\\n else:\\n all_logits = np.append(all_logits, sf_raw_logits.cpu().numpy(), axis=0)\\n\\n \\n if valid_flg: \\n epoch_loss = valid_loss / batch_idx\\n epoch_accuracy = valid_accuracy / batch_idx\\n else:\\n epoch_loss, epoch_accuracy = 0, 0\\n return all_logits, epoch_loss, epoch_accuracy\\n\\n\\ndef preds_class_prob(all_logits, data_loader):\\n print(\"predict target class and its probabilty\")\\n final_predictions = []\\n final_predictions_score = []\\n stream = tqdm(data_loader)\\n len_sample = all_logits.shape[0]\\n\\n for batch_idx, batch in enumerate(stream, start=0):\\n for minibatch_idx in range(HyperParameters.valid_batch_size):\\n sample_idx = int(batch_idx * HyperParameters.valid_batch_size + minibatch_idx)\\n if sample_idx > len_sample - 1 : break\\n word_ids = batch[\\'word_ids\\'][minibatch_idx].numpy()\\n predictions =[]\\n predictions_prob = []\\n pred_class_id = np.argmax(all_logits[sample_idx], axis=1)\\n pred_score = np.max(all_logits[sample_idx], axis=1)\\n pred_class_labels = [IDS_TO_LABELS[i] for i in pred_class_id]\\n prev_word_idx = -1\\n for idx, word_idx in enumerate(word_ids):\\n if word_idx == -1:\\n pass\\n elif word_idx != prev_word_idx:\\n predictions.append(pred_class_labels[idx])\\n predictions_prob.append(pred_score[idx])\\n prev_word_idx = word_idx\\n final_predictions.append(predictions)\\n final_predictions_score.append(predictions_prob)\\n return final_predictions, final_predictions_score', 'inference': , 'preds_class_prob': , '_i24': 'def get_preds_onefold(model, df, dl, criterion, valid_flg):\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n all_preds, all_preds_prob = preds_class_prob(logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred, valid_loss, valid_acc\\n\\ndef get_preds_folds(model, df, dl, criterion, valid_flg=False):\\n for i_fold in range(HyperParameters.n_fold):\\n model_filename = os.path.join(HyperParameters.model_dir, f\"{HyperParameters.model_savename}_{i_fold}.bin\")\\n print(f\"{model_filename} inference\")\\n model = model.to(device)\\n model.load_state_dict(torch.load(model_filename))\\n logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\\n if i_fold == 0:\\n avg_pred_logits = logits\\n else:\\n avg_pred_logits += logits\\n avg_pred_logits /= HyperParameters.n_fold\\n all_preds, all_preds_prob = preds_class_prob(avg_pred_logits, dl)\\n df_pred = post_process_pred(df, all_preds, all_preds_prob)\\n return df_pred\\n\\ndef post_process_pred(df, all_preds, all_preds_prob):\\n final_preds = []\\n for i in range(len(df)):\\n idx = df.id.values[i]\\n pred = all_preds[i]\\n pred_prob = all_preds_prob[i]\\n j = 0\\n while j < len(pred):\\n cls = pred[j]\\n if cls == \\'O\\': j += 1\\n else: cls = cls.replace(\\'B\\', \\'I\\')\\n end = j + 1\\n while end < len(pred) and pred[end] == cls:\\n end += 1\\n if cls != \\'O\\' and cls !=\\'\\':\\n avg_score = np.mean(pred_prob[j:end])\\n if end - j > MIN_THRESH[cls] and avg_score > PROB_THRESH[cls]:\\n final_preds.append((idx, cls.replace(\\'I-\\', \\'\\'), \\' \\'.join(map(str, list(range(j, end))))))\\n j = end\\n df_pred = pd.DataFrame(final_preds)\\n df_pred.columns = [\\'id\\', \\'class\\', \\'new_predictionstring\\']\\n return df_pred', 'get_preds_onefold': , 'get_preds_folds': , 'post_process_pred': , '_i25': 'print_gpu_utilization()', '_i26': 'def pretty_size(size):\\n\\t\"\"\"Pretty prints a torch.Size object\"\"\"\\n\\tassert(isinstance(size, torch.Size))\\n\\treturn \" × \".join(map(str, size))\\n\\ndef dump_tensors(gpu_only=True):\\n\\t\"\"\"Prints a list of the Tensors being tracked by the garbage collector.\"\"\"\\n\\timport gc\\n\\ttotal_size = 0\\n\\tfor obj in gc.get_objects():\\n\\t\\ttry:\\n\\t\\t\\tif torch.is_tensor(obj):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s:%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.numel()\\n\\t\\t\\telif hasattr(obj, \"data\") and torch.is_tensor(obj.data):\\n\\t\\t\\t\\tif not gpu_only or obj.is_cuda:\\n\\t\\t\\t\\t\\tprint(\"%s → %s:%s%s%s%s %s\" % (type(obj).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t type(obj.data).__name__, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" GPU\" if obj.is_cuda else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" pinned\" if obj.data.is_pinned else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" grad\" if obj.requires_grad else \"\", \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \" volatile\" if obj.volatile else \"\",\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t pretty_size(obj.data.size())))\\n\\t\\t\\t\\t\\ttotal_size += obj.data.numel()\\n\\t\\texcept Exception as e:\\n\\t\\t\\tpass \\n\\tprint(\"Total size:\", total_size)', 'pretty_size': , 'dump_tensors': , '_i27': 'dump_tensors()', '_i28': 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'oof': Empty DataFrame\n",
+ "Columns: []\n",
+ "Index: [], 'i_fold': 0, 'tokenizer': PreTrainedTokenizerFast(name_or_path='allenai/longformer-base-4096', vocab_size=50265, model_max_len=4096, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '', 'sep_token': ' ', 'pad_token': '', 'cls_token': '', 'mask_token': AddedToken(\"\", rstrip=False, lstrip=True, single_word=False, normalized=False)}), 'optimizer': Adam (\n",
+ "Parameter Group 0\n",
+ " amsgrad: False\n",
+ " betas: (0.9, 0.999)\n",
+ " eps: 1e-08\n",
+ " lr: 4e-05\n",
+ " weight_decay: 0\n",
+ "), 'df_train': id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "3 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "4 616F8E0EFABF \"Can you imagine a time in the future when no ... \n",
+ "... ... ... \n",
+ "12472 1C899F124FEB While some students may think it's a beneficia... \n",
+ "12473 4453444AF383 There has been a strong arguement going on wea... \n",
+ "12474 EF0D75BF48DA I favor in to changing election by popular vot... \n",
+ "12475 8FFDA5B9D359 Do you think students would benefit from being... \n",
+ "12476 ACAB1FCA0A30 I would like to change the election for the pr... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [People, sometimes, have, a, different, opinio... \n",
+ "3 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "4 [\"Can, you, imagine, a, time, in, the, future,... \n",
+ "... ... \n",
+ "12472 [While, some, students, may, think, it's, a, b... \n",
+ "12473 [There, has, been, a, strong, arguement, going... \n",
+ "12474 [I, favor, in, to, changing, election, by, pop... \n",
+ "12475 [Do, you, think, students, would, benefit, fro... \n",
+ "12476 [I, would, like, to, change, the, election, fo... \n",
+ "\n",
+ " entities kfold \\\n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "3 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 \n",
+ "4 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 1 \n",
+ "... ... ... \n",
+ "12472 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "12473 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "12474 [B-Position, I-Position, I-Position, I-Positio... 3 \n",
+ "12475 [B-Position, I-Position, I-Position, I-Positio... 2 \n",
+ "12476 [B-Position, I-Position, I-Position, I-Positio... 4 \n",
+ "\n",
+ " labels \n",
+ "0 [1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "1 [0, 0, 0, 0, 0, 0, 0, 0, 2, 9, 9, 9, 9, 9, 9, ... \n",
+ "2 [1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "3 [0, 0, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "4 [1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "... ... \n",
+ "12472 [1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "12473 [1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "12474 [2, 9, 9, 9, 9, 9, 9, 9, 9, 4, 11, 11, 11, 11,... \n",
+ "12475 [2, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, ... \n",
+ "12476 [2, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, ... \n",
+ "\n",
+ "[12477 rows x 6 columns], 'ds_train': <__main__.FeedbackPrizeDataset object at 0x7faf5a68b510>, 'df_val': id text \\\n",
+ "0 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "1 F9368F6BAB62 You should join the Seagoing Cowboys program. ... \n",
+ "2 617D56A15483 Do you want to go on daily trips and help peop... \n",
+ "3 D4AA8EC389C6 School Grades:\\n\\nI don't think that it is fai... \n",
+ "4 D92FAEE355E1 Dear Mrs. Principal:\\n\\nI think this new schoo... \n",
+ "... ... ... \n",
+ "3112 3BEFA845FC27 There are people all around the world that mak... \n",
+ "3113 F166DA36A4A3 In this essay im going to prove to you why tha... \n",
+ "3114 8F33151F0334 He joined the programe for many reasons to hel... \n",
+ "3115 DDA2A181F486 I'm against this technology that reads your em... \n",
+ "3116 ACCD71550365 Do you think it is a good idea to succeed? I b... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "1 [You, should, join, the, Seagoing, Cowboys, pr... \n",
+ "2 [Do, you, want, to, go, on, daily, trips, and,... \n",
+ "3 [School, Grades:, I, don't, think, that, it, i... \n",
+ "4 [Dear, Mrs., Principal:, I, think, this, new, ... \n",
+ "... ... \n",
+ "3112 [There, are, people, all, around, the, world, ... \n",
+ "3113 [In, this, essay, im, going, to, prove, to, yo... \n",
+ "3114 [He, joined, the, programe, for, many, reasons... \n",
+ "3115 [I'm, against, this, technology, that, reads, ... \n",
+ "3116 [Do, you, think, it, is, a, good, idea, to, su... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "1 [B-Position, I-Position, I-Position, I-Positio... 0 \n",
+ "2 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 0 \n",
+ "3 [O, O, B-Position, I-Position, I-Position, I-P... 0 \n",
+ "4 [O, O, O, B-Position, I-Position, I-Position, ... 0 \n",
+ "... ... ... \n",
+ "3112 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 0 \n",
+ "3113 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 0 \n",
+ "3114 [B-Evidence, I-Evidence, I-Evidence, I-Evidenc... 0 \n",
+ "3115 [B-Position, I-Position, I-Position, I-Positio... 0 \n",
+ "3116 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 0 \n",
+ "\n",
+ "[3117 rows x 5 columns], 'val_idlist': ['2E4AFCD3987F', 'F9368F6BAB62', '617D56A15483', 'D4AA8EC389C6', 'D92FAEE355E1', '58EE193B3F7D', '4A2693C68175', '3741D275F8BC', 'FC9E2A38AFBB', '8B5CB2AA72E9', '2DEC8D3BA891', '217A7429341D', '7E558B4E7009', 'CC38587E9634', '8DD596F5F5B5', '632C8DA9E2B1', '76C8FE4FB3D8', 'D7D66E258830', 'C20011D6FA9D', '508DB90F68C7', '1655FBD1D22A', '0DE6D016C7FC', '1D07A65F9B7F', '0257D9E42247', '31DB1BDE89A1', '13FB62D377E3', '33C081EC2A59', '90691C2B3D5A', '6D6215C23989', '7FB898313EA9', 'BCAE5B0783CF', 'C5C81327D129', '5942FB27D5C3', '185FD725317F', 'ACBC86CD65B4', '027616F7ED1D', '6068A54ED99D', 'B75E90DC14B3', 'BA545022DFCD', '2D654BBA9310', 'F53C55BBD326', '0821EB1AEDED', '7F74864D0865', 'DCDBC5C7873F', 'EA9FA5DA65F3', '486B53D5F8A0', '1CCEB6CD629D', '1F8DDCFCA766', 'DBCC1F8F92B5', '51E211998DF0', 'D447CD5A0C67', 'C50FE04C4D29', '32B2D5592E1D', '2962D1BB17A2', '59E8C8BE4ACA', '155F508445E2', 'C3F35CEC4B5A', 'B5113469D13C', 'E09064F496DF', 'E5EF52219F63', 'C43C4EE10535', '2DFFB145BE07', '2F3210D23414', '084DC646D8E3', '2B002FEE337E', '8DB4751C05A1', '8C7464101130', '3752D33FDE11', 'F3CB6601CF24', 'FE1DEACABE37', 'BF0241C10324', '8F91417B8A7D', '9DBB8B0C3367', '69891F080D3A', 'EE63F23F10B3', 'D7C33D289E26', '325B922888A7', '3A08788DCA7D', 'CFA4E76C506A', 'B3C2E6274311', '7F77FE73B338', 'E1130AA34EE8', 'CFB99F0D0D31', '3712A5E56380', '1B1FBDE25C2C', '6D6B05E5478D', 'D1673363C56A', '1BA91BC3D89A', 'D5724EDF0DBA', '5FFC25FE5E00', '6ED00F0E1BFE', '4C0EB60473E1', 'F605023B87A7', '2617310033A4', 'D284AB2165C8', '29B80B7D1092', 'EA205A51D32C', '5137514F130A', '0C0A8EBDC931', 'BF5D82D486F5', '4A8AC603C9C5', '3188A9E2D162', '92887B4B5F2A', 'AE844BA96314', '21A162D4B0CB', '4390794B2751', '46E97FDCA469', 'B5838ACCDBA5', '318B9B177D68', '41A159819E12', '29BE597866EE', '9396FCEB118A', '48FB3005F4DC', '8134AD7791FC', '8E938A38BA43', '220A26E7E802', '04ACF5873DE9', 'ED4186119E60', '1CC9A9598942', '9A5D8C898877', 'D6466B4502D5', 'B60A2DC4B93C', '7ECB7FDA805A', '3AFA78FFFC15', '611228818239', '619D1D7FDC04', '85255F76A261', '94CCB4234EAE', '971D0C1C75F0', '710A2280E20F', '7B64BC831CD9', 'C9DA3BE5EB24', '479F6A965C8C', 'D24777F39289', 'D7946F0C2A83', '65590D44B68A', '2B4A794ED0D8', '242E9BA55C0C', 'E3F3C192B3A2', '61671FCB6624', '3F8C4D2CA16E', '2E9B5CD4716D', 'D022656B1A36', 'D5CB4FD63313', 'D66B89FC3757', 'D7401AE3A016', 'AC7C84C2A13E', '727DE3CD0AEC', '7C1FF9F7C5C8', '2A7C4A1CBA41', 'BA18C6F80423', '23C6ED53A179', 'C05809232F0E', 'C8D3EB90EB29', '4AD3DB40F5D3', '46284DF2F31C', 'AF34B1CDD152', 'A6CC10322C3A', '0DBE82E9933F', 'F5A0BD46D3AF', '3CA61DE9B5C6', '899E6252BD6F', 'EA5E9342C547', '89CD5C5D9180', 'FE47325AD93F', '6CD4FAAB7653', 'F503C7DC334F', '870D9B35F20A', '37FC9DB2D1DB', '8C9A286F5351', '7AD4CB4029C0', 'BAEE45819FA1', '7BBC565EFE33', 'F44F49940938', 'E7576760695E', '116FB053BEC7', '5EF4174A5264', 'B651AEF954C3', '06E3595C94BF', '0054850878E3', '7091F3705B94', 'FC90D46FBE1F', 'DE1EF5A9F3AA', 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'5B1604AE437E', '8A1A6161F47C', '27DE9583F170', '07C7A31386A0', 'B7C3128E0973', '39B6908F453E', 'D2E2D4CCBF8C', 'CFE773F1600B', '527EB3C89F2F', 'A66B88F03338', 'B936994E140D', '497079209629', '102F13317092', 'C4ED4217DA09', '45BF01B7EF4D', '3BEFA845FC27', 'F166DA36A4A3', '8F33151F0334', 'DDA2A181F486', 'ACCD71550365'], 'df_val_eval': id discourse_id discourse_start discourse_end \\\n",
+ "0 A8445CABFECE 1.622576e+12 18.0 85.0 \n",
+ "1 A8445CABFECE 1.622576e+12 86.0 202.0 \n",
+ "2 A8445CABFECE 1.622576e+12 203.0 1030.0 \n",
+ "3 A8445CABFECE 1.622576e+12 1031.0 1243.0 \n",
+ "4 A97DE0D49AEA 1.622645e+12 63.0 129.0 \n",
+ "... ... ... ... ... \n",
+ "28992 0814426B27DF 1.617896e+12 1440.0 1563.0 \n",
+ "28993 0814426B27DF 1.617896e+12 1564.0 1955.0 \n",
+ "28994 0814426B27DF 1.617896e+12 1956.0 2003.0 \n",
+ "28995 0814426B27DF 1.617896e+12 2004.0 2075.0 \n",
+ "28996 0814426B27DF 1.617896e+12 2076.0 2359.0 \n",
+ "\n",
+ " discourse_text \\\n",
+ "0 Drivers should not be able to use phones while... \n",
+ "1 Drivers who used their phone while operating a... \n",
+ "2 According to an article by the Edgar Snyder Fi... \n",
+ "3 In conclusion, drivers should not able to work... \n",
+ "4 Driver's should desist from using their Cell ... \n",
+ "... ... \n",
+ "28992 that one of the people you asked for advice ma... \n",
+ "28993 Some don't think all the same things if you we... \n",
+ "28994 Some people will disagree with my three reasons \n",
+ "28995 but I don't like to listen to the people who d... \n",
+ "28996 Maybe this helped you maybe it didn't.\\n\\nTo s... \n",
+ "\n",
+ " discourse_type discourse_type_num \\\n",
+ "0 Position Position 1 \n",
+ "1 Claim Claim 1 \n",
+ "2 Evidence Evidence 1 \n",
+ "3 Concluding Statement Concluding Statement 1 \n",
+ "4 Position Position 1 \n",
+ "... ... ... \n",
+ "28992 Claim Claim 5 \n",
+ "28993 Evidence Evidence 3 \n",
+ "28994 Counterclaim Counterclaim 1 \n",
+ "28995 Rebuttal Rebuttal 1 \n",
+ "28996 Concluding Statement Concluding Statement 1 \n",
+ "\n",
+ " predictionstring \\\n",
+ "0 3 4 5 6 7 8 9 10 11 12 13 14 \n",
+ "1 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 3... \n",
+ "2 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 5... \n",
+ "3 177 178 179 180 181 182 183 184 185 186 187 18... \n",
+ "4 11 12 13 14 15 16 17 18 19 20 \n",
+ "... ... \n",
+ "28992 290 291 292 293 294 295 296 297 298 299 300 30... \n",
+ "28993 315 316 317 318 319 320 321 322 323 324 325 32... \n",
+ "28994 391 392 393 394 395 396 397 398 \n",
+ "28995 399 400 401 402 403 404 405 406 407 408 409 41... \n",
+ "28996 413 414 415 416 417 418 419 420 421 422 423 42... \n",
+ "\n",
+ " text_by_index new_start new_end \\\n",
+ "0 Drivers should not be able to use phones while... 18 86 \n",
+ "1 Drivers who used their phone while operating a... 86 202 \n",
+ "2 According to an article by the Edgar Snyder Fi... 203 1030 \n",
+ "3 In conclusion, drivers should not able to work... 1031 1231 \n",
+ "4 Driver's should desist from using their Cell ... 64 129 \n",
+ "... ... ... ... \n",
+ "28992 that one of the people you asked for advice ma... 1440 1563 \n",
+ "28993 Some don't think all the same things if you we... 1564 1955 \n",
+ "28994 Some people will disagree with my three reasons 1956 2004 \n",
+ "28995 but I don't like to listen to the people who d... 2004 2075 \n",
+ "28996 Maybe this helped you maybe it didn't.\\n\\nTo s... 2076 2359 \n",
+ "\n",
+ " text_by_new_index \\\n",
+ "0 Drivers should not be able to use phones while... \n",
+ "1 Drivers who used their phone while operating a... \n",
+ "2 According to an article by the Edgar Snyder Fi... \n",
+ "3 In conclusion, drivers should not able to work... \n",
+ "4 Driver's should desist from using their Cell P... \n",
+ "... ... \n",
+ "28992 that one of the people you asked for advice ma... \n",
+ "28993 Some don't think all the same things if you we... \n",
+ "28994 Some people will disagree with my three reasons \n",
+ "28995 but I don't like to listen to the people who d... \n",
+ "28996 Maybe this helped you maybe it didn't.\\n\\nTo s... \n",
+ "\n",
+ " new_predictionstring \n",
+ "0 3 4 5 6 7 8 9 10 11 12 13 14 \n",
+ "1 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 3... \n",
+ "2 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 5... \n",
+ "3 177 178 179 180 181 182 183 184 185 186 187 18... \n",
+ "4 11 12 13 14 15 16 17 18 19 20 \n",
+ "... ... \n",
+ "28992 290 291 292 293 294 295 296 297 298 299 300 30... \n",
+ "28993 315 316 317 318 319 320 321 322 323 324 325 32... \n",
+ "28994 391 392 393 394 395 396 397 398 \n",
+ "28995 399 400 401 402 403 404 405 406 407 408 409 41... \n",
+ "28996 413 414 415 416 417 418 419 420 421 422 423 42... \n",
+ "\n",
+ "[28997 rows x 13 columns], 'ds_val': <__main__.FeedbackPrizeDataset object at 0x7faf5a76b410>, 'dl_train': , 'dl_val': , 'best_val_loss': inf, 'criterion': CrossEntropyLoss(), 'epoch': 1, '_i29': 'try:\\n print(gc.get_referrers(model))\\n del model\\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i30': 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', 'KaggleDataset': , '_i31': 'try:\\n print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i32': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i33': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n print(df_train.head())\\n raise Exception()\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'MAPPING': {'O': 0, 'B-Lead': 1, 'B-Position': 2, 'B-Evidence': 3, 'B-Claim': 4, 'B-Concluding Statement': 5, 'B-Counterclaim': 6, 'B-Rebuttal': 7, 'I-Lead': 8, 'I-Position': 9, 'I-Evidence': 10, 'I-Claim': 11, 'I-Concluding Statement': 12, 'I-Counterclaim': 13, 'I-Rebuttal': 14}, '_i34': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i35': 'class KaggleDataset(Dataset):\\n \"\"\"\\n Class for loading data in batches after it has been processed\\n \"\"\"\\n def __init__(self, dataframe, tokenizer, max_length):\\n\\n super().__init__()\\n\\n # -- prepare data\\n assert sorted(dataframe.columns) == [\\'labels\\', \\'text\\'], f\"Please make sure input dataframe has the columns (text, labels)\"\\n # data must be in the correct format\\n self.inputs = dataframe.text.values\\n self.targets = dataframe.labels.values\\n #if not is_string_dtype(self.inputs): raise TypeError(\\'Text data must be string type\\')\\n # TODO assertion below is bug; not deleting so remember to add correct assertions\\n #if not is_integer_dtype(self.targets): raise TypeError(\\'Label data must be integer type\\')\\n\\n # -- prepare tokenizer\\n self.tokenizer = tokenizer\\n self.max_length = max_length\\n\\n def __len__(self):\\n return len(self.inputs)\\n\\n def __getitem__(self, index):\\n # self.inputs anf self.targets must be of a type that is indexible as shown\\n inputs = self.inputs[index]\\n targets = self.targets[index]\\n\\n inputs = self.tokenizer(\\n # consider parametrising these\\n inputs.split(),\\n is_split_into_words=True, # this means that extra \\\\n should be ignored\\n padding=\\'max_length\\',\\n truncation=True,\\n max_length=self.max_length\\n )\\n\\n word_ids = inputs.word_ids()\\n word_id_mask = [word_id is not None for word_id in word_ids]\\n word_ids = [word_id for word_id in word_ids if word_id is not None]\\n\\n inputs = {\\n key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\\n }\\n targets = torch.as_tensor(targets, dtype=torch.long)\\n expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\\n expanded_targets[word_id_mask] = targets[word_ids]\\n inputs[\\'labels\\'] = expanded_targets\\n inputs[\\'word_ids\\'] = torch.tensor(word_ids, dtype=torch.long)\\n return inputs\\n\\n ', '_i36': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i37': \"class FeedbackModel(nn.Module):\\n def __init__(self):\\n super(FeedbackModel, self).__init__()\\n \\n # init config of transformer model of choice:\\n # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\\n model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\\n print(model_config)\\n self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\\n \\n # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\\n self.model_config = model_config\\n self.dropout1 = nn.Dropout(0.1)\\n self.dropout2 = nn.Dropout(0.2)\\n self.dropout3 = nn.Dropout(0.3)\\n self.dropout4 = nn.Dropout(0.4)\\n self.dropout5 = nn.Dropout(0.5)\\n self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\\n \\n def forward(self, input_ids, mask):\\n x = self.backbone(input_ids, mask)\\n logits1 = self.head(self.dropout1(x[0]))\\n logits2 = self.head(self.dropout2(x[0]))\\n logits3 = self.head(self.dropout3(x[0]))\\n logits4 = self.head(self.dropout4(x[0]))\\n logits5 = self.head(self.dropout5(x[0]))\\n logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\\n return logits\", '_i38': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 1024))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i39': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i40': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(raw_logits.shape)\\n print(word_ids.shape)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n logits = active_logits(raw_logits, word_ids)\\n labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", '_i41': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", '_i42': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i43': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", '_i44': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', '_i45': \"def train_fn(model, train_data_loader, optimizer, epoch, criterion):\\n print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\\n model.train()\\n train_loss = 0\\n train_accuracy = 0\\n stream = tqdm(train_data_loader)\\n # Init gradscaler to ensure everything works smoothly on cuda\\n scaler = GradScaler()\\n print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\\n for batch_idx, batch in enumerate(stream, start = 1):\\n ids = batch['input_ids'].to(device, dtype = torch.long)\\n mask = batch['attention_mask'].to(device, dtype = torch.long)\\n raw_labels = batch['labels'].to(device, dtype = torch.long)\\n word_ids = batch['word_ids'].to(device, dtype = torch.long)\\n print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\\n\\n optimizer.zero_grad()\\n print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\\n\\n # Calculate output with autocast for cuda support\\n with autocast():\\n raw_logits = model(input_ids = ids, mask = mask)\\n print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\\n\\n #logits = active_logits(raw_logits, word_ids)\\n #labels = active_labels(raw_labels)\\n\\n logits = raw_logits\\n labels = raw_labels\\n sf_logits = torch.softmax(logits, dim=-1)\\n preds, preds_prob = active_preds_prob(sf_logits)\\n train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\\n criterion = nn.CrossEntropyLoss()\\n loss = criterion(logits, labels)\\n\\n print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\\n\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n train_loss += loss.item()\\n print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\\n\\n \\n if batch_idx % HyperParameters.verbose_steps == 0:\\n loss_step = train_loss / batch_idx\\n print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\\n\\n if batch_idx == 3:\\n raise Exception()\\n \\n epoch_loss = train_loss / batch_idx\\n epoch_accuracy = train_accuracy / batch_idx \\n print_gpu_utilization()\\n # Cleanup\\n del train_data_loader, raw_logits, logits, raw_labels, preds, labels\\n torch.cuda.empty_cache()\\n gc.collect()\\n \\n print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\\n print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')\", '_i46': 'try:\\n #print(gc.get_referrers(model))\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])', 'model': FeedbackModel(\n",
+ " (backbone): LongformerModel(\n",
+ " (embeddings): LongformerEmbeddings(\n",
+ " (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
+ " (position_embeddings): Embedding(4098, 768, padding_idx=1)\n",
+ " (token_type_embeddings): Embedding(1, 768)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (encoder): LongformerEncoder(\n",
+ " (layer): ModuleList(\n",
+ " (0): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (1): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (2): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
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+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (3): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
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+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
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+ " )\n",
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+ " (4): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
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+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
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+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
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+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (5): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (6): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (7): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
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+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
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+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (8): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
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+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (9): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (10): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (11): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " )\n",
+ " (output): LongformerSelfOutput(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (intermediate): LongformerIntermediate(\n",
+ " (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
+ " (intermediate_act_fn): GELUActivation()\n",
+ " )\n",
+ " (output): LongformerOutput(\n",
+ " (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (pooler): LongformerPooler(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (activation): Tanh()\n",
+ " )\n",
+ " )\n",
+ " (dropout1): Dropout(p=0.1, inplace=False)\n",
+ " (dropout2): Dropout(p=0.2, inplace=False)\n",
+ " (dropout3): Dropout(p=0.3, inplace=False)\n",
+ " (dropout4): Dropout(p=0.4, inplace=False)\n",
+ " (dropout5): Dropout(p=0.5, inplace=False)\n",
+ " (head): Linear(in_features=768, out_features=15, bias=True)\n",
+ "), '_i47': 'try:\\n print(gc.get_referrers(model))\\n raise Exception()\\n del model, \\n torch.cuda.empty_cache()\\n gc.collect()\\n print(\\'Model existed. Deleting...\\')\\n print_gpu_utilization()\\nexcept:\\n print(\\'Model NOT found... new model will be created\\')\\n print_gpu_utilization()\\n\\nMAPPING = {\\n \"O\": 0,\\n \"B-Lead\": 1,\\n \"B-Position\": 2,\\n \"B-Evidence\": 3,\\n \"B-Claim\": 4,\\n \"B-Concluding Statement\": 5,\\n \"B-Counterclaim\": 6,\\n \"B-Rebuttal\": 7,\\n \"I-Lead\": 8,\\n \"I-Position\": 9,\\n \"I-Evidence\": 10,\\n \"I-Claim\": 11,\\n \"I-Concluding Statement\": 12,\\n \"I-Counterclaim\": 13,\\n \"I-Rebuttal\": 14,\\n}\\noof = pd.DataFrame()\\n\\nfor i_fold in range(HyperParameters.n_fold):\\n print(f\\'=== fold{i_fold} training ===\\')\\n print(f\\'First run: {print_gpu_utilization()}\\')\\n model, tokenizer = build_model_tokenizer()\\n model = model.to(device)\\n print(f\\'Model and tokeniser: {print_gpu_utilization()}\\')\\n\\n optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\\n print(f\\'Optimizer: {print_gpu_utilization()}\\')\\n\\n df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\\n df_train = df_train.assign(labels=lambda x: x[\\'entities\\'].apply(lambda x: [MAPPING[key] for key in x]))\\n ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\\n df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\\n val_idlist = df_val[\\'id\\'].unique().tolist()\\n df_val_eval = df_alltrain.query(\\'id==@val_idlist\\').reset_index(drop=True)\\n ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\\n dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\\n dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\\n\\n dl_train = DataLoader(KaggleDataset(df_train[[\\'text\\', \\'labels\\']], tokenizer, 532))\\n best_val_loss = np.inf\\n criterion = nn.CrossEntropyLoss()\\n print(f\\'Prior to training, GPU utilisation is: {print_gpu_utilization()}\\')\\n for epoch in range(1, HyperParameters.n_epoch + 1):\\n train_fn(model, dl_train, optimizer, epoch, criterion)\\n valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\\n if valid_loss < best_val_loss:\\n best_val_loss = valid_loss\\n _oof_fold_best = _oof\\n _oof_fold_best[\"kfold\"] = i_fold\\n model_filename = f\\'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin\\'\\n \\n # Saving the boy\\n torch.save(model.state_dict(), model_filename)\\n print(f\\'{model_filename} saved\\')\\n\\n oof = pd.concat([oof, _oof_fold_best])'}]\n",
+ "Model NOT found... new model will be created\n",
+ "GPU memory occupied: 8527 MB.\n",
+ "=== fold0 training ===\n",
+ "GPU memory occupied: 8527 MB.\n",
+ "First run: None\n",
+ "LongformerConfig {\n",
+ " \"_name_or_path\": \"allenai/longformer-base-4096\",\n",
+ " \"attention_mode\": \"longformer\",\n",
+ " \"attention_probs_dropout_prob\": 0.1,\n",
+ " \"attention_window\": [\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512,\n",
+ " 512\n",
+ " ],\n",
+ " \"bos_token_id\": 0,\n",
+ " \"classifier_dropout\": null,\n",
+ " \"eos_token_id\": 2,\n",
+ " \"gradient_checkpointing\": false,\n",
+ " \"hidden_act\": \"gelu\",\n",
+ " \"hidden_dropout_prob\": 0.1,\n",
+ " \"hidden_size\": 768,\n",
+ " \"ignore_attention_mask\": false,\n",
+ " \"initializer_range\": 0.02,\n",
+ " \"intermediate_size\": 3072,\n",
+ " \"layer_norm_eps\": 1e-05,\n",
+ " \"max_position_embeddings\": 4098,\n",
+ " \"model_type\": \"longformer\",\n",
+ " \"num_attention_heads\": 12,\n",
+ " \"num_hidden_layers\": 12,\n",
+ " \"pad_token_id\": 1,\n",
+ " \"position_embedding_type\": \"absolute\",\n",
+ " \"sep_token_id\": 2,\n",
+ " \"transformers_version\": \"4.17.0\",\n",
+ " \"type_vocab_size\": 1,\n",
+ " \"use_cache\": true,\n",
+ " \"vocab_size\": 50265\n",
+ "}\n",
+ "\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the model checkpoint at allenai/longformer-base-4096 were not used when initializing LongformerModel: ['lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias']\n",
+ "- This IS expected if you are initializing LongformerModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing LongformerModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "GPU memory occupied: 9131 MB.\n",
+ "Model and tokeniser: None\n",
+ "GPU memory occupied: 9131 MB.\n",
+ "Optimizer: None\n",
+ "GPU memory occupied: 9131 MB.\n",
+ "Prior to training, GPU utilisation is: None\n",
+ "Training for epoch 1 started. GPU utilisation: 9131\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "eef7a90afeb44c448532e10c8ef5a20e",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ " 0%| | 0/12477 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Prior to training, GPU utilisation is: {print_gpu_utilization()}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mHyperParameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epoch\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m \u001b[0mtrain_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 58\u001b[0m \u001b[0mvalid_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_oof\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalid_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_val_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalid_loss\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mbest_val_loss\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mtrain_fn\u001b[0;34m(model, train_data_loader, optimizer, epoch, criterion)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0msf_logits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds_prob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mactive_preds_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msf_logits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0mtrain_accuracy\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 33\u001b[0m \u001b[0mcriterion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCrossEntropyLoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36maccuracy_score\u001b[0;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0;31m# Compute accuracy for each possible representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 212\u001b[0m \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"multilabel\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;31m# No metrics support \"multiclass-multioutput\" format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multilabel-indicator\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0} is not supported\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mValueError\u001b[0m: multiclass-multioutput is not supported"
+ ]
+ }
+ ],
+ "source": [
+ "try:\n",
+ " print(gc.get_referrers(model))\n",
+ " raise Exception()\n",
+ " del model, \n",
+ " torch.cuda.empty_cache()\n",
+ " gc.collect()\n",
+ " print('Model existed. Deleting...')\n",
+ " print_gpu_utilization()\n",
+ "except:\n",
+ " print('Model NOT found... new model will be created')\n",
+ " print_gpu_utilization()\n",
+ "\n",
+ "MAPPING = {\n",
+ " \"O\": 0,\n",
+ " \"B-Lead\": 1,\n",
+ " \"B-Position\": 2,\n",
+ " \"B-Evidence\": 3,\n",
+ " \"B-Claim\": 4,\n",
+ " \"B-Concluding Statement\": 5,\n",
+ " \"B-Counterclaim\": 6,\n",
+ " \"B-Rebuttal\": 7,\n",
+ " \"I-Lead\": 8,\n",
+ " \"I-Position\": 9,\n",
+ " \"I-Evidence\": 10,\n",
+ " \"I-Claim\": 11,\n",
+ " \"I-Concluding Statement\": 12,\n",
+ " \"I-Counterclaim\": 13,\n",
+ " \"I-Rebuttal\": 14,\n",
+ "}\n",
+ "oof = pd.DataFrame()\n",
+ "\n",
+ "for i_fold in range(HyperParameters.n_fold):\n",
+ " print(f'=== fold{i_fold} training ===')\n",
+ " print(f'First run: {print_gpu_utilization()}')\n",
+ " model, tokenizer = build_model_tokenizer()\n",
+ " model = model.to(device)\n",
+ " print(f'Model and tokeniser: {print_gpu_utilization()}')\n",
+ "\n",
+ " optimizer = torch.optim.Adam(params=model.parameters(), lr=HyperParameters.lr)\n",
+ " print(f'Optimizer: {print_gpu_utilization()}')\n",
+ "\n",
+ " df_train = alltrain_texts[alltrain_texts[\"kfold\"] != i_fold].reset_index(drop = True)\n",
+ " df_train = df_train.assign(labels=lambda x: x['entities'].apply(lambda x: [MAPPING[key] for key in x]))\n",
+ " ds_train = FeedbackPrizeDataset(df_train, tokenizer, HyperParameters.max_length, True)\n",
+ " df_val = alltrain_texts[alltrain_texts[\"kfold\"] == i_fold].reset_index(drop = True)\n",
+ " val_idlist = df_val['id'].unique().tolist()\n",
+ " df_val_eval = df_alltrain.query('id==@val_idlist').reset_index(drop=True)\n",
+ " ds_val = FeedbackPrizeDataset(df_val, tokenizer, HyperParameters.max_length, True)\n",
+ " dl_train = DataLoader(ds_train, batch_size=HyperParameters.train_batch_size, shuffle=True, num_workers=2, pin_memory=True)\n",
+ " dl_val = DataLoader(ds_val, batch_size=HyperParameters.valid_batch_size, shuffle=False, num_workers=2, pin_memory=True)\n",
+ "\n",
+ " dl_train = DataLoader(KaggleDataset(df_train[['text', 'labels']], tokenizer, 532))\n",
+ " best_val_loss = np.inf\n",
+ " criterion = nn.CrossEntropyLoss()\n",
+ " print(f'Prior to training, GPU utilisation is: {print_gpu_utilization()}')\n",
+ " for epoch in range(1, HyperParameters.n_epoch + 1):\n",
+ " train_fn(model, dl_train, optimizer, epoch, criterion)\n",
+ " valid_loss, _oof = valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion)\n",
+ " if valid_loss < best_val_loss:\n",
+ " best_val_loss = valid_loss\n",
+ " _oof_fold_best = _oof\n",
+ " _oof_fold_best[\"kfold\"] = i_fold\n",
+ " model_filename = f'{HyperParameters.model_dir}/{HyperParameters.model_savename}_{i_fold}.bin'\n",
+ " \n",
+ " # Saving the boy\n",
+ " torch.save(model.state_dict(), model_filename)\n",
+ " print(f'{model_filename} saved')\n",
+ "\n",
+ " oof = pd.concat([oof, _oof_fold_best])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hlmZeM47UxuT"
+ },
+ "outputs": [],
+ "source": [
+ "oof.to_csv(f'{HyperParameters.output_dir}/oof_{HyperParameters.name}.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aO8hcaQ8UxuT"
+ },
+ "source": [
+ "Looking at performance:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "VOcFtSUhUxuT"
+ },
+ "outputs": [],
+ "source": [
+ "if HyperParameters.is_debug:\n",
+ " idlist = alltrain_texts['id'].unique().tolist()\n",
+ " df_train = df_alltrain.query('id==@idlist')\n",
+ "else:\n",
+ " df_train = df_alltrain.copy()\n",
+ "print(f'overall cv score: {score_feedback_comp(df_train, oof)}')"
+ ]
+ }
+ ],
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diff --git a/experiments/yousef/PrepareData.ipynb b/experiments/yousef/PrepareData.ipynb
new file mode 100644
index 0000000..21aba41
--- /dev/null
+++ b/experiments/yousef/PrepareData.ipynb
@@ -0,0 +1,231 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "ed110207",
+ "metadata": {},
+ "source": [
+ "# Prepare Data\n",
+ "\n",
+ "Notebook for preparing the datasets that we will be using"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b435bf71",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from argminer.data import PersuadeProcessor, TUDarmstadtProcessor\n",
+ "\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f7721f4",
+ "metadata": {},
+ "source": [
+ "# TU Darmstadt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "34ba8e46",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "murdering criminals is therefore immoral and hard to accept\n",
+ "\n",
+ "\"murdering\" criminals is therefore immoral and hard to accept\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "Click is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\n",
+ "\"Click\" is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "Blood diamond, an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\n",
+ "\"Blood diamond\", an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "rush hours are usually not the direct consequence of drivers\n",
+ "\n",
+ "\"rush hours\" are usually not the direct consequence of drivers\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path = '../../data/UCL/dataset2/ArgumentAnnotatedEssays-2.0'\n",
+ "\n",
+ "processor = TUDarmstadtProcessor(path)\n",
+ "processor = processor.preprocess()\n",
+ "processor.save_json('../../data/UCL/core')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "cd73bf87",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Errno 17] File exists: '../../data/bio'\n"
+ ]
+ }
+ ],
+ "source": [
+ "for strategy in ['bio', 'io', 'bieo']:\n",
+ " processor = TUDarmstadtProcessor('../../data/UCL/core').from_json('preprocessed')\n",
+ " try:\n",
+ " os.mkdir(f'../../data/{strategy}')\n",
+ " except Exception as e:\n",
+ " print(e)\n",
+ " processor = processor.process(strategy).postprocess()\n",
+ " processor.save_json(f'../../data/{strategy}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1db8724a",
+ "metadata": {},
+ "source": [
+ "# Persuade"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "cda8ad52",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Errno 17] File exists: '../../data/bio'\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_66818/2749773426.py:10: UserWarning: PersuadeProcessor does not have a preprocessor. Instead the postprocess method will prepare the data end-to-end\n",
+ " processor = processor.preprocess().process(strategy).postprocess()\n",
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_66818/2749773426.py:10: UserWarning: PersuadeProcessor does not have a processor. Instead the postprocess method will prepare the data end-to-end\n",
+ " processor = processor.preprocess().process(strategy).postprocess()\n",
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_66818/2749773426.py:10: DeprecationWarning: The postprocess method is behaving in a special way because of data corruption. This behaviour will change in the future.\n",
+ " processor = processor.preprocess().process(strategy).postprocess()\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Errno 17] File exists: '../../data/io'\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_66818/2749773426.py:10: UserWarning: PersuadeProcessor does not have a preprocessor. Instead the postprocess method will prepare the data end-to-end\n",
+ " processor = processor.preprocess().process(strategy).postprocess()\n",
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_66818/2749773426.py:10: UserWarning: PersuadeProcessor does not have a processor. Instead the postprocess method will prepare the data end-to-end\n",
+ " processor = processor.preprocess().process(strategy).postprocess()\n",
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_66818/2749773426.py:10: DeprecationWarning: The postprocess method is behaving in a special way because of data corruption. This behaviour will change in the future.\n",
+ " processor = processor.preprocess().process(strategy).postprocess()\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Errno 17] File exists: '../../data/bieo'\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_66818/2749773426.py:10: UserWarning: PersuadeProcessor does not have a preprocessor. Instead the postprocess method will prepare the data end-to-end\n",
+ " processor = processor.preprocess().process(strategy).postprocess()\n",
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_66818/2749773426.py:10: UserWarning: PersuadeProcessor does not have a processor. Instead the postprocess method will prepare the data end-to-end\n",
+ " processor = processor.preprocess().process(strategy).postprocess()\n",
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_66818/2749773426.py:10: DeprecationWarning: The postprocess method is behaving in a special way because of data corruption. This behaviour will change in the future.\n",
+ " processor = processor.preprocess().process(strategy).postprocess()\n"
+ ]
+ }
+ ],
+ "source": [
+ "path = '../../data/kaggle/feedback-prize-2021'\n",
+ "processor = PersuadeProcessor(path)\n",
+ "for strategy in ['bio', 'io', 'bieo']:\n",
+ " processor = PersuadeProcessor(path)\n",
+ " try:\n",
+ " os.mkdir(f'../../data/{strategy}')\n",
+ " except Exception as e:\n",
+ " print(e)\n",
+ " \n",
+ " processor = processor.preprocess().process(strategy).postprocess()\n",
+ " processor.save_json(f'../../data/{strategy}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7b23fb74",
+ "metadata": {},
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+ }
+ ],
+ "metadata": {
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+ "name": "python",
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+ "version": "3.8.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "C7n9O8Rxn2X2",
+ "outputId": "58589851-2595-4857-8617-aecc0001565c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting colab-dev-tools\n",
+ " Downloading colab_dev_tools-0.0.7-py3-none-any.whl (3.7 kB)\n",
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from colab-dev-tools) (1.21.5)\n",
+ "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from colab-dev-tools) (1.10.0+cu111)\n",
+ "Requirement already satisfied: nvidia-ml-py3 in /usr/local/lib/python3.7/dist-packages (from colab-dev-tools) (7.352.0)\n",
+ "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->colab-dev-tools) (4.1.1)\n",
+ "Installing collected packages: colab-dev-tools\n",
+ "Successfully installed colab-dev-tools-0.0.7\n",
+ "Google Drive import successful.\n",
+ "CUDA device detected. Using GPU...\n",
+ "Mounted at /content/drive\n",
+ "Google Drive mount successful.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "!python3.7 -m pip install colab-dev-tools\n",
+ "from colabtools.utils import get_gpu_utilization, mount_drive, install_private_library\n",
+ "\n",
+ "drive_path = mount_drive()\n",
+ "project_path = os.path.join(drive_path, 'argument-mining')\n",
+ "development_dir = os.path.join(drive_path, 'argument-mining/argminer')\n",
+ "\n",
+ "install_private_library(os.path.join(project_path, 'data/github_config.json'), 'argument-mining')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# -*- coding: utf-8 -*-\n",
+ "\"\"\"This is imported from End_to_end_GPU.ipynb\n",
+ "\n",
+ "Automatically generated by Colaboratory.\n",
+ "\n",
+ "Original file is located at\n",
+ " https://colab.research.google.com/github/namiyousef/argument-mining/blob/develop/experiments/changmao/End-to-end_GPU_v1.ipynb\n",
+ "\n",
+ "# End-to-end\n",
+ "\n",
+ "This notebook should form the core skeleton of the 'run' function\n",
+ "\n",
+ "## Colab Set up\n",
+ "\"\"\"\n",
+ "# -- public imports\n",
+ "\n",
+ "# base\n",
+ "import os\n",
+ "import gc\n",
+ "import json\n",
+ "import time\n",
+ "import warnings\n",
+ "\n",
+ "# pytorch\n",
+ "import torch\n",
+ "from torch.utils.data import DataLoader\n",
+ "\n",
+ "# huggingface\n",
+ "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
+ "\n",
+ "# other libs\n",
+ "import pandas as pd\n",
+ "from pandas.testing import assert_frame_equal\n",
+ "\n",
+ "import plac\n",
+ "\n",
+ "\n",
+ "# -- private imports\n",
+ "\n",
+ "# ml-utils\n",
+ "from mlutils.torchtools.metrics import FScore\n",
+ "\n",
+ "# colab-dev-tools\n",
+ "from colabtools.utils import move_to_device, get_gpu_utilization\n",
+ "from colabtools.config import DEVICE\n",
+ "\n",
+ "# argminer\n",
+ "from argminer.data import ArgumentMiningDataset, TUDarmstadtProcessor, PersuadeProcessor\n",
+ "from argminer.evaluation import inference\n",
+ "from argminer.utils import encode_model_name\n",
+ "from argminer.config import LABELS_MAP_DICT, MAX_NORM\n",
+ "\n",
+ "\n",
+ "# -- globals\n",
+ "TEST_SIZE = 0.3\n",
+ "\n",
+ "# -- define plac inputs\n",
+ "@plac.annotations(\n",
+ " dataset=(\"name of dataset to use\", \"positional\", None, str),\n",
+ " strategy=(\"name of training strategy to use in form {level}_{labellin scheme}\", \"positional\", None, str),\n",
+ " model_name=(\"name of HuggingFace model to use\", \"positional\", None, str),\n",
+ " max_length=(\"max number of tokens\", \"positional\", None, int),\n",
+ " test_size=(\"test size to use (as a percentage of entire dataset)\", \"option\", None, float),\n",
+ " batch_size=(\"Batch size for training\", \"option\", \"b\", int),\n",
+ " epochs=(\"Number of epochs to train for\", \"option\", \"e\", int),\n",
+ " save_freq=(\"How frequently to save model, in epochs\", \"option\", None, int),\n",
+ " verbose=(\"Set model verbosity\", \"option\", None, int),\n",
+ " run_inference=(\"Flag to run inference or not\", \"option\", 'i', bool)\n",
+ ")\n",
+ "def main(dataset, strategy, model_name, max_length, test_size, batch_size, epochs, save_freq, verbose, run_inference):\n",
+ " \"\"\"\n",
+ " Function to run models as a script. This function expects the following directory structure:\n",
+ " - cwd (usually $TMPDIR on cluster)\n",
+ " - df_{dataset}_postprocessed.json\n",
+ " - train-test-split.csv (for TUDarmstadt)\n",
+ " :param dataset:\n",
+ " :param strategy:\n",
+ " :param model_name:\n",
+ " :param max_length:\n",
+ " :param batch_size:\n",
+ " :param epochs:\n",
+ " :param save_freq:\n",
+ " :param verbose:\n",
+ " :return:\n",
+ " \"\"\"\n",
+ " # TODO: imports sklearn to check f1 score\n",
+ " from sklearn.metrics import f1_score\n",
+ " # configs\n",
+ " strat_name, strat_label = strategy.split('_')\n",
+ " df_label_map = LABELS_MAP_DICT[dataset][strat_label]\n",
+ " num_labels = len(set(df_label_map.label))\n",
+ "\n",
+ " # get data processor and train_test_splits\n",
+ " try:\n",
+ " # TODO change made here to fix paths\n",
+ " path = os.path.join(project_path, f'data/{strategy.split(\"_\")[1]}')\n",
+ " processor = TUDarmstadtProcessor(path) if dataset == 'TUDarmstadt' else PersuadeProcessor(path)\n",
+ " processor = processor.from_json()\n",
+ " if dataset == 'TUDarmstadt':\n",
+ " if test_size == 0:\n",
+ " warnings.warn(\n",
+ " f\"Detected test_size={test_size}. \"\n",
+ " f\"No splits being made. \"\n",
+ " f\"Train and test data treated identicallu.\",\n",
+ " UserWarning, stacklevel=2\n",
+ " )\n",
+ " df_total = processor.dataframe\n",
+ " df_dict = {\n",
+ " 'train': df_total,\n",
+ " 'test': df_total\n",
+ " }\n",
+ " else:\n",
+ " df_dict = processor.get_tts(test_size=test_size)\n",
+ " else:\n",
+ " df_dict = processor.get_tts(test_size=test_size)\n",
+ "\n",
+ " df_train = df_dict.get('train')[['text', 'labels']] # TODO standardise this, so that you don't need to apply this..\n",
+ " df_test = df_dict.get('test')[['text', 'labels']]\n",
+ "\n",
+ " except Exception as e: # TODO granularise errors\n",
+ " raise Exception(f'Error occurred during data processing and splitting. Full logs: {e}')\n",
+ "\n",
+ " # get tokenizer, model and optimizer\n",
+ " try:\n",
+ " tokenizer = AutoTokenizer.from_pretrained(model_name, add_prefix_space=True)\n",
+ " model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=num_labels)\n",
+ " # TODO force_download\n",
+ " # TODO add option for optimizer\n",
+ " optimizer = torch.optim.Adam(params=model.parameters())\n",
+ " except Exception as e:\n",
+ " raise Exception(f'Error from tokenizer and model loading. Full logs {e}')\n",
+ "\n",
+ " try:\n",
+ " train_set = ArgumentMiningDataset(df_label_map, df_train, tokenizer, max_length, strategy)\n",
+ " test_set = ArgumentMiningDataset(df_label_map, df_test, tokenizer, max_length, strategy, is_train=False)\n",
+ "\n",
+ " # TODO delete this\n",
+ " all_ids = set()\n",
+ " for label in df_train['labels'].values:\n",
+ " all_ids = all_ids.union(set(label))\n",
+ "\n",
+ " # TODO delete above\n",
+ " train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n",
+ " test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True)\n",
+ " except Exception as e:\n",
+ " raise Exception(f'Error occured during dataset/loader creation. Full logs {e}')\n",
+ "\n",
+ " if not os.path.exists('models'):\n",
+ " os.makedirs('models')\n",
+ " print('models directory created!')\n",
+ "\n",
+ "\n",
+ " metrics = [FScore(average='macro')]\n",
+ " scores = {\n",
+ " 'scores': {\n",
+ " metric.__class__.__name__: [] for metric in metrics\n",
+ " },\n",
+ " 'epoch_scores': {\n",
+ " metric.__class__.__name__: [] for metric in metrics\n",
+ " },\n",
+ " 'epoch_batch_ids': {\n",
+ " metric.__class__.__name__: [] for metric in metrics\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " model.to(DEVICE)\n",
+ " print(f'Model pushed to device: {DEVICE}')\n",
+ " for epoch in range(epochs):\n",
+ " model.train()\n",
+ " start_epoch_message = f'EPOCH {epoch + 1} STARTED'\n",
+ " print(start_epoch_message)\n",
+ " print(f'{\"-\" * len(start_epoch_message)}')\n",
+ " start_epoch = time.time()\n",
+ "\n",
+ " start_load = time.time()\n",
+ " training_loss = 0\n",
+ " for i, (inputs, targets) in enumerate(train_loader):\n",
+ " start_train = time.time()\n",
+ " inputs = move_to_device(inputs, DEVICE)\n",
+ " targets = move_to_device(targets, DEVICE)\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " loss, outputs = model(\n",
+ " labels=targets,\n",
+ " input_ids=inputs['input_ids'],\n",
+ " attention_mask=inputs['attention_mask'],\n",
+ " return_dict=False\n",
+ " )\n",
+ " gpu_util = 'NO GPU' if DEVICE == 'cpu' else get_gpu_utilization()\n",
+ " training_loss += loss.item()\n",
+ "\n",
+ " # backward pass\n",
+ "\n",
+ " torch.nn.utils.clip_grad_norm_(\n",
+ " parameters=model.parameters(), max_norm=MAX_NORM\n",
+ " )\n",
+ "\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " # metrics\n",
+ " for metric in metrics:\n",
+ " score = metric(outputs, targets)\n",
+ " scores['scores'][metric.__class__.__name__].append(score.item())\n",
+ "\n",
+ " # TODO delete\n",
+ " targets = targets.flatten()\n",
+ " ignore_ids = targets == -100\n",
+ " outputs = torch.argmax(outputs, dim=-1).flatten()[~ignore_ids].cpu().tolist()\n",
+ " targets = targets[~ignore_ids].cpu().tolist()\n",
+ " print('OUTPUTS: ', outputs)\n",
+ " print('TARGETS: ', targets)\n",
+ " sklearn_score = f1_score(targets, outputs, average='macro')\n",
+ " del targets, inputs, loss, outputs\n",
+ " gc.collect()\n",
+ " torch.cuda.empty_cache()\n",
+ "\n",
+ " end_train = time.time()\n",
+ "\n",
+ " if verbose > 1:\n",
+ " print(\n",
+ " f'Batch {i + 1} complete. '\n",
+ " f'Time taken: load({start_train - start_load:.3g}),'\n",
+ " f'train({end_train - start_train:.3g}),'\n",
+ " f'total({end_train - start_load:.3g}). '\n",
+ " f'GPU util. after train: {gpu_util}. '\n",
+ " f'Metrics: {\" \".join([f\"{metric_name}({score_list[-1]:.3g})\" for metric_name, score_list in scores[\"scores\"].items()])}. '\n",
+ " f'F1 from sklearn: {sklearn_score}' # TODO delete this\n",
+ " )\n",
+ " start_load = time.time()\n",
+ "\n",
+ " for metric in metrics:\n",
+ " score = scores['scores'][metric.__class__.__name__][:i+1]\n",
+ " avg_score = sum(score)/len(score)\n",
+ " scores['epoch_scores'][metric.__class__.__name__].append(avg_score)\n",
+ " scores['epoch_batch_ids'][metric.__class__.__name__].append(i)\n",
+ "\n",
+ " print_message = f'Epoch {epoch + 1}/{epochs} complete. ' \\\n",
+ " f'Time taken: {start_load - start_epoch:.3g}. ' \\\n",
+ " f'Loss: {training_loss/(i+1): .3g}. ' \\\n",
+ " f'Metrics: {\" \".join([f\"{metric_name}({score_list[-1]:.3g})\" for metric_name, score_list in scores[\"epoch_scores\"].items()])}'\n",
+ "\n",
+ " if verbose:\n",
+ " print(f'{\"-\" * len(print_message)}')\n",
+ " print(print_message)\n",
+ " print(f'{\"-\" * len(print_message)}')\n",
+ "\n",
+ "\n",
+ " if epoch % save_freq == 0:\n",
+ " encoded_model_name = encode_model_name(model_name, epoch+1)\n",
+ " save_path = f'models/{encoded_model_name}'\n",
+ " model.save_pretrained(save_path)\n",
+ " print(f'Model saved at epoch {epoch+1} at: {save_path}')\n",
+ "\n",
+ " encoded_model_name = encode_model_name(model_name, 'final')\n",
+ " save_path = f'models/{encoded_model_name}'\n",
+ " model.save_pretrained(save_path)\n",
+ " print(f'Model saved at epoch {epoch + 1} at: {save_path}')\n",
+ "\n",
+ " with open('training_scores.json', 'w') as f:\n",
+ " json.dump(scores, f)\n",
+ " print('Saved scores.')\n",
+ "\n",
+ " # load trained model\n",
+ " if run_inference:\n",
+ " trained_model = AutoModelForTokenClassification.from_pretrained(save_path)\n",
+ " df_metrics, df_scores = inference(trained_model, test_loader)\n",
+ " df_scores.to_json('scores.json')"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JmKpZhZen7ju",
+ "outputId": "286f2d11-24bc-4ba0-dd23-0d8b3fdda0c3"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Import of ArgMiner successful\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "main(\n",
+ " dataset=\"Persuade\",\n",
+ " strategy='standard_bieo',\n",
+ " model_name=\"google/bigbird-roberta-base\",\n",
+ " max_length=1024,\n",
+ " test_size=0.3,\n",
+ " batch_size=2,\n",
+ " epochs=5,\n",
+ " save_freq=1,\n",
+ " verbose=2,\n",
+ " run_inference=1,\n",
+ ")"
+ ],
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+ "1cec0b04e65743329f7b51fab65194e3",
+ "3dd1679f6d6a45af826c3adaf2d23cfc",
+ "6d90a69e001842b893fa0bf8503c5235",
+ "8582375b313c43c5807988e40a1d7e51",
+ "fb3005cca4694932a88173e0dbb64d41",
+ "0486757ab7574cbb94756333e6e9e2b4",
+ "d5a9154eb6fd42bdaa74bff90640b874",
+ "3df7909ca0e54800894843bd8b9b23ea",
+ "ac3f2f6d910e458e893f66eac20eb86e",
+ "72c2b2f63fbc48ff919d7923aa08b817",
+ "406e97abbff14c94b24b36a1cf798e58",
+ "15cadc98967343dbb524499032a08664",
+ "8eafc6501f2446cf8f64d008751cb234",
+ "313db72f6a8c408693ac929a7481bd1e",
+ "c0dee41c39294e06a96dcfc3eda92084",
+ "79b9c100dd994dbc8916a35faeab11ca",
+ "254388d857e84c53b5c03b16035b113c",
+ "c56cb99e57314de6be3df936047c6256",
+ "9b6a2d07925048c091c335e005177941",
+ "d102f545c14548498742dfdef94dc2ea",
+ "b2c9b97d48f3437eb27a3829a0223dfa"
+ ]
+ },
+ "id": "K9kQZrgSoVPe",
+ "outputId": "85af58ef-ccaf-443c-cc5f-5b135cb4cc6e"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading: 0%| | 0.00/0.99k [00:00\u001b[0;36m\u001b[0m\n\u001b[0;31m from python_files.data import create_labels_doc_level #, KaggleDataset\u001b[0m\n",
+ "\u001b[0;36m File \u001b[0;32m~/Desktop/Main/0.Education/2.UCL/Courses/NLP/argument-mining/experiments/yousef/python_files/data.py:115\u001b[0;36m\u001b[0m\n\u001b[0;31m pass\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- dev imports\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "from python_files.data import create_labels_doc_level #, KaggleDataset\n",
+ "from python_files.run import train_longformer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50a80de8",
+ "metadata": {},
+ "source": [
+ "## Model Configuration"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e459e0d",
+ "metadata": {},
+ "source": [
+ "### Tokenizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "e00fe9f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n",
+ "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = 'google/bigbird-roberta-large'\n",
+ "\n",
+ "tokenizer = BigBirdTokenizer.from_pretrained(model)\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model, add_prefix_space=True)\n",
+ "# def need an encoding function to categoize the inputs or at least to release them, maybe need to store them together tbh?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "f9061003",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[None, 0, 1, None]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenizer('humans humans').word_ids()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1fd05899",
+ "metadata": {},
+ "source": [
+ "### Model and Optmizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "5c5fc460",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of the model checkpoint at google/bigbird-roberta-large were not used when initializing BigBirdForTokenClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight']\n",
+ "- This IS expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of BigBirdForTokenClassification were not initialized from the model checkpoint at google/bigbird-roberta-large and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "config_model = AutoConfig.from_pretrained(model) \n",
+ "config_model.num_labels = 3 # might have to change if we're using the numbered discourse type as well\n",
+ "#config_model.save_pretrained('model')\n",
+ "\n",
+ "model = AutoModelForTokenClassification.from_pretrained(model, config=config_model)\n",
+ "optimizer = torch.optim.Adam(params=model.parameters())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4c4f0c3",
+ "metadata": {},
+ "source": [
+ "## Data Preparation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "id": "990172d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "texts = [\n",
+ " 'Hi my name is Yousef Nami',\n",
+ " 'Hi I am Shirin'\n",
+ " ]\n",
+ "labels = [\n",
+ " ['O', 'O', 'O', 'O', 'B-PERS', 'I-PERS'],\n",
+ " ['O', 'O', 'O', 'B-PERS']\n",
+ "]\n",
+ "\n",
+ "# TODO see if bert can accept text inputs?\n",
+ "labels_numeric = [\n",
+ " [0, 0, 0, 0, 1, 2],\n",
+ " [0, 0, 0, 1]\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "id": "9c3bb28e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame({\n",
+ " 'text': texts,\n",
+ " 'labels': labels_numeric\n",
+ " })"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "id": "ad91646e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset = KaggleDataset(\n",
+ " df,\n",
+ " tokenizer,\n",
+ " max_length=20\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "id": "6db005dd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'input_ids': tensor([ 65, 16003, 717, 1539, 419, 676, 617, 992, 500, 6378,\n",
+ " 66, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'doc_ids': 0, 'word_ids': [0, 1, 2, 3, 4, 4, 4, 5, 5]}\n",
+ "{'input_ids': tensor([ 65, 16003, 415, 817, 1012, 47489, 66, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'doc_ids': 1, 'word_ids': [0, 1, 2, 3, 3]}\n"
+ ]
+ }
+ ],
+ "source": [
+ "for (inputs, targets) in dataset:\n",
+ " print(inputs)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6e583f70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"You sef Na mi\" -> \"BNAME, BNAME, INAME, INAME\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29c86ed8",
+ "metadata": {},
+ "source": [
+ "## Data Training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "6564d266",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'dataset' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_loader \u001b[38;5;241m=\u001b[39m DataLoader(\u001b[43mdataset\u001b[49m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "train_loader = DataLoader(dataset, shuffle=True, batch_size=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "230ea70a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_longformer(model=model, optimizer=optimizer , epochs=1, train_loader=train_loader)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6df7d2e",
+ "metadata": {},
+ "source": [
+ "## Test on Kaggle dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "0655a699",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/utils/data/dataloader.py:521\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 520\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()\n\u001b[0;32m--> 521\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 522\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 524\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 525\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/utils/data/dataloader.py:561\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 560\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 561\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m 562\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m 563\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data)\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py:49\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfetch\u001b[39m(\u001b[38;5;28mself\u001b[39m, possibly_batched_index):\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_collation:\n\u001b[0;32m---> 49\u001b[0m data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 51\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py:49\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfetch\u001b[39m(\u001b[38;5;28mself\u001b[39m, possibly_batched_index):\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_collation:\n\u001b[0;32m---> 49\u001b[0m data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 51\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
+ "File \u001b[0;32m~/Desktop/Main/0.Education/2.UCL/Courses/NLP/argument-mining/experiments/yousef/python_files/data.py:58\u001b[0m, in \u001b[0;36mKaggleDataset.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 56\u001b[0m targets \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mas_tensor(targets, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong)\n\u001b[1;32m 57\u001b[0m expanded_targets \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_length, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong)\n\u001b[0;32m---> 58\u001b[0m expanded_targets[word_id_mask] \u001b[38;5;241m=\u001b[39m \u001b[43mtargets\u001b[49m\u001b[43m[\u001b[49m\u001b[43mword_ids\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (inputs, expanded_targets)\n",
+ "\u001b[0;31mIndexError\u001b[0m: too many indices for tensor of dimension 0"
+ ]
+ }
+ ],
+ "source": [
+ "k_train_loader = DataLoader(kaggle_dataset, batch_size=2)\n",
+ "for (inputs, targets) in k_train_loader:\n",
+ " print(inputs)\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "ccc40760",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Attention type 'block_sparse' is not possible if sequence_length: 20 <= num global tokens: 2 * config.block_size + min. num sliding tokens: 3 * config.block_size + config.num_random_blocks * config.block_size + additional buffer: config.num_random_blocks * config.block_size = 704 with config.block_size = 64, config.num_random_blocks = 3. Changing attention type to 'original_full'...\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "EPOCH 1 STARTED\n",
+ "---------------\n",
+ "tensor(1.0604, grad_fn=)\n",
+ "tensor([[[-0.1543, -0.0606, -0.1969],\n",
+ " [-0.0912, 0.0348, -0.0372],\n",
+ " [ 0.0957, 0.2102, 0.1286],\n",
+ " [ 0.4059, 0.4303, 0.0561],\n",
+ " [ 0.8208, 0.8731, -0.2832],\n",
+ " [ 0.0943, 0.1394, -0.4872],\n",
+ " [ 0.2177, 0.0998, -0.0414],\n",
+ " [ 0.2388, 0.5826, -0.4362],\n",
+ " [ 0.2919, 0.1661, 0.0572],\n",
+ " [-0.2495, 0.1740, -0.3572],\n",
+ " [ 0.3496, 0.3546, 0.2610],\n",
+ " [ 0.2826, 0.4353, -0.3105],\n",
+ " [-0.1331, -0.0448, -0.2625],\n",
+ " [ 0.3561, 0.1659, -0.1571],\n",
+ " [-0.1036, -0.0262, -0.2957],\n",
+ " [ 0.0132, 0.1542, -0.4864],\n",
+ " [ 0.2793, 0.2334, -0.1412],\n",
+ " [-0.1528, -0.0184, -0.2182],\n",
+ " [-0.1823, -0.0512, -0.2283],\n",
+ " [-0.1329, -0.0350, -0.2510]]], grad_fn=) torch.Size([1, 20, 3])\n",
+ "Batch 1 complete. Time taken: load(0.0131), train(18.6), total(18.6). \n"
+ ]
+ },
+ {
+ "ename": "Exception",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [31]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrain_longformer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mk_train_loader\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Desktop/Main/0.Education/2.UCL/Courses/NLP/argument-mining/experiments/yousef/python_files/run.py:59\u001b[0m, in \u001b[0;36mtrain_longformer\u001b[0;34m(model, optimizer, epochs, train_loader, val_loader, verbose)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m 55\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBatch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m complete. Time taken: load(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstart_train \u001b[38;5;241m-\u001b[39m start_load\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3g\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m), \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mend_train \u001b[38;5;241m-\u001b[39m start_train\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3g\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m), total(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mend_train \u001b[38;5;241m-\u001b[39m start_load\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3g\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m). \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 57\u001b[0m )\n\u001b[1;32m 58\u001b[0m start_load \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m---> 59\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m()\n",
+ "\u001b[0;31mException\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "train_longformer(model=model, optimizer=optimizer, epochs=1, train_loader=k_train_loader)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "id": "c3636ba1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def active_logits(raw_logits, word_ids):\n",
+ " word_ids = word_ids.view(-1)\n",
+ " active_mask = word_ids.unsqueeze(1).expand(word_ids.shape[0], 3)\n",
+ " print(active_mask)\n",
+ " active_mask = active_mask != -1\n",
+ " active_logits = raw_logits.view(-1, 3)\n",
+ " print(active_logits)\n",
+ " active_logits = torch.masked_select(active_logits, active_mask) # return 1dTensor\n",
+ " active_logits = active_logits.view(-1, 3) \n",
+ " return active_logits"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "id": "3f3a068f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[-1, -1, -1],\n",
+ " [ 0, 0, 0],\n",
+ " [ 1, 1, 1],\n",
+ " [ 2, 2, 2],\n",
+ " [ 3, 3, 3],\n",
+ " [ 4, 4, 4],\n",
+ " [ 4, 4, 4],\n",
+ " [ 5, 5, 5],\n",
+ " [ 5, 5, 5],\n",
+ " [ 5, 5, 5],\n",
+ " [-1, -1, -1]])\n",
+ "tensor([[0.9573, 0.3607, 0.6314],\n",
+ " [0.4865, 0.2565, 0.6984],\n",
+ " [0.4938, 0.6211, 0.6231],\n",
+ " [0.1811, 0.8638, 0.6316],\n",
+ " [0.6443, 0.3207, 0.4406],\n",
+ " [0.1913, 0.6350, 0.8052],\n",
+ " [0.2440, 0.1049, 0.7696],\n",
+ " [0.5300, 0.6490, 0.8611],\n",
+ " [0.2157, 0.6968, 0.4399],\n",
+ " [0.8934, 0.0529, 0.4582],\n",
+ " [0.0897, 0.0093, 0.6105]])\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[0.4865, 0.2565, 0.6984],\n",
+ " [0.4938, 0.6211, 0.6231],\n",
+ " [0.1811, 0.8638, 0.6316],\n",
+ " [0.6443, 0.3207, 0.4406],\n",
+ " [0.1913, 0.6350, 0.8052],\n",
+ " [0.2440, 0.1049, 0.7696],\n",
+ " [0.5300, 0.6490, 0.8611],\n",
+ " [0.2157, 0.6968, 0.4399],\n",
+ " [0.8934, 0.0529, 0.4582]])"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "word_ids = tokenizer('Hi my name is سیس Yousef').word_ids()\n",
+ "L = len(word_ids)\n",
+ "word_ids = [word_id if word_id is not None else -1 for word_id in word_ids]\n",
+ "word_ids = torch.tensor(word_ids)\n",
+ "\n",
+ "raw_logits = torch.rand((L, 3))\n",
+ "active_logits(raw_logits, word_ids)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "bb497c4b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torch.utils.data import Dataset\n",
+ "# need help with this\n",
+ "IGNORE_INDEX = -100\n",
+ "NON_LABEL = -1\n",
+ "LABELS_TO_IDS = {\n",
+ " 'O': 0,\n",
+ " 'B-PERS': 1,\n",
+ " 'I-PERS': 2\n",
+ "}\n",
+ "\n",
+ "df = pd.DataFrame({\n",
+ " 'text': texts,\n",
+ " 'entities': labels\n",
+ " })\n",
+ "\n",
+ "SUBTOKENS = True\n",
+ "class FeedbackPrizeDataset(Dataset):\n",
+ " def __init__(self, dataframe, tokenizer, max_len, has_labels):\n",
+ " self.len = len(dataframe)\n",
+ " self.data = dataframe\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_len = max_len\n",
+ " self.has_labels = has_labels\n",
+ " \n",
+ " def __getitem__(self, index):\n",
+ " text = self.data.text[index]\n",
+ " encoding = self.tokenizer(\n",
+ " text.split(),\n",
+ " is_split_into_words = True,\n",
+ " padding = 'max_length',\n",
+ " truncation = True,\n",
+ " max_length = self.max_len\n",
+ " )\n",
+ " word_ids = encoding.word_ids()\n",
+ "\n",
+ " # targets\n",
+ " if self.has_labels:\n",
+ " word_labels = self.data.entities[index]\n",
+ " prev_word_idx = None\n",
+ " labels_ids = []\n",
+ " for word_idx in word_ids:\n",
+ " if word_idx is None:\n",
+ " labels_ids.append(IGNORE_INDEX)\n",
+ " elif word_idx != prev_word_idx:\n",
+ " labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\n",
+ " else:\n",
+ " if SUBTOKENS:\n",
+ " labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\n",
+ " else:\n",
+ " labels_ids.append(IGNORE_INDEX)\n",
+ " prev_word_idx = word_idx\n",
+ " encoding['labels'] = labels_ids\n",
+ " # convert to torch.tensor\n",
+ " item = {k: torch.as_tensor(v) for k, v in encoding.items()}\n",
+ " word_ids2 = [w if w is not None else NON_LABEL for w in word_ids]\n",
+ " item['word_ids'] = torch.as_tensor(word_ids2)\n",
+ " return item\n",
+ "\n",
+ " def __len__(self):\n",
+ " return self.len\n",
+ " \n",
+ "dset = FeedbackPrizeDataset(df.rename(columns={'labels':'entities'}), tokenizer, 10,True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "2c719715",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def active_labels(labels):\n",
+ " active_mask = labels.view(-1) != IGNORE_INDEX\n",
+ " active_labels = torch.masked_select(labels.view(-1), active_mask)\n",
+ " return active_labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "be755b8d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([-100, 0, 0, 0, 0, 1, 1, 1, 2, -100])\n",
+ "tensor([0, 0, 0, 0, 1, 1, 1, 2])\n"
+ ]
+ }
+ ],
+ "source": [
+ "for item in dset:\n",
+ " input_ids, attention_mask, labels, word_ids = item.values()\n",
+ " print(labels)\n",
+ " print(active_labels(labels))\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e17e3aaf",
+ "metadata": {},
+ "source": [
+ "# Model evalation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "fb25ed4d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[ True, False, False, True],\n",
+ " [False, False, False, True]])\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "tensor([2, 1])"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from python_files.run import evaluate\n",
+ "\n",
+ "t1 = torch.tensor([[1,2,3,4], [5,6,7,8]])\n",
+ "t2 = torch.tensor([[1,3,2,4], [6, 7, 5, 8]])\n",
+ "\n",
+ "print(t1==t2)\n",
+ "(t1 == t2).sum(dim=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "68afe6fb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[0, 0]])"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(t1 == 1).nonzero()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "75356eeb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test = pd.DataFrame(\n",
+ " {'t1': [set([1,2]), set([4,5])],\n",
+ " 't2':[set([1,1]), set([4,5])]}\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "80c832b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test['gt_and_pred'] = df_test.apply(lambda x: x.t1.intersection(x.t2), axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "7118ad48",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 {1}\n",
+ "1 {4, 5}\n",
+ "Name: gt_and_pred, dtype: object"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_test['gt_and_pred']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "a059c39e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([[2, 4, 6, 8],\n",
+ " [2, 2, 2, 4]])"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.tensor([[1,2,3,4], [1,1,1,2]]).apply_(lambda x: x*2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "2efc6f41",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "torch.LongTensor is not a Module subclass",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [56]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mModuleList\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/container.py:174\u001b[0m, in \u001b[0;36mModuleList.__init__\u001b[0;34m(self, modules)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[38;5;28msuper\u001b[39m(ModuleList, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m modules \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 174\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m modules\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/container.py:215\u001b[0m, in \u001b[0;36mModuleList.__iadd__\u001b[0;34m(self, modules)\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__iadd__\u001b[39m(\u001b[38;5;28mself\u001b[39m, modules: Iterable[Module]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mModuleList\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 215\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodules\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/container.py:254\u001b[0m, in \u001b[0;36mModuleList.extend\u001b[0;34m(self, modules)\u001b[0m\n\u001b[1;32m 252\u001b[0m offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(modules):\n\u001b[0;32m--> 254\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_module\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moffset\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
+ "File \u001b[0;32m~/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/torch/nn/modules/module.py:377\u001b[0m, in \u001b[0;36mModule.add_module\u001b[0;34m(self, name, module)\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Adds a child module to the current module.\u001b[39;00m\n\u001b[1;32m 368\u001b[0m \n\u001b[1;32m 369\u001b[0m \u001b[38;5;124;03mThe module can be accessed as an attribute using the given name.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;124;03m module (Module): child module to be added to the module.\u001b[39;00m\n\u001b[1;32m 375\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 376\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(module, Module) \u001b[38;5;129;01mand\u001b[39;00m module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 377\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m is not a Module subclass\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 378\u001b[0m torch\u001b[38;5;241m.\u001b[39mtypename(module)))\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(name, torch\u001b[38;5;241m.\u001b[39m_six\u001b[38;5;241m.\u001b[39mstring_classes):\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule name should be a string. Got \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 381\u001b[0m torch\u001b[38;5;241m.\u001b[39mtypename(name)))\n",
+ "\u001b[0;31mTypeError\u001b[0m: torch.LongTensor is not a Module subclass"
+ ]
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "48ffc8f1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor([0, 1, 2, 3, 4, 5, 6]),)"
+ ]
+ },
+ "execution_count": 72,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.tensor_split(torch.arange(7), [])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 298,
+ "id": "f6ad18ef",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def _get_zero_start_ids(iterable, split_by=[0]):\n",
+ " \"\"\" TODO run unit tests on this \"\"\"\n",
+ "\n",
+ " split_by = set(split_by)\n",
+ " add_item = float(iterable[0]) in split_by\n",
+ " zero_start_ids = []\n",
+ " for i, item in enumerate(iterable):\n",
+ " if (float(item) in split_by) ^ add_item:\n",
+ " add_item = not add_item\n",
+ " zero_start_ids.append(i)\n",
+ " return zero_start_ids"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 300,
+ "id": "dce14bb0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2, 5, 6, 8, 11]\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[autoreload of python_files.run failed: Traceback (most recent call last):\n",
+ " File \"/Users/yousefnami/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/IPython/extensions/autoreload.py\", line 257, in check\n",
+ " superreload(m, reload, self.old_objects)\n",
+ " File \"/Users/yousefnami/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/IPython/extensions/autoreload.py\", line 455, in superreload\n",
+ " module = reload(module)\n",
+ " File \"/Users/yousefnami/opt/anaconda3/envs/argument-mining/lib/python3.8/importlib/__init__.py\", line 169, in reload\n",
+ " _bootstrap._exec(spec, module)\n",
+ " File \"\", line 604, in _exec\n",
+ " File \"\", line 839, in exec_module\n",
+ " File \"\", line 976, in get_code\n",
+ " File \"\", line 906, in source_to_code\n",
+ " File \"\", line 219, in _call_with_frames_removed\n",
+ " File \"/Users/yousefnami/Desktop/Main/0.Education/2.UCL/Courses/NLP/argument-mining/experiments/yousef/python_files/run.py\", line 99\n",
+ " def get_predicted_label(outputs):\n",
+ " ^\n",
+ "IndentationError: expected an indented block\n",
+ "]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(tensor([0, 0]),\n",
+ " tensor([1, 2, 3]),\n",
+ " tensor([0]),\n",
+ " tensor([3, 5]),\n",
+ " tensor([0, 0, 0]),\n",
+ " tensor([5, 6]))"
+ ]
+ },
+ "execution_count": 300,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tensor = torch.tensor([0, 0, 1,2,3, 0, 3,5,0,0,0,5,6])\n",
+ "split_ids = _get_zero_start_ids(tensor)\n",
+ "print(split_ids)\n",
+ "torch.tensor_split(tensor, split_ids)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 217,
+ "id": "5c239d02",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor([0, 0, 1, 2]), tensor([3, 5, 0]), tensor([0, 0, 5, 6]))"
+ ]
+ },
+ "execution_count": 217,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.tensor_split(torch.tensor([0, 0, \n",
+ "1,2,3,5,0,0,0,5,6]), [4, 7])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "id": "5f0fb449",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CUDA device NOT detected. Using CPU...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'input_ids': [65, 16003, 717, 1539, 321, 100, 372, 676, 617, 992, 66], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
+ ]
+ },
+ "execution_count": 111,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenizer('Hi my name \\nis Yousef')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "id": "9043611c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([2, 4, 3])"
+ ]
+ },
+ "execution_count": 125,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test = (torch.rand((2,4,3)) * 10).int()\n",
+ "test.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 130,
+ "id": "209fb10d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "probas, values = test.max(dim=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "id": "f5ed77c4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CUDA device NOT detected. Using CPU...\n"
+ ]
+ }
+ ],
+ "source": [
+ "df = pd.concat(\n",
+ " [pd.DataFrame(data={'id':i, 'probas':probas, 'values':values}) for i, (probas, values) in enumerate(zip(probas, values))]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "id": "83976796",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ")\n",
+ "tensor([-1, 0, 1, 2, 3, 4, 4, 4, 5, 5, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+ " -1, -1])\n",
+ "tensor([[-0.0166, -0.3431, 0.7926],\n",
+ " [ 0.0151, 0.1921, 0.6123],\n",
+ " [ 0.0664, -0.0830, 0.5713],\n",
+ " [ 0.0521, -0.2243, 0.7010],\n",
+ " [ 0.2475, -0.2039, 0.3427],\n",
+ " [ 0.0468, -0.2063, 0.4431],\n",
+ " [ 0.3500, -0.1089, 0.4890],\n",
+ " [-0.1642, 0.0992, 0.3482],\n",
+ " [ 0.2735, -0.3232, 0.6088]], grad_fn=)\n",
+ "tensor([[-0.0166, -0.3431, 0.7926],\n",
+ " [ 0.0151, 0.1921, 0.6123],\n",
+ " [ 0.0664, -0.0830, 0.5713],\n",
+ " [ 0.0521, -0.2243, 0.7010],\n",
+ " [ 0.6442, -0.5192, 1.2748],\n",
+ " [ 0.1094, -0.2241, 0.9570]], dtype=torch.float64,\n",
+ " grad_fn=)\n",
+ "tensor([-1, 0, 1, 2, 3, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+ " -1, -1])\n",
+ "tensor([[-0.1565, 0.0522, 0.3423],\n",
+ " [-0.1566, 0.0523, 0.3424],\n",
+ " [-0.1557, 0.0502, 0.3406],\n",
+ " [-0.1565, 0.0522, 0.3423],\n",
+ " [-0.1569, 0.0525, 0.3428]], grad_fn=)\n",
+ "tensor([[-0.1565, 0.0522, 0.3423],\n",
+ " [-0.1566, 0.0523, 0.3424],\n",
+ " [-0.1557, 0.0502, 0.3406],\n",
+ " [-0.3134, 0.1048, 0.6851]], dtype=torch.float64,\n",
+ " grad_fn=)\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i, (inputs, targets) in enumerate(loader):\n",
+ " print(inputs)\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " \n",
+ " # for evaluation purposes may need to save the embedding weights??\n",
+ " \n",
+ " word_ids = inputs['word_ids']\n",
+ " word_id_mask = inputs['word_id_mask']\n",
+ " loss, outputs = model(\n",
+ " labels=targets,\n",
+ " input_ids=inputs['input_ids'],\n",
+ " attention_mask=inputs['attention_mask'],\n",
+ " return_dict=False\n",
+ " )\n",
+ " print(outputs)\n",
+ " inference(outputs, word_ids, 'avg')\n",
+ " break\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "3245b15e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def inference(batch, word_id_batch, agg_strategy):\n",
+ " # TODO need to have assertions to prevent certain strategies from happening together!\n",
+ "\n",
+ "\n",
+ " for (predictions, word_ids) in zip(batch, word_id_batch):\n",
+ " # TODO probably no need to store mask?\n",
+ " mask = word_ids != -1\n",
+ " print(word_ids)\n",
+ " word_ids = word_ids[mask]\n",
+ " predictions = predictions[mask]\n",
+ " print(predictions)\n",
+ " unique_word_ids, word_id_counts = torch.unique_consecutive(word_ids, return_counts=True)\n",
+ " agg_predictions = torch.zeros((len(unique_word_ids), predictions.shape[-1]), dtype=float)\n",
+ " start_id = 0\n",
+ " for i, (unique_word_id, word_id_count) in enumerate(zip(unique_word_ids, word_id_counts)):\n",
+ " end_id = start_id + word_id_count\n",
+ " agg_predictions[i] = predictions[start_id: end_id]\n",
+ " start_id = end_id\n",
+ " print(agg_predictions)\n",
+ " return agg_predictions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "803ba66b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Size([2, 2, 3])"
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "word_ids()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a40b749b",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/kaggle_benchmark_1.ipynb b/experiments/yousef/kaggle_benchmark_1.ipynb
new file mode 100644
index 0000000..cdd9ed6
--- /dev/null
+++ b/experiments/yousef/kaggle_benchmark_1.ipynb
@@ -0,0 +1,146847 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "f6342cf1",
+ "metadata": {},
+ "source": [
+ "# Benchmark 1\n",
+ "\n",
+ "## Algorithm\n",
+ "\n",
+ "### Train\n",
+ "- training features are average segment word vectors and location start and end\n",
+ "- train using a decision tree classifier\n",
+ "- prediction outputs are the sentiments\n",
+ "\n",
+ "### Test\n",
+ "- First split by paragraphs using newline\n",
+ "- Then segment\n",
+ "- Using trained model, evaluate\n",
+ "- Concatenate like segments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "5bdbdd6b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import spacy\n",
+ "import os\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from dframcy import DframCy\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "# paths\n",
+ "path_base = '../../data/kaggle/feedback-prize-2021'\n",
+ "dir_train = os.path.join(path_base, 'train')\n",
+ "dir_test = os.path.join(path_base, 'test')\n",
+ "path_train = os.path.join(path_base, 'train.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "bb15827f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0margminer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'argminer'"
+ ]
+ }
+ ],
+ "source": [
+ "import argminer\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "caef4bcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "081b6018",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "278 µs ± 28.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit\n",
+ "length = 10 ** 4\n",
+ "for i in range(length):\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68f429e2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "name": "test.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/tu_darmstadt_dataset.ipynb b/experiments/yousef/tu_darmstadt_dataset.ipynb
new file mode 100644
index 0000000..d0617e9
--- /dev/null
+++ b/experiments/yousef/tu_darmstadt_dataset.ipynb
@@ -0,0 +1,1368 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d9d2c16f",
+ "metadata": {},
+ "source": [
+ "# Development Notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "51853cce",
+ "metadata": {},
+ "source": [
+ "### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "57ddaf5f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# -- public imports\n",
+ "\n",
+ "import os\n",
+ "import pandas as pd\n",
+ "from brat_parser import get_entities_relations_attributes_groups"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "fe460c05",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# -- private import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "20077bcd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- dev imports\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "976709b2",
+ "metadata": {},
+ "source": [
+ "## Importing the dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "d1598cdb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "base_path = '../../data/UCL/dataset2'\n",
+ "path = os.path.join(base_path, 'ArgumentAnnotatedEssays-2.0')\n",
+ "text_file_dir = os.path.join(path, 'brat-project-final')\n",
+ "\n",
+ "texts = []\n",
+ "annotated_texts = []\n",
+ "for file in os.listdir(text_file_dir):\n",
+ " essay_num, file_extension = file.split('.')\n",
+ " if file_extension == 'ann':\n",
+ " with open(os.path.join(text_file_dir, file), 'r') as f:\n",
+ " df_temp = pd.read_csv(f, delimiter='\\t', header=None, names=['label_type', 'label', 'text'])\n",
+ " df_temp[['label', 'label_comp1', 'label_comp2']] = df_temp.label.str.split(expand=True)\n",
+ " df_temp['doc_id'] = essay_num\n",
+ " annotated_texts.append(df_temp)\n",
+ " elif file_extension == 'txt':\n",
+ " with open(os.path.join(text_file_dir, file), 'r') as f:\n",
+ " texts.append((essay_num, f.read()))\n",
+ " else:\n",
+ " continue\n",
+ "\n",
+ "df_texts = pd.DataFrame.from_records(texts, columns=['doc_id', 'text'])\n",
+ "df_annotated = pd.concat(annotated_texts)\n",
+ "\n",
+ "assert sorted(df_annotated.doc_id.unique()) == sorted(df_texts.doc_id)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "76d5bd4f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m test \u001b[38;5;241m=\u001b[39m DataProcessor(\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mtest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpreprocess\u001b[49m()\n\u001b[1;32m 3\u001b[0m test\u001b[38;5;241m.\u001b[39mpreprocess()\n",
+ "File \u001b[0;32m~/Desktop/Main/0.Education/2.UCL/Courses/NLP/argument-mining/argminer/data.py:25\u001b[0m, in \u001b[0;36mDataProcessor.preprocess\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPreprocess method has already been called.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 25\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_preprocess\u001b[49m\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'DataProcessor' object has no attribute '_preprocess'"
+ ]
+ }
+ ],
+ "source": [
+ "test = DataProcessor(1)\n",
+ "test.preprocess()\n",
+ "test.pr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "id": "8c257188",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "murdering criminals is therefore immoral and hard to accept\n",
+ "\n",
+ "\"murdering\" criminals is therefore immoral and hard to accept\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "Click is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\n",
+ "\"Click\" is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "Blood diamond, an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\n",
+ "\"Blood diamond\", an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "rush hours are usually not the direct consequence of drivers\n",
+ "\n",
+ "\"rush hours\" are usually not the direct consequence of drivers\n",
+ "\n",
+ " doc_id text label\n",
+ "0 essay001 Should students be taught to compete or to coo... Other\n",
+ "1 essay001 we should attach more importance to cooperatio... MajorClaim\n",
+ "2 essay001 .\\nFirst of all, Other\n",
+ "3 essay001 through cooperation, children can learn about ... Claim\n",
+ "4 essay001 . Other\n",
+ "... ... ... ...\n",
+ "12575 essay402 . Secondly, Other\n",
+ "12576 essay402 playing sport makes children getting healthy a... Premise\n",
+ "12577 essay402 . \\nIn conclusion, Other\n",
+ "12578 essay402 both studying hard at school and playing sport... MajorClaim\n",
+ "12579 essay402 . Other\n",
+ "\n",
+ "[12580 rows x 3 columns]\n",
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "path = '../../data/UCL/dataset2/ArgumentAnnotatedEssays-2.0/brat-project-final'\n",
+ "test2 = TUDarmstadtProcessor(path)\n",
+ "test2.preprocess()\n",
+ "test2.process('bio')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "id": "d3ea7b54",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " a\n",
+ "0 2\n",
+ "1 4\n",
+ "2 6\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[autoreload of argminer.data failed: Traceback (most recent call last):\n",
+ " File \"/Users/yousefnami/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/IPython/extensions/autoreload.py\", line 257, in check\n",
+ " superreload(m, reload, self.old_objects)\n",
+ " File \"/Users/yousefnami/opt/anaconda3/envs/argument-mining/lib/python3.8/site-packages/IPython/extensions/autoreload.py\", line 455, in superreload\n",
+ " module = reload(module)\n",
+ " File \"/Users/yousefnami/opt/anaconda3/envs/argument-mining/lib/python3.8/importlib/__init__.py\", line 169, in reload\n",
+ " _bootstrap._exec(spec, module)\n",
+ " File \"\", line 604, in _exec\n",
+ " File \"\", line 839, in exec_module\n",
+ " File \"\", line 976, in get_code\n",
+ " File \"\", line 906, in source_to_code\n",
+ " File \"\", line 219, in _call_with_frames_removed\n",
+ " File \"/Users/yousefnami/Desktop/Main/0.Education/2.UCL/Courses/NLP/argument-mining/argminer/data.py\", line 180\n",
+ " df =\n",
+ " ^\n",
+ "SyntaxError: invalid syntax\n",
+ "]\n"
+ ]
+ }
+ ],
+ "source": [
+ "df = pd.DataFrame({'a':[1,2,3]})\n",
+ "\n",
+ "df2 = df\n",
+ "df2.a = df2.a.apply(lambda x: x*2)\n",
+ "print(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "id": "36696d4e",
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "preprocessed\n"
+ ]
+ },
+ {
+ "ename": "Exception",
+ "evalue": "Preprocess method has already been called.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [66]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtest2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpreprocess\u001b[49m()\n",
+ "File \u001b[0;32m~/Desktop/Main/0.Education/2.UCL/Courses/NLP/argument-mining/argminer/data.py:25\u001b[0m, in \u001b[0;36mDataProcessor.preprocess\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatus)\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 25\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPreprocess method has already been called.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preprocess\n",
+ "\u001b[0;31mException\u001b[0m: Preprocess method has already been called."
+ ]
+ }
+ ],
+ "source": [
+ "test2.preprocess()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41ca3c0d",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..fa7ca44
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,48 @@
+from setuptools import setup, find_packages
+import codecs
+import os.path
+
+def read(rel_path):
+ here = os.path.abspath(os.path.dirname(__file__))
+ with codecs.open(os.path.join(here, rel_path), 'r') as fp:
+ return fp.read()
+
+def get_version(rel_path):
+ for line in read(rel_path).splitlines():
+ if line.startswith('__version__'):
+ delim = '"' if '"' in line else "'"
+ return line.split(delim)[1]
+ else:
+ raise RuntimeError("Unable to find version string.")
+
+setup(
+ name='argminer',
+ version=get_version("argminer/__init__.py"), #'0.0.12',
+ description='',
+ author='Yousef Nami',
+ author_email='namiyousef@hotmail.com',
+ url='https://github.com/namiyousef/argument-mining',
+ install_requires=[
+ 'torch',
+ 'transformers',
+ 'sentencepiece',
+ 'pandas',
+ 'plac',
+ 'numpy',
+ 'tqdm',
+ 'sklearn',
+ 'protobuf',
+ 'colab-dev-tools',
+ 'matplotlib',
+ #'colab-dev-tools@git+https://git@github.com/namiyousef/colab-utils.git',
+ 'mlutils@git+https://git@github.com/namiyousef/ml-utils.git@develop',
+ 'connexion[swagger-ui]'
+ #'pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html'
+ ],
+ #package_data={}
+ packages=find_packages(exclude=('tests*', 'experiments*')),
+ license='MIT',
+ entry_points={
+ 'console_scripts': ['argminer-api=argminer.run_api:main'],
+ }
+)
\ No newline at end of file
diff --git a/tests/test_config.py b/tests/test_config.py
new file mode 100644
index 0000000..0511c7d
--- /dev/null
+++ b/tests/test_config.py
@@ -0,0 +1,100 @@
+import unittest
+import pandas as pd
+from pandas.testing import assert_frame_equal
+from itertools import product
+from argminer.config import LABELS_MAP_DICT, PREDICTION_STRING_START_ID
+
+
+class TestConfig(unittest.TestCase):
+
+ def test_label_map_dict(self):
+
+ # -- configuration
+ datasets = ['TUDarmstadt', 'Persuade']
+ labelling_schemes = ['io', 'bio', 'bieo', 'bixo']
+
+ inputs = {
+ f'{dataset}_{labelling_scheme}': (dataset, labelling_scheme) for dataset, labelling_scheme in product(
+ datasets, labelling_schemes
+ )
+ }
+
+ # -- test labelling schemes
+
+ expected_outputs = dict(
+ TUDarmstadt_io=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3],
+ label=['O', 'I-MajorClaim', 'I-Claim', 'I-Premise'],
+
+ )),
+ TUDarmstadt_bio=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6],
+ label=['O', 'B-MajorClaim', 'I-MajorClaim', 'B-Claim', 'I-Claim', 'B-Premise', 'I-Premise'],
+
+ )),
+ TUDarmstadt_bieo=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
+ label=['O', 'B-MajorClaim', 'I-MajorClaim', 'E-MajorClaim',
+ 'B-Claim', 'I-Claim', 'E-Claim', 'B-Premise', 'I-Premise', 'E-Premise'],
+
+ )),
+ TUDarmstadt_bixo=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7],
+ label=['O', 'X', 'B-MajorClaim', 'I-MajorClaim', 'B-Claim', 'I-Claim', 'B-Premise', 'I-Premise'],
+
+ )),
+ Persuade_io=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7],
+ label=[
+ 'O',
+ 'I-Lead', 'I-Position', 'I-Claim', 'I-Counterclaim',
+ 'I-Rebuttal', 'I-Evidence', 'I-Concluding Statement'
+ ],
+
+ )),
+ Persuade_bio=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
+ label=[
+ 'O',
+ 'B-Lead', 'I-Lead', 'B-Position', 'I-Position', 'B-Claim', 'I-Claim',
+ 'B-Counterclaim', 'I-Counterclaim', 'B-Rebuttal', 'I-Rebuttal',
+ 'B-Evidence', 'I-Evidence', 'B-Concluding Statement', 'I-Concluding Statement'
+ ],
+
+ )),
+ Persuade_bieo=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
+ label=[
+ 'O',
+ 'B-Lead', 'I-Lead', 'E-Lead', 'B-Position', 'I-Position', 'E-Position',
+ 'B-Claim', 'I-Claim', 'E-Claim', 'B-Counterclaim', 'I-Counterclaim', 'E-Counterclaim',
+ 'B-Rebuttal', 'I-Rebuttal', 'E-Rebuttal', 'B-Evidence', 'I-Evidence', 'E-Evidence',
+ 'B-Concluding Statement', 'I-Concluding Statement', 'E-Concluding Statement'
+ ],
+ )),
+ Persuade_bixo=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
+ label=[
+ 'O', 'X',
+ 'B-Lead', 'I-Lead', 'B-Position', 'I-Position', 'B-Claim', 'I-Claim',
+ 'B-Counterclaim', 'I-Counterclaim', 'B-Rebuttal', 'I-Rebuttal',
+ 'B-Evidence', 'I-Evidence', 'B-Concluding Statement', 'I-Concluding Statement'
+ ],
+ ))
+ )
+
+ for configuration, (dataset, labelling_scheme) in inputs.items():
+ expected_output = expected_outputs[configuration]
+ output = LABELS_MAP_DICT[dataset][labelling_scheme]
+
+ assert_frame_equal(expected_output, output)
+
+ def test_constants(self):
+
+ assert PREDICTION_STRING_START_ID == 0
+
+
+
+
+if __name__ == '__main__':
+ unittest.main()
\ No newline at end of file
diff --git a/tests/test_data_helpers.py b/tests/test_data_helpers.py
new file mode 100644
index 0000000..89a5548
--- /dev/null
+++ b/tests/test_data_helpers.py
@@ -0,0 +1,69 @@
+import unittest
+from itertools import product
+
+from argminer.data import _generate_entity_labels
+
+
+class TestDataHelpers(unittest.TestCase):
+
+ def test_generate_entity_labels(self):
+ # -- configuration
+ labels_dict = dict(
+ Other='Other',
+ NotOther='NotOther'
+ )
+ add_beg_dict = dict(Beg=True, NotBeg=False)
+ add_end_dict = dict(End=True, NotEnd=False)
+
+ test_params = {}
+ for (label, add_beg_key, add_end_key) in product(labels_dict, add_beg_dict, add_end_dict):
+ identifier = f'{label}{add_beg_key}{add_end_key}'
+ test_params[identifier] = dict(
+ label=labels_dict[label],
+ add_beg=add_beg_dict[add_beg_key],
+ add_end=add_end_dict[add_end_key]
+ )
+
+ # -- test normal case
+ length = 3
+
+ expected_outputs = dict(
+ OtherBegEnd=['O', 'O', 'O'],
+ OtherBegNotEnd=['O', 'O', 'O'],
+ OtherNotBegEnd=['O', 'O', 'O'],
+ OtherNotBegNotEnd=['O', 'O', 'O'],
+ NotOtherBegEnd=['B-NotOther', 'I-NotOther', 'E-NotOther'],
+ NotOtherBegNotEnd=['B-NotOther', 'I-NotOther', 'I-NotOther'],
+ NotOtherNotBegEnd=['I-NotOther', 'I-NotOther', 'E-NotOther'],
+ NotOtherNotBegNotEnd=['I-NotOther', 'I-NotOther', 'I-NotOther'],
+ )
+
+ for configuration, expected_output in expected_outputs.items():
+ params = test_params[configuration]
+ output = _generate_entity_labels(length, **params)
+ assert expected_output == output
+
+ # test edge case (even)
+ length = 2
+
+ expected_outputs = dict(
+ NotOtherBegEnd=['B-NotOther', 'E-NotOther']
+ )
+
+ for configuration, expected_output in expected_outputs.items():
+ params = test_params[configuration]
+ output = _generate_entity_labels(length, **params)
+ assert expected_output == output
+
+ # -- test edge case (only 1)
+ length = 1
+
+ expected_outputs = dict(
+ NotOtherBegEnd=['B-NotOther']
+ )
+
+ for configuration, expected_output in expected_outputs.items():
+ params = test_params[configuration]
+ output = _generate_entity_labels(length, **params)
+ assert expected_output == output
+
diff --git a/tests/test_data_processor.py b/tests/test_data_processor.py
new file mode 100644
index 0000000..e8eea6d
--- /dev/null
+++ b/tests/test_data_processor.py
@@ -0,0 +1,230 @@
+# -- public imports
+import io
+import json
+from pandas.testing import assert_frame_equal
+import unittest
+import sys
+import pandas as pd
+import numpy as np
+from itertools import permutations
+from unittest.mock import Mock, patch
+
+# -- private imports
+from argminer.data import DataProcessor, TUDarmstadtProcessor, PersuadeProcessor
+
+# -- functions
+class Logger:
+ def __init__(self):
+ print(f'Running test "{sys._getframe(1).f_code.co_name}"')
+ print("=" * 50)
+ self.counter = 1
+
+ def log(self, description):
+ print(f'{self.counter}: {description}')
+ self.counter += 1
+
+class TestDataProcessor(unittest.TestCase):
+
+ def test_base_processor(self):
+ logger = Logger()
+
+ logger.log('Test the preproc, proc, postproc cannot be called in incorrect order')
+
+ # define dummy subclass to test superclass features
+ class DummyProcessor(DataProcessor):
+ def __init__(self, path=''):
+ super().__init__(path)
+ def _preprocess(self):
+
+ self.dataframe = pd.DataFrame().from_records([
+ (1, '1', '1'),
+ (1, '2', '2'),
+ (1, '3', '3'),
+ (1, '4', '4'),
+ ])
+ self.status = 'preprocessed'
+
+ def _process(self):
+ pass
+
+ def _postprocess(self):
+ pass
+
+ processor = DummyProcessor()
+
+ status = ['preprocess', 'process', 'postprocess']
+ status_perm = list(permutations(status, r=2))
+
+ for s1, s2 in status_perm:
+ processor.status = s1
+ try:
+ getattr(processor, s2)
+ raise Exception(f'Failure: Successfully ran {s2} after {s1}')
+ except:
+ pass
+ processor.status = None
+
+ logger.log('Test saving')
+
+ logger.log('Test loading')
+
+
+ logger.log('Test default train test split')
+
+
+
+
+ logger.log('Test custom train test split')
+
+
+ def test_tu_darmstadt_processor(self):
+
+ df_tu_darmstadt_raw = pd.DataFrame.from_records([
+ ('1', 'Sentence 1', '1'),
+ ('1', 'Sentence 2', '3'),
+ ('1', 'Sentence 3', '2'),
+ ('2', 'Sentence 1', '1'),
+ ('3', 'Sentence 1', '0'),
+ ('3', 'Sentence 2', '1'),
+ ], columns=['doc_id', 'text', 'label'])
+
+ def _preprocess_mock(self):
+ self.dataframe = df_tu_darmstadt_raw
+ self.status = 'preprocessed'
+ return self
+
+ def _save_json_mock(self):
+ return self.dataframe
+
+ df_expected_outputs = dict(
+ test=pd.DataFrame.from_records([
+ ('2', 'Sentence 1', ['B-1', 'I-1'], [0, 1]),
+ ('3', 'Sentence 1', ['B-0', 'I-0'], [0, 1]),
+ ('3', 'Sentence 2', ['B-1', 'I-1'], [2, 3]),
+ ], columns=['doc_id', 'text', 'label', 'predictionString']),
+ train=pd.DataFrame.from_records([
+ ('1', 'Sentence 1', ['B-1', 'I-1'], [0, 1]),
+ ('1', 'Sentence 2', ['B-3', 'I-3'], [2, 3]),
+ ('1', 'Sentence 3', ['B-2', 'I-2'], [4, 5]),
+ ], columns=['doc_id', 'text', 'label', 'predictionString']),
+ )
+
+ df_expected_outputs_post = dict(
+ test=pd.DataFrame.from_records([
+ ('2', 'Sentence 1', ['B-1', 'I-1'], [0, 1]),
+ ('3', 'Sentence 1 Sentence 2', ['B-0', 'I-0', 'B-1', 'I-1'], [0, 1, 2, 3]),
+ ], columns=['doc_id', 'text', 'labels', 'predictionString']),
+ train=pd.DataFrame.from_records([
+ ('1', 'Sentence 1 Sentence 2 Sentence 3', ['B-1', 'I-1', 'B-3', 'I-3', 'B-2', 'I-2'], [0, 1, 2, 3, 4, 5]),
+ ], columns=['doc_id', 'text', 'labels', 'predictionString']),
+ )
+
+ logger = Logger()
+
+ logger.log('Test train test split - during process method')
+
+ with patch.object(
+ TUDarmstadtProcessor, '_preprocess', _preprocess_mock
+ ) as _preprocess_mock, \
+ patch.object(pd, 'read_csv') as read_csv_mock,\
+ patch.object(DataProcessor, 'save_json', _save_json_mock) as _save_json_mock:
+ read_csv_mock.return_value = pd.DataFrame().from_records([
+ ('1', 'TRAIN'),
+ ('2', 'TEST'),
+ ('3', 'TEST')
+ ], columns=['ID', 'SET'])
+ # TODO needs to add a test for the validation....
+ for split in ['test', 'train']:
+ processor = TUDarmstadtProcessor().preprocess().process('bio', split=split)
+ df_output = processor.dataframe.reset_index(drop=True)
+ assert_frame_equal(
+ df_output,
+ df_expected_outputs[split]
+ )
+
+
+ processor = processor.postprocess()
+ df_output = processor.dataframe.reset_index(drop=True)[['doc_id', 'text', 'labels', 'predictionString']]
+ assert_frame_equal(
+ df_output,
+ df_expected_outputs_post[split][['doc_id', 'text', 'labels', 'predictionString']]
+ )
+
+
+ def test_persuade_processor(self):
+
+
+ logger = Logger()
+
+ df_persuade_raw = pd.DataFrame.from_records([
+ ('1', 'Sentence 1', '1'),
+ ('1', 'Sentence 2', '3'),
+ ('1', 'Sentence 3', '2'),
+ ('2', 'Sentence 1', '1'),
+ ('3', 'Sentence 1', '0'),
+ ('3', 'Sentence 2', '1'),
+ ], columns=['doc_id', 'text', 'label'])
+
+ def _preprocess_mock(self):
+ self.dataframe = df_persuade_raw
+ self.status = 'preprocessed'
+ return self
+
+ def _save_json_mock(self):
+ return self.dataframe
+
+ df_expected_outputs = dict(
+ train=pd.DataFrame.from_records([
+ ('2', 'Sentence 1', ['B-1', 'I-1'], [0, 1]),
+ ('3', 'Sentence 1', ['B-0', 'I-0'], [0, 1]),
+ ('3', 'Sentence 2', ['B-1', 'I-1'], [2, 3]),
+ ], columns=['doc_id', 'text', 'label', 'predictionString']),
+ test=pd.DataFrame.from_records([
+ ('1', 'Sentence 1', ['B-1', 'I-1'], [0, 1]),
+ ('1', 'Sentence 2', ['B-3', 'I-3'], [2, 3]),
+ ('1', 'Sentence 3', ['B-2', 'I-2'], [4, 5]),
+ ], columns=['doc_id', 'text', 'label', 'predictionString']),
+ )
+
+ df_expected_outputs_post = dict(
+ train=pd.DataFrame.from_records([
+ ('2', 'Sentence 1', ['B-1', 'I-1'], [0, 1]),
+ ('3', 'Sentence 1 Sentence 2', ['B-0', 'I-0', 'B-1', 'I-1'], [0, 1, 2, 3]),
+ ], columns=['doc_id', 'text', 'labels', 'predictionString']),
+ test=pd.DataFrame.from_records([
+ ('1', 'Sentence 1 Sentence 2 Sentence 3', ['B-1', 'I-1', 'B-3', 'I-3', 'B-2', 'I-2'],
+ [0, 1, 2, 3, 4, 5]),
+ ], columns=['doc_id', 'text', 'labels', 'predictionString']),
+ )
+
+
+
+ logger.log('Test train test split - during process method')
+
+ with patch.object(
+ PersuadeProcessor, '_preprocess', _preprocess_mock
+ ) as _preprocess_mock, \
+ patch.object(np.random, 'shuffle') as shuffle_mock, \
+ patch.object(DataProcessor, 'save_json', _save_json_mock) as _save_json_mock:
+ shuffle_mock.return_value = None
+ # TODO needs to add a test for the validation....
+ for split in ['test', 'train']:
+ processor = PersuadeProcessor().preprocess().process('bio', split=split, test_size=0.5)
+ df_output = processor.dataframe.reset_index(drop=True)
+ assert_frame_equal(
+ df_output,
+ df_expected_outputs[split]
+ )
+ processor = processor.postprocess()
+ df_output = processor.dataframe.reset_index(drop=True)
+ assert_frame_equal(
+ df_output[['doc_id', 'text', 'labels', 'predictionString']],
+ df_expected_outputs_post[split][['doc_id', 'text', 'labels', 'predictionString']]
+ )
+
+ def test_arg2020_processor(self):
+ pass
+
+
+if __name__ == '__main__':
+ unittest.main()
\ No newline at end of file
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
new file mode 100644
index 0000000..0b9c6d1
--- /dev/null
+++ b/tests/test_dataset.py
@@ -0,0 +1,542 @@
+import unittest
+from argminer.data import ArgumentMiningDataset
+from argminer.utils import _get_label_maps
+from transformers import AutoTokenizer
+import pandas as pd
+import torch
+
+from itertools import product
+
+
+test_string = 'I am Yousef Nami. NLP is the fastest growing Machine Learning field. NLP to science direct is it the most fire field in Machine NLP. Some other sentences. NLP, I conclude that NLP is NLP'
+labels = ['Other', 'Claim', 'Evidence', 'Other', 'Conclusion']
+def create_labelled_stentences(string, labels, strategy):
+ sentences = string.split('.')
+ output_labels = []
+ for sentence, label in zip(sentences, labels):
+ print(sentence, label)
+ if label == 'Other':
+ sentence_labels = len(sentence.split()) * ['O']
+ else:
+ sentence_labels = len(sentence.split()) * [f'I-{label}']
+ if strategy == 'bieo':
+ sentence_labels[-1] = f'E-{label}'
+ sentence_labels[0] = f'B-{label}'
+ output_labels += sentence_labels
+ return pd.DataFrame({'text':[' '.join(string)], 'labels':[output_labels]})
+
+
+class TestDataset(unittest.TestCase):
+
+ def test_dataset_bigbird_tokenizer(self):
+ model_name = 'google/bigbird-roberta-large'
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+ # tests
+ strategy_names = ['bio', 'bieo', 'bixo']
+ strategy_levels = ['wordLevel', 'standard']
+
+
+ strategies = [
+ f'{strategy_level}_{strategy_name}' for strategy_name in strategy_names for strategy_level in strategy_levels if f'{strategy_level}_{strategy_name}' != 'word_level_bixo'
+ ]
+ label_maps = {
+ strategy: _get_label_maps(labels, strategy.split('_')[-1]) for strategy in strategies
+ }
+
+ test_string = 'I am Yousef Nami ' \
+ 'NLP is the fastest growing Machine Learning field ' \
+ 'NLP to science direct is it the most fire field in Machine NLP ' \
+ 'Some other sentences ' \
+ 'NLP I conclude that NLP is NLP'
+
+ bio_labels = ['O']*4 +['B-Claim']+['I-Claim']*7 + ['B-Evidence']+['I-Evidence']*12 + ['O']*3 + ['B-Conclusion']+['I-Conclusion']*6
+ bieo_labels = ['O']*4 +['B-Claim']+['I-Claim']*6+['E-Claim'] + ['B-Evidence']+['I-Evidence']*11+['E-Evidence'] + ['O']*3 + ['B-Conclusion']+['I-Conclusion']*5+['E-Conclusion']
+
+ text_labels = dict(
+ wordLevel_bio=bio_labels,
+ standard_bio=bio_labels,
+ wordLevel_bieo=bieo_labels,
+ standard_bieo=bieo_labels,
+ standard_bixo=bio_labels
+ )
+
+ text_dfs = {
+ key: pd.DataFrame({
+ "labels": [text_labels[key]],
+ "text":test_string}) for key in text_labels
+ }
+
+ max_length = len(tokenizer(test_string).word_ids())
+ print(tokenizer.tokenize(test_string))
+ expected_output = dict(
+ wordLevel_bio=[0]*7 + [1]*2 + [2]*7 + [3]*2 + [4]*13 + [0]*3 + [5]*2 + [6]*8,
+ wordLevel_bieo=[0]*7 + [1]*2 + [2]*6 + [3]*1 + [4]*2 + [5]*11 + [6]*2 + [0]*3 + [7]*2 + [8]*6 + [9]*2,
+ standard_bio=[0]*7 + [1] + [2]*8 + [3]*1 + [4]*14 + [0]*3 + [5]*1 + [6]*9,
+ standard_bieo=[0]*7 + [1] + [2]*7 + [3]*1 + [4]*1 + [5]*13 + [6]*1 + [0]*3 + [7]*1 + [8]*8 + [9]*1,
+ standard_bixo=[0]*7 + [2] + [1] + [3]*7 + [4] + [1] + [5]*12 + [1] + [0]*3 + [6] + [1] + [7]*4 + [1] + [7]*2+ [1]
+ )
+ for i, strategy in enumerate(strategies):
+ if strategy in expected_output:
+ dataset = ArgumentMiningDataset(label_maps[strategy], text_dfs[strategy], tokenizer, max_length, strategy)
+ for (inputs, targets) in dataset:
+ targets = targets[targets != -100]
+ expected_targets = torch.as_tensor(expected_output[strategy])
+ assert (targets == expected_targets).all(), f'assertion failed for strategy: {strategy}:\n{targets}\n{expected_targets}'
+
+ def test_argument_mining_dataset(self):
+
+ # -- configuration
+
+ class TokenizerMock:
+ def __init__(self):
+ # set random how to tokenize
+
+ class Inputs:
+ def __init__(self, input_ids, attention_mask, word_id_list):
+ self.data = dict(
+ input_ids=input_ids,
+ attention_mask=attention_mask
+ )
+ self.word_id_list = word_id_list
+
+ def __getitem__(self, key):
+ return self.data[key]
+
+ def __setitem__(self, key, value):
+ self.data[key] = value
+
+ def word_ids(self):
+ return self.word_id_list
+
+ def items(self):
+ return [(key, val) for key, val in self.data.items()]
+
+ self.inputs = Inputs
+ self.vocab = {'PAD': 0, 'CLS': 1, 'ing': 2, 'er': 3, 'es': 4, 'SEP':5, 'UNK': 6}
+
+
+ def __call__(self, word_split, max_length, **kwargs):
+ input_ids = [self.vocab['CLS']]
+ attention_mask = [1]
+ word_id_list = [None]
+ if max_length == 2:
+ return self.inputs(input_ids+[self.vocab['SEP']], attention_mask+[1], word_id_list+[None])
+ else:
+ for i, word in enumerate(word_split):
+ for j, token in enumerate(self.vocab):
+ if token in word:
+ input_ids.append(self.vocab['UNK'])
+ word_id_list.append(i)
+ attention_mask.append(1)
+
+ input_ids.append(self.vocab[token])
+ word_id_list.append(i)
+ attention_mask.append(1)
+ break
+ elif j == len(self.vocab) - 1:
+ if word not in self.vocab:
+ input_ids.append(self.vocab['UNK'])
+ word_id_list.append(i)
+ attention_mask.append(1)
+ if max_length == 1:
+ input_ids.append(self.vocab['CLS'])
+ attention_mask.append(i)
+ word_id_list.append(None)
+ else:
+ if max_length < len(input_ids):
+ # cut
+ input_ids = input_ids[:max_length]
+ attention_mask = attention_mask[:max_length]
+ word_id_list = word_id_list[:max_length]
+ input_ids[-1] = self.vocab['SEP']
+ word_id_list[-1] = None
+ else:
+ if max_length == len(input_ids):
+ input_ids[-1] = self.vocab['SEP']
+ word_id_list[-1] = None
+ else:
+ input_ids.append(self.vocab['SEP'])
+ word_id_list.append(None)
+ attention_mask.append(1)
+ for i in range(max_length - len(input_ids)):
+ input_ids.append(self.vocab['PAD'])
+ attention_mask.append(0)
+ word_id_list.append(None)
+ return self.inputs(input_ids, attention_mask, word_id_list)
+
+ tokenizer = TokenizerMock()
+ print(tokenizer('Rares Mohammad Nami. I bring her with Rares'.split(), 15).items())
+ # todo tokenizer needs testing
+
+ df_text = pd.DataFrame(dict(
+ text=['Rares Mohammad Rares I bring her with Rares']
+ ))
+
+ df_text_dict = dict(
+ io=df_text.assign(
+ labels=[['I-PERS', 'I-PERS', 'I-PERS', 'O', 'O', 'O', 'O', 'I-PERS']]
+ ),
+ bio=df_text.assign(
+ labels=[['B-PERS', 'I-PERS', 'I-PERS', 'O', 'O', 'O', 'O', 'B-PERS']]
+ ),
+ bieo=df_text.assign(
+ labels=[['B-PERS', 'I-PERS', 'E-PERS', 'O', 'O', 'O', 'O', 'B-PERS']]
+ ),
+ bixo=df_text.assign(
+ labels=[['B-PERS', 'I-PERS', 'I-PERS', 'O', 'O', 'O', 'O', 'B-PERS']]
+ )
+ )
+
+ df_label_map_dict = dict(
+ io=pd.DataFrame(dict(
+ label_id=[0, 1],
+ label=['O', 'I-PERS']
+ )),
+ bio=pd.DataFrame(dict(
+ label_id=[0, 1, 2],
+ label=['O', 'B-PERS', 'I-PERS']
+ )),
+ bieo=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3],
+ label=['O','B-PERS', 'I-PERS', 'E-PERS']
+ )),
+ bixo=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3],
+ label=['O','X', 'B-PERS', 'I-PERS']
+ ))
+ )
+
+ max_length_dict=dict(
+ MaxLengthLess=6,
+ MaxLengthEqual=15,
+ MaxLengthMore=17,
+ )
+
+ # Actual: Rares Mohammad Rar es I bring her with Rares'
+ # Tokenised: Ra res Mohammad Rar es I br ing h er with Ra res'
+ # word_ids: 0 0 1 2 2 3 4 4 5 5 6 7 7
+ # input_ids: 6 4 6 6 4 6 6 2 6 3 6 6 4
+ # targets: ? ? ? ? ? 0 0 0 0 0 0 ? ?
+ # length less = 6, equal = 13, more =15
+
+ inputs_dict = dict(
+ MaxLengthLess=dict(
+ input_ids=torch.tensor([
+ 1,
+ 6, 4, 6, 6,
+ 5
+ ]),
+ word_ids=torch.tensor([
+ -100,
+ 0, 0, 1, 2,
+ -100
+ ]),
+ attention_mask=torch.tensor([
+ 1,
+ 1, 1, 1, 1,
+ 1
+ ]),
+ index=torch.tensor([0])
+ ),
+ MaxLengthEqual=dict(
+ input_ids=torch.tensor([
+ 1,
+ 6, 4, 6, 6, 4,
+ 6, 6, 2, 6, 3, 6,
+ 6, 4,
+ 5
+ ]),
+ word_ids=torch.tensor([
+ -100,
+ 0, 0, 1, 2, 2,
+ 3, 4, 4, 5, 5, 6,
+ 7, 7,
+ -100
+ ]),
+ attention_mask=torch.tensor([
+ 1,
+ 1, 1, 1, 1, 1,
+ 1, 1, 1, 1, 1, 1,
+ 1, 1,
+ 1
+ ]),
+ index=torch.tensor([0])
+ ),
+ MaxLengthMore=dict(
+ input_ids=torch.tensor([
+ 1,
+ 6, 4, 6, 6, 4,
+ 6, 6, 2, 6, 3, 6,
+ 6, 4,
+ 5,
+ 0, 0
+ ]),
+ word_ids=torch.tensor([
+ -100,
+ 0, 0, 1, 2, 2,
+ 3, 4, 4, 5, 5, 6,
+ 7, 7,
+ -100,
+ -100, -100
+ ]),
+ attention_mask=torch.tensor([
+ 1,
+ 1, 1, 1, 1, 1,
+ 1, 1, 1, 1, 1, 1,
+ 1, 1,
+ 1,
+ 0, 0
+ ])
+ )
+ )
+ expected_outputs = dict(
+ standard_io_MaxLengthLess=dict(
+ inputs=inputs_dict['MaxLengthLess'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 1, 1,
+ -100
+ ])
+ ),
+ standard_io_MaxLengthEqual=dict(
+ inputs=inputs_dict['MaxLengthEqual'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 1, 1, 1,
+ 0, 0, 0, 0, 0, 0,
+ 1, 1,
+ -100
+ ])
+ ),
+ standard_io_MaxLengthMore=dict(
+ inputs=inputs_dict['MaxLengthMore'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 1, 1, 1,
+ 0, 0, 0, 0, 0, 0,
+ 1, 1,
+ -100,
+ -100, -100
+ ])
+ ),
+ wordLevel_io_MaxLengthLess=dict(
+ inputs=inputs_dict['MaxLengthLess'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 1, 1,
+ -100
+ ])
+ ),
+ wordLevel_io_MaxLengthEqual=dict(
+ inputs=inputs_dict['MaxLengthEqual'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 1, 1, 1,
+ 0, 0, 0, 0, 0, 0,
+ 1, 1,
+ -100
+ ])
+ ),
+ wordLevel_io_MaxLengthMore=dict(
+ inputs=inputs_dict['MaxLengthMore'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 1, 1, 1,
+ 0, 0, 0, 0, 0, 0,
+ 1, 1,
+ -100,
+ -100, -100
+ ])
+ ),
+ standard_bio_MaxLengthLess=dict(
+ inputs=inputs_dict['MaxLengthLess'],
+ targets=torch.tensor([
+ -100,
+ 1, 2, 2, 2,
+ -100
+ ])
+ ),
+ # Actual: Rares Mohammad Nami. I bring her with Rares'
+ # Tokenised: Ra res Mohammad Nami. I br ing h er with Ra res'
+ # word_ids: 0 0 1 1 2 3 3 4 4 5 6 6
+ # input_ids: 6 4 6 6 6 6 2 6 3 6 6 4
+ # targets: ? ? ? ? 0 0 0 0 0 0 ? ?
+ # length less = 6, equal = 13, more =15
+ standard_bio_MaxLengthEqual=dict(
+ inputs=inputs_dict['MaxLengthEqual'],
+ targets=torch.tensor([
+ -100,
+ 1, 2, 2, 2, 2,
+ 0, 0, 0, 0, 0, 0,
+ 1, 2,
+ -100
+ ])
+ ),
+ standard_bio_MaxLengthMore=dict(
+ inputs=inputs_dict['MaxLengthMore'],
+ targets=torch.tensor([
+ -100,
+ 1, 2, 2, 2, 2,
+ 0, 0, 0, 0, 0, 0,
+ 1, 2,
+ -100,
+ -100, -100
+ ])
+ ),
+ wordLevel_bio_MaxLengthLess=dict(
+ inputs=inputs_dict['MaxLengthLess'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 2, 2,
+ -100
+ ])
+ ),
+ wordLevel_bio_MaxLengthEqual=dict(
+ inputs=inputs_dict['MaxLengthEqual'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 2, 2, 2,
+ 0, 0, 0, 0, 0, 0,
+ 1, 1,
+ -100
+ ])
+ ),
+ wordLevel_bio_MaxLengthMore=dict(
+ inputs=inputs_dict['MaxLengthMore'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 2, 2, 2,
+ 0, 0, 0, 0, 0, 0,
+ 1, 1,
+ -100,
+ -100, -100
+ ])
+ ),
+
+ standard_bieo_MaxLengthLess=dict(
+ inputs=inputs_dict['MaxLengthLess'],
+ targets=torch.tensor([
+ -100,
+ 1, 2, 2, 3, # TODO double check if this is the behaviour you want!
+ -100
+ ])
+ ),
+ standard_bieo_MaxLengthEqual=dict(
+ inputs=inputs_dict['MaxLengthEqual'],
+ targets=torch.tensor([
+ -100,
+ 1, 2, 2, 2, 3,
+ 0, 0, 0, 0, 0, 0,
+ 1, 2,
+ -100,
+ ])
+ ),
+ standard_bieo_MaxLengthMore=dict(
+ inputs=inputs_dict['MaxLengthMore'],
+ targets=torch.tensor([
+ -100,
+ 1, 2, 2, 2, 3,
+ 0, 0, 0, 0, 0, 0,
+ 1, 2,
+ -100,
+ -100, -100
+ ])
+ ),
+ wordLevel_bieo_MaxLengthLess=dict(
+ inputs=inputs_dict['MaxLengthLess'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 2, 3,
+ -100
+ ])
+ ),
+ wordLevel_bieo_MaxLengthEqual=dict(
+ inputs=inputs_dict['MaxLengthEqual'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 2, 3, 3,
+ 0, 0, 0, 0, 0, 0,
+ 1, 1,
+ -100,
+ ])
+ ),
+ wordLevel_bieo_MaxLengthMore=dict(
+ inputs=inputs_dict['MaxLengthMore'],
+ targets=torch.tensor([
+ -100,
+ 1, 1, 2, 3, 3,
+ 0, 0, 0, 0, 0, 0,
+ 1, 1,
+ -100,
+ -100, -100
+ ])
+ ),
+
+ standard_bixo_MaxLengthLess=dict(
+ inputs=inputs_dict['MaxLengthLess'],
+ targets=torch.tensor([
+ -100,
+ 2, 1, 3, 3,
+ -100
+ ])
+ ),
+ standard_bixo_MaxLengthEqual=dict(
+ inputs=inputs_dict['MaxLengthEqual'],
+ targets=torch.tensor([
+ -100,
+ 2, 1, 3, 3, 1,
+ 0, 0, 0, 0, 0, 0,
+ 2, 1,
+ -100,
+ ])
+ ),
+ standard_bixo_MaxLengthMore=dict(
+ inputs=inputs_dict['MaxLengthMore'],
+ targets=torch.tensor([
+ -100,
+ 2, 1, 3, 3, 1,
+ 0, 0, 0, 0, 0, 0,
+ 2, 1,
+ -100,
+ -100, -100
+ ])
+ ),
+ )
+
+ strategies = [
+ 'standard_io',
+ 'wordLevel_io',
+ 'standard_bio',
+ 'wordLevel_bio',
+ 'standard_bixo',
+ 'standard_bieo',
+ 'wordLevel_bieo'
+ ]
+
+ test_params = {
+ '_'.join([strategy, max_length_key]): dict(
+ df_label_map=df_label_map_dict[strategy.split('_')[1]],
+ max_length=max_length_dict[max_length_key],
+ strategy=strategy,
+ df_text=df_text_dict[strategy.split('_')[1]],
+ tokenizer=TokenizerMock(),
+ is_train=False
+ ) for (max_length_key, strategy) in product(max_length_dict, strategies)
+ }
+
+ for configuration, expected_output in expected_outputs.items():
+ param = test_params[configuration]
+ dataset = ArgumentMiningDataset(**param)
+ inputs, targets = dataset[0]
+
+ expected_inputs = expected_output['inputs']
+ expected_targets = expected_output['targets']
+ assert (targets == expected_targets).all(), f'{configuration} Targets:\n' \
+ f'Expected: {expected_targets}\n' \
+ f'Predicted: {targets}\n'
+
+ for key in expected_inputs:
+ assert (inputs[key] == expected_inputs[key]).all(), f'{configuration} {key}:\n' \
+ f'Expected: {expected_inputs[key]}\n' \
+ f'Predicted: {inputs[key]}\n'
\ No newline at end of file
diff --git a/tests/test_evaluation.py b/tests/test_evaluation.py
new file mode 100644
index 0000000..e4bd45c
--- /dev/null
+++ b/tests/test_evaluation.py
@@ -0,0 +1,286 @@
+import unittest
+import torch
+import pandas as pd
+from pandas.testing import assert_frame_equal
+from argminer.evaluation import get_word_labels, get_predictionString, evaluate
+
+class TestEvaluation(unittest.TestCase):
+
+ def test_get_word_labels(self):
+ """
+ Function to test the function 'get_word_labels'
+ NOTE: this function is expected to be used in a pipeline. It can be
+ used to concatenate sequences back into original form given IDs, usually used
+ to concatenate subtokens back to text:
+ Example: Yousef "Tsunami" Nami. I like NLP!
+ Tokenized: Y ou sef "Tsu na mi" Na mi. I li ke NLP !
+ IDs: 0 0 0 1 1 1 2 2 3 4 4 5 6
+
+ Typically, this will have extra tokens that are to be ignored at the beginning
+ and end of the sequence, there are indicated by -100.
+ The final IDs is thus:
+ -100, 0, 0, 0, 1, 1, 1, 2, 2, 3, 4, 4, 5, 6
+ """
+
+ # define word ids
+ word_ids = torch.tensor([[
+ -100,
+ 0, 0, 0,
+ 1, 1, 1,
+ 2, 2,
+ 3,
+ 4, 4,
+ 5,
+ 6,
+ -100
+ ]])
+
+ # define probabilities associated with each token for a 2 class problem
+ probas = torch.tensor(
+ [[
+ [0.5, 0.2],
+ [0.5, 0.1], [0.2, 0.7], [0.3, 0.1],
+ [0.1, 0.8], [0.9, 0.7], [0.3, 0.1],
+ [0.9, 0.8], [0.9, 0.2],
+ [0.1, 0.8],
+ [0.4, 0.2], [0.6, 0.7],
+ [0.1, 0.1],
+ [0.1, 0.1],
+ [0.2, 0.8]
+ ]],
+ )
+
+ # define the ground truth target classes
+ classes = torch.tensor([
+ [
+ -100,
+ 1, 2, 3,
+ 1, 1, 1,
+ 1, 2,
+ 1,
+ 1, 2,
+ 1,
+ 1,
+ -100
+ ]
+ ])
+
+
+ # define expected targets with first aggregation strategy
+ targets_first = [
+ torch.tensor([[1, 1, 1, 1, 1, 1, 1]])
+ ]
+
+
+ # define expected outputs with first aggregation strategy
+ outputs_first = [
+ torch.tensor([
+ [0.5, 0.1],
+ [0.1, 0.8],
+ [0.9, 0.8],
+ [0.1, 0.8],
+ [0.4, 0.2],
+ [0.1, 0.1],
+ [0.1, 0.1],
+ ])
+
+ ]
+
+ # define expected outputs with mean aggregation strategy
+ outputs_mean = [
+ torch.tensor([
+ [1 / 3, 0.3],
+ [13 / 30, 8 / 15],
+ [0.9, 0.5],
+ [0.1, 0.8],
+ [0.5, 0.45],
+ [0.1, 0.1],
+ [0.1, 0.1]
+ ])
+
+ ]
+
+ # define expected outputs with max agg strategy
+ outputs_max = [
+ torch.tensor([
+ [0.5, 0.7],
+ [0.9, 0.8],
+ [0.9, 0.8],
+ [0.1, 0.8],
+ [0.6, 0.7],
+ [0.1, 0.1],
+ [0.1, 0.1],
+ ])
+ ]
+
+ expected_outputs = dict(
+ first_HasX=outputs_first,
+ mean_HasX=outputs_first,
+ max_HasX=outputs_first,
+ first_NoX=outputs_first,
+ mean_NoX=outputs_mean,
+ max_NoX=outputs_max,
+ )
+
+ for agg_strategy in ['first', 'mean', 'max']:
+ for identifier, has_x in {'HasX': True, 'NoX': False}.items():
+ configuration = f'{agg_strategy}_{identifier}'
+ outputs = get_word_labels(word_ids, probas, agg_strategy, has_x)
+ targets = get_word_labels(word_ids, classes, 'first', has_x) # *** fixes targets to first aggregation strategy
+
+ expected_output = expected_outputs[configuration]
+ expected_targets = targets_first # see above ***
+ assert torch.tensor([
+ torch.allclose(output, expected_output_) for output, expected_output_ in zip(
+ outputs, expected_output
+ )
+ ]).all(), f'For {configuration} outputs FAILED\n' \
+ f'EXPECTED: {expected_output}\n' \
+ f'PREDICTED: {outputs}\n'
+ assert torch.tensor([
+ (target == expected_target).all() for target, expected_target in zip(
+ targets, expected_targets
+ )
+ ]).all(), f'For {configuration} targets FAILED\n' \
+ f'EXPECTED: {expected_targets}\n' \
+ f'PREDICTED: {targets}'
+ def test_get_predictionString(self):
+
+ labels = [
+ torch.tensor([1, 2, 2, 3, 3, 3, 4]),
+ torch.tensor([1, 2, 3, 4, 5, 6, 7])
+ ]
+ doc_ids = torch.tensor([1, 2])
+
+ df_expected = pd.DataFrame().from_records([
+ (1, 1, {0, 1}),
+ (1, 2, {2, 3, 4}),
+ (1, 3, {5, 6, 7, 8}),
+ (1, 4, {9, 10}),
+ (2, 1, {0, 1}),
+ (2, 2, {2, 3}),
+ (2, 3, {4, 5}),
+ (2, 4, {6, 7}),
+ (2, 5, {8, 9}),
+ (2, 6, {10, 11}),
+ (2, 7, {12, 13}),
+ ], columns=['id', 'class', 'predictionString'])
+ df_output = get_predictionString(labels, doc_ids)
+
+ # ensure equal for testing purposes
+ df_output.predictionString = df_output.predictionString.apply(lambda x: sorted(x))
+ df_expected.predictionString = df_expected.predictionString.apply(lambda x: sorted(x))
+
+ assert_frame_equal(
+ df_output,
+ df_expected
+ )
+
+ def test_evaluate(self):
+ # Gold sentence 1
+ # Hi, this is an argument. -- Other (0)
+ # According to Wikipedia, Wales is the largest country in the world. -- Evidence (1)
+ # This is just filler. -- Other (0)
+ # Montenegro is beautiful. -- Claim (2)
+ # In conclusion, -- Other (0)
+ # Montenegro is the best country in the world. -- Concluding Statement (3)
+
+ # Gold sentence 2
+ # The new Batman film is a 6/10. -- Claim (2)
+ # This is because, -- Other (0)
+ # It is internally inconsistent with regards to the
+ # assumptions it makes about the hero and decisions he takes. -- Evidence (1)
+
+
+ df_targets_in = pd.DataFrame().from_records([
+ (1, 0, {0, 1, 2, 3, 4}),
+ (1, 1, {5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}),
+ (1, 0, {16, 17, 18, 19}),
+ (1, 2, {20, 21, 22}),
+ (1, 0, {23, 24}),
+ (1, 3, {25, 26, 27, 28, 29, 30, 31, 32}),
+
+ (2, 2, {0, 1, 2, 3, 4, 5, 6}),
+ (2, 0, {7, 8, 9}),
+ (2, 1, {10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27})
+ ], columns=['id', 'class', 'predictionString'])
+
+ df_outputs_in_dict = dict(
+ EqualMisses=pd.DataFrame().from_records([
+ (1, 0, {0, 1}), # FP
+ (1, 1, {2, 3, 4, 5, 6, 7, 8, 9}), # FP
+ (1, 0, {10, 11, 12, 13, 14, 15, 16, 17, 18, 19}), # FP
+ (1, 2, {20, 21, 22, 23}), # should be a true positive
+ (1, 0, {24, 25, 26, 27, 28, 29}), # FP
+ (1, 3, {30, 31, 32}), # FP
+ (2, 2, {0, 1, 2}), # FP
+ (2, 0, {7}), # FP
+ (2, 1, {8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27}),
+ ], columns=['id', 'class', 'predictionString']),
+ AllTruePositive=pd.DataFrame().from_records([
+ (1, 0, {0, 1, 2, 3, 4}),
+ (1, 1, {4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}),
+ (1, 0, {15, 16, 17, 18}),
+ (1, 2, {19, 20, 21}),
+ (1, 0, {22, 23, 24}),
+ (1, 3, {25, 26, 27, 28, 29, 30, 31, 32}),
+ (2, 2, {0, 1, 2, 3}),
+ (2, 0, {4, 5, 6, 7, 8, 9}),
+ (2, 1, {10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27})
+ ], columns=['id', 'class', 'predictionString']),
+ ComplexPrediction=pd.DataFrame().from_records([
+ (1, 1, {0, 1, 2, 3, 4}), # prediction misses ground truth completely, FP1
+ (1, 1, {5, 6, 7, 8, 9, 10}), #TP1
+ (1, 0, {11, 12, 13}), #FP0
+ (1, 1, {14, 15}), #FP1
+ (1, 4, {16, 17}), # out of prediction value!, #FP4
+ (1, 0, {18, 19}), #TP0
+ (1, 2, {20, 21}), #TP2
+ (1, 0, {22, 23}), #TP0
+ (1, 3, {25, 26, 27, 28}), #TP3
+ (1, 0, {29, 30}), # FP0
+ (1, 3, {31, 32}), #FP3
+
+ (2, 2, {0, 1, 2, 3}), #TP2
+ (2, 0, {4, 5, 6}), #FP0
+ (2, 1, {7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25}), #TP1
+ (2, 6, {26, 27}) #FP6
+ ], columns=['id', 'class', 'predictionString'])
+ )
+ # TODO df_expected outputs will likely need to change if shape of
+ # output scores changes!
+ df_expected_outputs_dict = dict(
+ AllTruePositive=pd.DataFrame({
+ 'class': [0, 1, 2, 3],
+ 'tp': [4, 2, 2, 1],
+ 'fn': [0, 0, 0, 0],
+ 'fp': [0, 0, 0, 0],
+ 'f1': [1., 1., 1., 1.] # TODO likely will remove
+ }),
+ EqualMisses=pd.DataFrame({
+ 'class': [0, 1, 2, 3],
+ 'tp': [0., 1., 1., 0.],
+ 'fn': [4., 1., 1., 1.],
+ 'fp': [4., 1., 1., 1.],
+ 'f1': [0, 0.5, 0.5, 0]
+ }),
+ ComplexPrediction=pd.DataFrame({
+ 'class': [0, 1, 2, 3, 4, 6],
+ 'tp': [2., 2., 2., 1., 0., 0.],
+ 'fn': [2., 0., 0., 0., 0., 0.],
+ 'fp': [3., 2., 0., 1., 1., 1.],
+ 'f1': [2/4.5, 2/3, 1, 1/1.5, 0, 0]
+ })
+ )
+
+ for configuration, df_expected_output in df_expected_outputs_dict.items():
+ df_output = evaluate(
+ df_outputs_in_dict[configuration],
+ df_targets_in
+ ).sort_values('class').reset_index(drop=True)
+ assert_frame_equal(df_output, df_expected_output, check_dtype=False)
+
+
+
+ def test_inference(self):
+ pass
\ No newline at end of file
diff --git a/tests/test_package_install.py b/tests/test_package_install.py
new file mode 100644
index 0000000..550b3ec
--- /dev/null
+++ b/tests/test_package_install.py
@@ -0,0 +1,6 @@
+import unittest
+
+class TestPackageInstall(unittest.TestCase):
+
+ def test_package_import(self):
+ import argminer
\ No newline at end of file
diff --git a/tests/test_text_processing.py b/tests/test_text_processing.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 0000000..09d98be
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,115 @@
+import unittest
+
+from argminer.utils import get_predStr, _get_label_maps
+import pandas as pd
+from pandas.testing import assert_frame_equal
+from itertools import product
+
+class TestUtils(unittest.TestCase):
+
+ def test_get_prediction_string(self):
+
+ texts = [
+ 'Hi my name is Yousef',
+ 'Hi my\n name is Yousef',
+ 'Hi my name\n is Yousef',
+ ' Hi my name is Joe' # extra space example
+ ]
+ df_input = pd.DataFrame(dict(
+ text=texts,
+ )).assign(doc_id=list(range(len(texts))), label='Other')
+
+ df_expected = df_input.copy().assign(predictionString=[
+ [0, 1, 2, 3, 4],
+ [0, 1, 2, 3, 4],
+ [0, 1, 2, 3, 4],
+ [0, 1, 2, 3, 4]
+ ])
+
+ df_output = get_predStr(df_input)
+
+ assert_frame_equal(df_expected, df_output)
+
+ def test_get_label_maps(self):
+ strategies = ['io', 'bio', 'bieo', 'bixo']
+ unique_labels_dict = dict(
+ TUDarmstadt=['MajorClaim', 'Claim', 'Premise'],
+ Persuade=['Lead', 'Position', 'Claim', 'Counterclaim', 'Rebuttal', 'Evidence', 'Concluding Statement']
+ )
+
+ test_params = {
+ f'{dataset}_{strategy}': dict(
+ unique_labels=unique_labels_dict[dataset], strategy=strategy
+ ) for (dataset, strategy) in product(unique_labels_dict, strategies)
+ }
+
+ # -- test
+
+ expected_outputs = dict(
+ TUDarmstadt_io=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3],
+ label=['O', 'I-MajorClaim', 'I-Claim', 'I-Premise'],
+
+ )),
+ TUDarmstadt_bio=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6],
+ label=['O', 'B-MajorClaim', 'I-MajorClaim', 'B-Claim', 'I-Claim', 'B-Premise', 'I-Premise'],
+
+ )),
+ TUDarmstadt_bieo=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
+ label=['O', 'B-MajorClaim', 'I-MajorClaim', 'E-MajorClaim',
+ 'B-Claim', 'I-Claim', 'E-Claim', 'B-Premise', 'I-Premise', 'E-Premise'],
+
+ )),
+ TUDarmstadt_bixo=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7],
+ label=['O', 'X', 'B-MajorClaim', 'I-MajorClaim', 'B-Claim', 'I-Claim', 'B-Premise', 'I-Premise'],
+
+ )),
+ Persuade_io=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7],
+ label=[
+ 'O',
+ 'I-Lead', 'I-Position', 'I-Claim', 'I-Counterclaim',
+ 'I-Rebuttal', 'I-Evidence', 'I-Concluding Statement'
+ ],
+
+ )),
+ Persuade_bio=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
+ label=[
+ 'O',
+ 'B-Lead', 'I-Lead', 'B-Position', 'I-Position', 'B-Claim', 'I-Claim',
+ 'B-Counterclaim', 'I-Counterclaim', 'B-Rebuttal', 'I-Rebuttal',
+ 'B-Evidence', 'I-Evidence', 'B-Concluding Statement', 'I-Concluding Statement'
+ ],
+
+ )),
+ Persuade_bieo=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
+ label=[
+ 'O',
+ 'B-Lead', 'I-Lead', 'E-Lead', 'B-Position', 'I-Position', 'E-Position',
+ 'B-Claim', 'I-Claim', 'E-Claim', 'B-Counterclaim', 'I-Counterclaim', 'E-Counterclaim',
+ 'B-Rebuttal', 'I-Rebuttal', 'E-Rebuttal', 'B-Evidence', 'I-Evidence', 'E-Evidence',
+ 'B-Concluding Statement', 'I-Concluding Statement', 'E-Concluding Statement'
+ ],
+ )),
+ Persuade_bixo=pd.DataFrame(dict(
+ label_id=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
+ label=[
+ 'O', 'X',
+ 'B-Lead', 'I-Lead', 'B-Position', 'I-Position', 'B-Claim', 'I-Claim',
+ 'B-Counterclaim', 'I-Counterclaim', 'B-Rebuttal', 'I-Rebuttal',
+ 'B-Evidence', 'I-Evidence', 'B-Concluding Statement', 'I-Concluding Statement'
+ ],
+ ))
+ )
+
+ for configuration, expected_output in expected_outputs.items():
+ param = test_params[configuration]
+ output = _get_label_maps(**param)
+ assert_frame_equal(output, expected_output)
+
+
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "perplexity | \n", + "time | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "4.615870 | \n", + "4.536939 | \n", + "0.190789 | \n", + "93.404465 | \n", + "00:07 | \n", + "
| 1 | \n", + "4.516165 | \n", + "4.319245 | \n", + "0.217928 | \n", + "75.131905 | \n", + "00:08 | \n", + "
| 2 | \n", + "4.396584 | \n", + "4.155612 | \n", + "0.232730 | \n", + "63.790993 | \n", + "00:07 | \n", + "
| 3 | \n", + "4.306918 | \n", + "4.078466 | \n", + "0.237664 | \n", + "59.054806 | \n", + "00:07 | \n", + "
| 4 | \n", + "4.228493 | \n", + "4.061732 | \n", + "0.239309 | \n", + "58.074825 | \n", + "00:07 | \n", + "
| epoch | \n", + "train_loss | \n", + "valid_loss | \n", + "accuracy | \n", + "perplexity | \n", + "time | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "3.915982 | \n", + "4.679247 | \n", + "0.179276 | \n", + "107.688942 | \n", + "00:12 | \n", + "
| 1 | \n", + "4.260599 | \n", + "4.045620 | \n", + "0.289474 | \n", + "57.146603 | \n", + "00:11 | \n", + "
| 2 | \n", + "4.116689 | \n", + "3.561882 | \n", + "0.277961 | \n", + "35.229454 | \n", + "00:11 | \n", + "
| 3 | \n", + "3.932480 | \n", + "3.406904 | \n", + "0.325658 | \n", + "30.171701 | \n", + "00:11 | \n", + "
| 4 | \n", + "3.790564 | \n", + "3.372018 | \n", + "0.328947 | \n", + "29.137257 | \n", + "00:11 | \n", + "
| \n", + " | text | \n", + "category | \n", + "
|---|---|---|
| 0 | \n", + "xxbos xxmaj is xxunk more xxunk than the xxunk xxunk ? \\n\\n xxunk a xxunk is xxunk a xxunk words xxunk is a xxunk xxunk that is being used for a very xxunk time . xxmaj this xxunk xxunk the xxunk of xxunk in xxunk xxunk , ideas , or xxunk to people . xxmaj xxunk , the importance of xxunk xxunk can never be xxunk as it is also another way of communication that is xxunk used . xxmaj hence , | \n", + "0 | \n", + "
| 1 | \n", + "xxbos xxmaj xxunk in transportation and communication like the xxunk and the phone \\n\\n xxmaj nowadays , everyone can see the xxunk of technology on their life , especially on the way of xxunk and communications such as the xxunk and the xxunk . xxmaj there are lots of other effects of growing technology on xxunk and communications , which are xxunk as xxunk . \\n xxmaj first and foremost , | \n", + "0 | \n", + "
| 2 | \n", + "xxbos xxmaj technology xxunk on people \\n\\n xxmaj nowadays , the xxmaj xxunk has become like a small xxunk . xxmaj in the course of the last xxunk years , the way majority of people xxunk with each other has been xxunk xxunk because of technology . xxmaj but there are xxunk xxunk of xxunk whether it is positive or negative . xxmaj from my point of view , | \n", + "0 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " doc_id \\\n",
+ "0 essay001 \n",
+ "1 essay002 \n",
+ "2 essay003 \n",
+ "3 essay004 \n",
+ "4 essay005 \n",
+ "\n",
+ " text \\\n",
+ "0 Should students be taught to compete or to cooperate?\\n\\nIt is always said that competition can effectively promote the development of economy. In order to survive in the competition, companies continue to improve their products and service, and as a result, the whole society prospers. However, when we discuss the issue of competition or cooperation, what we are concerned about is not the whole society, but the development of an individual's whole life. From this point of view, I firmly believe that we should attach more importance to cooperation during primary education .\\nFirst of all, ... \n",
+ "1 More people are migrating to other countries than ever before\\n\\nThe last 50 years have seen an increasing number of immigrants to other countries. People moved due to a number of reasons, namely better educations or higher salary jobs. Some people thought that they should follow the local customs in order to integrate into their adopted countries’ cultures. However I strongly believe that they are able to sustain their cultural identities and doing so help they keep their origin values .\\nFirstly, maintaining one’s cultural identity is a key important rule to help individuals emerge in ... \n",
+ "2 International tourism is now more common than ever before\\n\\nThe last decade has seen an increasing number of tourists traveling to visit natural wonder sights, ancient heritages and different cultures around the world. While some people might think that this international tourism has negative effects on the destination countries, I would contend that it has contributed to the economic development as well as preserved the culture and environment of the tourist destinations . \\nFirstly, international tourism promotes many aspects of the destination country’s economy in order to serve vari... \n",
+ "3 International tourism is now more common than ever before\\n\\nThe last 50 years have seen a significant increase in the number of tourist traveling worldwide. While some might think the tourism bring large profit for the destination countries , I would contend that this industry has affected the cultural attributes and damaged the natural environment of the tourist destinations .\\nFirstly, it is an undeniable fact that tourists from different cultures will probably cause changes to the cultural identity of the tourist destinations . Take Thailand for example, in the Vietnam War, many Am... \n",
+ "4 Living and studying overseas\\n\\nIt is every student's desire to study at a good university and experience a new environment. While some students study and live overseas to achieve this, some prefer to study home because of the difficulties of living and studying overseas. In my opinion, one who studies overseas will gain many skills throughout this experience for several reasons.\\nFirst, studying at an overseas university gives individuals the opportunity to improve social skills by interacting and communicating with students from different origins and cultures . Compared to the peers ... \n",
+ "\n",
+ " predictionString \\\n",
+ "0 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...] \n",
+ "1 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...] \n",
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+ ]
+ },
+ "execution_count": 170,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 215,
+ "id": "0c2a5743",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def convert_labelId(str_list):\n",
+ " l = []\n",
+ " for s in str_list:\n",
+ " l.append(labels_to_ids[s])\n",
+ " return l\n",
+ "df['label_idx'] = df['labels'].apply(convert_labelId)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 216,
+ "id": "924c864a",
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+ "0 Should students be taught to compete or to cooperate?\\n\\nIt is always said that competition can effectively promote the development of economy. In order to survive in the competition, companies continue to improve their products and service, and as a result, the whole society prospers. However, when we discuss the issue of competition or cooperation, what we are concerned about is not the whole society, but the development of an individual's whole life. From this point of view, I firmly believe that we should attach more importance to cooperation during primary education .\\nFirst of all, ... \n",
+ "1 More people are migrating to other countries than ever before\\n\\nThe last 50 years have seen an increasing number of immigrants to other countries. People moved due to a number of reasons, namely better educations or higher salary jobs. Some people thought that they should follow the local customs in order to integrate into their adopted countries’ cultures. However I strongly believe that they are able to sustain their cultural identities and doing so help they keep their origin values .\\nFirstly, maintaining one’s cultural identity is a key important rule to help individuals emerge in ... \n",
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+ "3 International tourism is now more common than ever before\\n\\nThe last 50 years have seen a significant increase in the number of tourist traveling worldwide. While some might think the tourism bring large profit for the destination countries , I would contend that this industry has affected the cultural attributes and damaged the natural environment of the tourist destinations .\\nFirstly, it is an undeniable fact that tourists from different cultures will probably cause changes to the cultural identity of the tourist destinations . Take Thailand for example, in the Vietnam War, many Am... \n",
+ "4 Living and studying overseas\\n\\nIt is every student's desire to study at a good university and experience a new environment. While some students study and live overseas to achieve this, some prefer to study home because of the difficulties of living and studying overseas. In my opinion, one who studies overseas will gain many skills throughout this experience for several reasons.\\nFirst, studying at an overseas university gives individuals the opportunity to improve social skills by interacting and communicating with students from different origins and cultures . Compared to the peers ... \n",
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+ "1 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, O, O, B-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premis... \n",
+ "2 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, O, O, B-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise,... \n",
+ "3 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, O, O, O, O, O, B-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, O, O, O, O, O, O, O, O, B-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise,... \n",
+ "4 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, O, O, O, O, B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, O, B-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-P... \n",
+ "\n",
+ " label_idx \\\n",
+ "0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 3, 4, 4, 4, ...] \n",
+ "1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...] \n",
+ "2 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 5, 6, 6, 6, 6, 6, 6, ...] \n",
+ "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...] \n",
+ "4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...] \n",
+ "\n",
+ " word_idx \n",
+ "0 [1, 2, 3, 4, 5, 6, 7, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 5, 24, 25, 18, 26, 27, 28, 5, 29, 30, 31, 32, 33, 32, 34, 35, 36, 18, 37, 38, 39, 40, 41, 42, 43, 18, 44, 20, 14, 7, 45, 46, 42, 47, 48, 49, 10, 50, 18, 37, 51, 52, 18, 19, 20, 53, 54, 37, 55, 56, 57, 58, 20, 59, 60, 61, 62, 13, 42, 63, 64, 65, 66, 5, 67, 68, 69, 70, 71, 72, 20, 73, 74, 45, 75, 15, ...] \n",
+ "1 [191, 119, 47, 192, 5, 109, 193, 194, 195, 196, 140, 197, 198, 199, 200, 201, 53, 202, 203, 20, 204, 5, 109, 205, 206, 207, 208, 5, 35, 203, 20, 209, 210, 211, 212, 7, 213, 214, 215, 216, 119, 217, 13, 218, 63, 219, 18, 220, 221, 25, 23, 5, 222, 223, 30, 224, 225, 226, 227, 60, 228, 62, 13, 218, 47, 229, 5, 230, 30, 231, 232, 32, 233, 234, 115, 218, 235, 30, 236, 237, 71, 238, 239, 240, 231, 241, 10, 35, 242, 243, 244, 5, 115, 245, 246, 25, 18, 247, 248, 249, ...] \n",
+ "2 [340, 341, 10, 342, 65, 343, 194, 195, 196, 140, 197, 344, 345, 201, 53, 202, 203, 20, 346, 347, 5, 348, 349, 350, 351, 352, 353, 32, 354, 271, 355, 18, 356, 357, 358, 119, 359, 105, 13, 57, 360, 341, 345, 361, 362, 117, 18, 321, 363, 60, 176, 364, 13, 131, 345, 365, 5, 18, 366, 19, 34, 118, 34, 367, 18, 339, 32, 368, 20, 18, 369, 370, 71, 238, 360, 341, 371, 338, 372, 20, 18, 321, 373, 374, 25, 23, 5, 375, 376, 377, 20, 346, 71, 145, 378, 122, 251, 35, 379, 203, ...] \n",
+ "3 [340, 341, 10, 342, 65, 343, 194, 195, 196, 140, 197, 198, 199, 200, 201, 35, 80, 442, 25, 18, 203, 20, 369, 347, 443, 357, 358, 359, 105, 18, 341, 444, 379, 422, 122, 18, 321, 193, 445, 60, 176, 364, 13, 57, 446, 345, 447, 18, 231, 448, 32, 449, 18, 349, 368, 20, 18, 369, 370, 71, 238, 131, 10, 53, 450, 451, 13, 346, 86, 354, 271, 120, 452, 453, 454, 5, 18, 231, 241, 20, 18, 369, 370, 71, 145, 455, 122, 251, 25, 18, 456, 457, 338, 458, 459, 460, 5, 455, 122, 35, ...] \n",
+ "4 [537, 32, 538, 539, 9, 10, 540, 541, 542, 5, 543, 544, 35, 545, 546, 32, 547, 35, 247, 548, 357, 358, 2, 543, 32, 549, 539, 5, 91, 550, 358, 551, 5, 543, 307, 552, 20, 18, 553, 20, 397, 32, 538, 554, 22, 555, 556, 557, 163, 558, 539, 120, 128, 338, 78, 559, 57, 547, 122, 560, 561, 562, 538, 544, 53, 539, 546, 563, 245, 18, 564, 5, 29, 294, 78, 418, 565, 32, 566, 94, 2, 86, 354, 567, 32, 271, 71, 568, 5, 18, 569, 538, 25, 18, 307, 570, 131, 120, 3, 65, ...] "
+ ]
+ },
+ "execution_count": 216,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def convert_wId(str_list):\n",
+ " sentence = str_list.split()\n",
+ " wid = []\n",
+ " for word in sentence:\n",
+ " wid.append(word_to_ids[word])\n",
+ " return wid\n",
+ "df['word_idx'] = df.text.apply(convert_wId)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 219,
+ "id": "62d72b1c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sentences = list(df.word_idx)\n",
+ "max_len = 512 #??\n",
+ "X = [[word for word in sentence] for sentence in sentences]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 221,
+ "id": "fc528d9b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "new_X = []\n",
+ "for seq in X:\n",
+ " new_seq = []\n",
+ " for i in range(max_len):\n",
+ " try:\n",
+ " new_seq.append(seq[i])\n",
+ " except:\n",
+ " new_seq.append(0)\n",
+ " new_X.append(new_seq)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 222,
+ "id": "d173c341",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "labels = list(df.label_idx)\n",
+ "y = pad_sequences(maxlen=max_len,sequences=labels,padding='post',value=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 223,
+ "id": "6e1fdf43",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_tr,X_te,y_tr,y_te = train_test_split(new_X,y,test_size=0.1,random_state=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 224,
+ "id": "83660b8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def generate_batch(x,y,batch_size=4):\n",
+ " length = len(x)\n",
+ " words = []\n",
+ " labels= []\n",
+ " \n",
+ " for i in range(length):\n",
+ " words.append(x[i])\n",
+ " labels.append(y[i])\n",
+ " \n",
+ " if len(words)==batch_size:\n",
+ " yield words,labels\n",
+ " words = []\n",
+ " labels = []\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 242,
+ "id": "d5535847",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class LSTM_NER(nn.Module):\n",
+ " def __init__(self,embedding_dim,hidden_dim,vocab_size,target_size):\n",
+ " super(LSTM_NER,self).__init__()\n",
+ " self.hidden_dim = hidden_dim\n",
+ " self.word_embedding = nn.Embedding(vocab_size,embedding_dim)\n",
+ " self.lstm = nn.LSTM(embedding_dim,hidden_dim,batch_first=True)\n",
+ " self.hidden2tag = nn.Linear(hidden_dim,target_size)\n",
+ " def forward(self,sentence):\n",
+ " \n",
+ " embeds = self.word_embedding(sentence) #??\n",
+ " lstm_out,_ = self.lstm(embeds)\n",
+ " score = F.softmax(self.hidden2tag(lstm_out),dim=-1)\n",
+ " return score\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 243,
+ "id": "bedd61ac",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "EMBEDDING_DIM = 300 #??\n",
+ "HIDDEN_DIM = 64\n",
+ "model = LSTM_NER(EMBEDDING_DIM,HIDDEN_DIM,len(word_to_ids.keys()),len(labels_to_ids.keys()))\n",
+ "loss_function = nn.CrossEntropyLoss(ignore_index=-1,reduction='mean')\n",
+ "optimizer = optim.SGD(model.parameters(),lr=0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 278,
+ "id": "4cc24ff7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 0\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "| \n", + " | doc_id | \n", + "text | \n", + "predictionString | \n", + "labels | \n", + "label_idx | \n", + "word_idx | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "essay001 | \n", + "Should students be taught to compete or to cooperate?\\n\\nIt is always said that competition can effectively promote the development of economy. In order to survive in the competition, companies continue to improve their products and service, and as a result, the whole society prospers. However, when we discuss the issue of competition or cooperation, what we are concerned about is not the whole society, but the development of an individual's whole life. From this point of view, I firmly believe that we should attach more importance to cooperation during primary education .\\nFirst of all, ... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...] | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, O, O, O, O, B-Claim, I-Claim, I-Claim, I-Claim, ...] | \n", + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 3, 4, 4, 4, ...] | \n", + "[1, 2, 3, 4, 5, 6, 7, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 5, 24, 25, 18, 26, 27, 28, 5, 29, 30, 31, 32, 33, 32, 34, 35, 36, 18, 37, 38, 39, 40, 41, 42, 43, 18, 44, 20, 14, 7, 45, 46, 42, 47, 48, 49, 10, 50, 18, 37, 51, 52, 18, 19, 20, 53, 54, 37, 55, 56, 57, 58, 20, 59, 60, 61, 62, 13, 42, 63, 64, 65, 66, 5, 67, 68, 69, 70, 71, 72, 20, 73, 74, 45, 75, 15, ...] | \n", + "
| 1 | \n", + "essay002 | \n", + "More people are migrating to other countries than ever before\\n\\nThe last 50 years have seen an increasing number of immigrants to other countries. People moved due to a number of reasons, namely better educations or higher salary jobs. Some people thought that they should follow the local customs in order to integrate into their adopted countries’ cultures. However I strongly believe that they are able to sustain their cultural identities and doing so help they keep their origin values .\\nFirstly, maintaining one’s cultural identity is a key important rule to help individuals emerge in ... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...] | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, O, O, B-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premis... | \n", + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...] | \n", + "[191, 119, 47, 192, 5, 109, 193, 194, 195, 196, 140, 197, 198, 199, 200, 201, 53, 202, 203, 20, 204, 5, 109, 205, 206, 207, 208, 5, 35, 203, 20, 209, 210, 211, 212, 7, 213, 214, 215, 216, 119, 217, 13, 218, 63, 219, 18, 220, 221, 25, 23, 5, 222, 223, 30, 224, 225, 226, 227, 60, 228, 62, 13, 218, 47, 229, 5, 230, 30, 231, 232, 32, 233, 234, 115, 218, 235, 30, 236, 237, 71, 238, 239, 240, 231, 241, 10, 35, 242, 243, 244, 5, 115, 245, 246, 25, 18, 247, 248, 249, ...] | \n", + "
| 2 | \n", + "essay003 | \n", + "International tourism is now more common than ever before\\n\\nThe last decade has seen an increasing number of tourists traveling to visit natural wonder sights, ancient heritages and different cultures around the world. While some people might think that this international tourism has negative effects on the destination countries, I would contend that it has contributed to the economic development as well as preserved the culture and environment of the tourist destinations . \\nFirstly, international tourism promotes many aspects of the destination country’s economy in order to serve vari... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...] | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, O, O, B-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise,... | \n", + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 5, 6, 6, 6, 6, 6, 6, ...] | \n", + "[340, 341, 10, 342, 65, 343, 194, 195, 196, 140, 197, 344, 345, 201, 53, 202, 203, 20, 346, 347, 5, 348, 349, 350, 351, 352, 353, 32, 354, 271, 355, 18, 356, 357, 358, 119, 359, 105, 13, 57, 360, 341, 345, 361, 362, 117, 18, 321, 363, 60, 176, 364, 13, 131, 345, 365, 5, 18, 366, 19, 34, 118, 34, 367, 18, 339, 32, 368, 20, 18, 369, 370, 71, 238, 360, 341, 371, 338, 372, 20, 18, 321, 373, 374, 25, 23, 5, 375, 376, 377, 20, 346, 71, 145, 378, 122, 251, 35, 379, 203, ...] | \n", + "
| 3 | \n", + "essay004 | \n", + "International tourism is now more common than ever before\\n\\nThe last 50 years have seen a significant increase in the number of tourist traveling worldwide. While some might think the tourism bring large profit for the destination countries , I would contend that this industry has affected the cultural attributes and damaged the natural environment of the tourist destinations .\\nFirstly, it is an undeniable fact that tourists from different cultures will probably cause changes to the cultural identity of the tourist destinations . Take Thailand for example, in the Vietnam War, many Am... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...] | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, O, O, O, O, O, B-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, O, O, O, O, O, O, O, O, B-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise,... | \n", + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...] | \n", + "[340, 341, 10, 342, 65, 343, 194, 195, 196, 140, 197, 198, 199, 200, 201, 35, 80, 442, 25, 18, 203, 20, 369, 347, 443, 357, 358, 359, 105, 18, 341, 444, 379, 422, 122, 18, 321, 193, 445, 60, 176, 364, 13, 57, 446, 345, 447, 18, 231, 448, 32, 449, 18, 349, 368, 20, 18, 369, 370, 71, 238, 131, 10, 53, 450, 451, 13, 346, 86, 354, 271, 120, 452, 453, 454, 5, 18, 231, 241, 20, 18, 369, 370, 71, 145, 455, 122, 251, 25, 18, 456, 457, 338, 458, 459, 460, 5, 455, 122, 35, ...] | \n", + "
| 4 | \n", + "essay005 | \n", + "Living and studying overseas\\n\\nIt is every student's desire to study at a good university and experience a new environment. While some students study and live overseas to achieve this, some prefer to study home because of the difficulties of living and studying overseas. In my opinion, one who studies overseas will gain many skills throughout this experience for several reasons.\\nFirst, studying at an overseas university gives individuals the opportunity to improve social skills by interacting and communicating with students from different origins and cultures . Compared to the peers ... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...] | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, I-MajorClaim, O, O, O, O, B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, I-Claim, O, B-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-Premise, I-P... | \n", + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...] | \n", + "[537, 32, 538, 539, 9, 10, 540, 541, 542, 5, 543, 544, 35, 545, 546, 32, 547, 35, 247, 548, 357, 358, 2, 543, 32, 549, 539, 5, 91, 550, 358, 551, 5, 543, 307, 552, 20, 18, 553, 20, 397, 32, 538, 554, 22, 555, 556, 557, 163, 558, 539, 120, 128, 338, 78, 559, 57, 547, 122, 560, 561, 562, 538, 544, 53, 539, 546, 563, 245, 18, 564, 5, 29, 294, 78, 418, 565, 32, 566, 94, 2, 86, 354, 567, 32, 271, 71, 568, 5, 18, 569, 538, 25, 18, 307, 570, 131, 120, 3, 65, ...] | \n", + "
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+ "outputs": [
+ {
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| 3 | \n", + "EB6C2AF20BFE | \n", + "People sometimes have a different opinion than... | \n", + "[B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... | \n", + "
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+ "processor.dataframe"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "036b551b",
+ "metadata": {},
+ "source": [
+ "# Comparison with original dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0d7db963",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from python_files.data import create_labels_doc_level as create_labels_doc_level_old\n",
+ "\n",
+ "kaggle_df = create_labels_doc_level_old(\n",
+ " '../../data/kaggle/feedback-prize-2021/train',\n",
+ " '../../data/kaggle/feedback-prize-2021/train.csv'\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "af8acb4e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "kaggle_df"
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+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kaggle_df.labels.values[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "e88caf2f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import pandas as pd\n",
+ "df_true = pd.read_csv( '../../data/kaggle/feedback-prize-2021/train.csv')\n",
+ "ids = []\n",
+ "texts = []\n",
+ "for row in kaggle_df.itertuples(index=False):\n",
+ " id = row.id\n",
+ " text_true = row.text\n",
+ " text = row.text_split\n",
+ " labels = torch.as_tensor(row.labels)\n",
+ " unique_vals, counts = torch.unique_consecutive(labels, return_counts=True)\n",
+ " start_id = 0\n",
+ " for unique_val, count in zip(unique_vals, counts):\n",
+ " end_id = start_id + count\n",
+ " texts.append(' '.join(text[start_id: end_id]))\n",
+ " start_id = end_id\n",
+ " #print(id, texts)\n",
+ " #print(df_true[df_true.id == id].discourse_text.values)\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "51ba1f8b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Yousef test\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Qingyu's code\n",
+ "\n",
+ "import json\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import os\n",
+ "from collections import defaultdict\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "import pdb\n",
+ "import torch\n",
+ "from torch import cuda\n",
+ "import time\n",
+ "print('Yousef test')\n",
+ "s = time.time()\n",
+ "BASE_PATH = '../../data/kaggle/feedback-prize-2021/'\n",
+ "path_from_base = lambda x: os.path.join(BASE_PATH, x)\n",
+ "train_df = pd.read_csv(path_from_base('train.csv'))\n",
+ "test_names, train_texts = [], []\n",
+ "\n",
+ "for f in list(os.listdir(path_from_base('train'))):\n",
+ " test_names.append(f.replace('.txt', ''))\n",
+ " train_texts.append(open(path_from_base(f'train/{f}'), 'r').read())\n",
+ " \n",
+ "doc_df = pd.DataFrame({'id': test_names, 'text': train_texts})\n",
+ "#Create entities for each document\n",
+ "entities = []\n",
+ "for index,row in doc_df.iterrows():\n",
+ " length_text = row['text'].split().__len__()\n",
+ " ent = [\"O\" for i in range(length_text)]\n",
+ "\n",
+ " for idx,r in train_df[train_df['id'] == row['id']].iterrows():\n",
+ " \n",
+ " pred_idx = r['predictionstring'].split()\n",
+ " ent[int(pred_idx[0])] = f\"B-{r['discourse_type']}\"\n",
+ "\n",
+ " for i in pred_idx[1:]:\n",
+ " ent[int(i)] = f\"I-{r['discourse_type']}\"\n",
+ " \n",
+ "\n",
+ " entities.append(ent)\n",
+ "\n",
+ "doc_df['elements'] = entities\n",
+ "\n",
+ "#Match the labels to entities\n",
+ "output_labels = ['O', 'B-Lead', 'I-Lead', 'B-Position', 'I-Position', 'B-Claim', 'I-Claim', 'B-Counterclaim', 'I-Counterclaim', \n",
+ " 'B-Rebuttal', 'I-Rebuttal', 'B-Evidence', 'I-Evidence', 'B-Concluding Statement', 'I-Concluding Statement']\n",
+ "\n",
+ "labels_to_ids = {v:k for k,v in enumerate(output_labels)}\n",
+ "ids_to_labels = {k:v for k,v in enumerate(output_labels)}\n",
+ "\n",
+ "doc_df['labels'] = doc_df['elements'].apply(lambda x: [labels_to_ids[i] for i in x])\n",
+ "for i, (text, label) in doc_df[['text', 'labels']].iterrows():\n",
+ " assert len(label) == len(text.split())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "68a6d482",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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| 2 | \n", + "2E4AFCD3987F | \n", + "Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... | \n", + "[Dear, State, Senator, ,, In, the, ruels, of, ... | \n", + "[0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 7, 8, ... | \n", + "[4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,... | \n", + "[3, 4, 4, 4, 4, 4, 4, 4, 4, 7, 8, 8, 8, 8, 8, ... | \n", + "
| 3 | \n", + "EB6C2AF20BFE | \n", + "People sometimes have a different opinion than... | \n", + "[People, sometimes, have, a, different, opinio... | \n", + "[1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "
| 4 | \n", + "A91A08E523D5 | \n", + "Dear senator,\\n\\nAs you know the Electoral Col... | \n", + "[Dear, senator,, As, you, know, the, Electoral... | \n", + "[0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1... | \n", + "[1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "
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+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... \n",
+ "\n",
+ " labels \n",
+ "0 [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... \n",
+ "1 [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, ... \n",
+ "2 [0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 6, ... \n",
+ "3 [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... \n",
+ "4 [0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... "
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "doc_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "a956dee2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for row1, row2 in zip(doc_df.itertuples(index=False), kaggle_df.itertuples(index=False)):\n",
+ " try:\n",
+ " assert row1.text == row2.text\n",
+ " except:\n",
+ " try:\n",
+ " assert row1.labels.__len__() == row2.labels.__len__()\n",
+ " except:\n",
+ " pass\n",
+ " \n",
+ " #print(row1.text)\n",
+ " #print()\n",
+ " #print(row1.text)\n",
+ " #print('========================================================')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "id": "d776487e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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| 4 | \n", + "A91A08E523D5 | \n", + "Dear senator,\\n\\nAs you know the Electoral Col... | \n", + "[O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... | \n", + "[0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "
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+ " id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
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+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " elements \\\n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... \n",
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+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... \n",
+ "\n",
+ " labels \n",
+ "0 [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... \n",
+ "1 [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, ... \n",
+ "2 [0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 6, ... \n",
+ "3 [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... \n",
+ "4 [0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... "
+ ]
+ },
+ "execution_count": 73,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "doc_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "id": "8acba867",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import pandas as pd\n",
+ "ids = []\n",
+ "texts = []\n",
+ "for row in doc_df.itertuples(index=False):\n",
+ " id = row.id\n",
+ " text_true = row.text\n",
+ " text = text_true.split()\n",
+ " labels = torch.as_tensor(row.labels)\n",
+ " unique_vals, counts = torch.unique_consecutive(labels, return_counts=True)\n",
+ " start_id = 0\n",
+ " for unique_val, count in zip(unique_vals, counts):\n",
+ " end_id = start_id + count\n",
+ " texts.append(' '.join(text[start_id: end_id]))\n",
+ " start_id = end_id\n",
+ " #print(texts)\n",
+ " #print(df_true[df_true.id == id].discourse_text.values)\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ca951cad",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "708a7948",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "synonyms = {\n",
+ " 'students': 'pupils'\n",
+ "}\n",
+ "\n",
+ "def synonym_processor(text):\n",
+ " text = ' '.join(\n",
+ " [\n",
+ " synonyms[word] if word in synonyms else word for word in text.split()]\n",
+ " )\n",
+ " return text"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "id": "77818e93",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'text pupils'"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "synonym_processor('text students')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "id": "ce039c34",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "murdering criminals is therefore immoral and hard to accept\n",
+ "\n",
+ "\"murdering\" criminals is therefore immoral and hard to accept\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "Click is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\n",
+ "\"Click\" is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "Blood diamond, an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\n",
+ "\"Blood diamond\", an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\n",
+ "Found non-matching segments:--------------------------------------------------\n",
+ "\n",
+ "rush hours are usually not the direct consequence of drivers\n",
+ "\n",
+ "\"rush hours\" are usually not the direct consequence of drivers\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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| 1 | \n", + "DFEAEC512BAB | \n", + "Should students design a summer project for sc... | \n", + "[O, O, O, O, O, O, O, O, B-Position, I-Positio... | \n", + "[0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, ... | \n", + "
| 2 | \n", + "2E4AFCD3987F | \n", + "Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... | \n", + "[O, O, O, O, B-Position, I-Position, I-Positio... | \n", + "[0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 6, ... | \n", + "
| 3 | \n", + "EB6C2AF20BFE | \n", + "People sometimes have a different opinion than... | \n", + "[B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... | \n", + "[1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "
| 4 | \n", + "A91A08E523D5 | \n", + "Dear senator,\\n\\nAs you know the Electoral Col... | \n", + "[O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... | \n", + "[0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ ],
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+ " doc_id text \\\n",
+ "0 essay001 Should pupils be taught to compete or to coope... \n",
+ "1 essay002 More people are migrating to other countries t... \n",
+ "2 essay003 International tourism is now more common than ... \n",
+ "3 essay004 International tourism is now more common than ... \n",
+ "4 essay005 Living and studying overseas It is every stude... \n",
+ ".. ... ... \n",
+ "397 essay398 We can not forcedly put the same numbers of ma... \n",
+ "398 essay399 Drugs, alcohol and messy sex lives Celebrities... \n",
+ "399 essay400 A greater proportion of the budget should be a... \n",
+ "400 essay401 Fatherhood should be as present as motherhood ... \n",
+ "401 essay402 Children should studying hard or playing sport... \n",
+ "\n",
+ " predictionString \\\n",
+ "0 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n",
+ "1 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n",
+ "2 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n",
+ "3 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n",
+ "4 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n",
+ ".. ... \n",
+ "397 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n",
+ "398 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n",
+ "399 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n",
+ "400 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n",
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+ "\n",
+ " labels \n",
+ "0 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
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+ "2 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "3 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "4 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ ".. ... \n",
+ "397 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "398 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "399 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "400 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "401 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "\n",
+ "[402 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 75,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path = '../../data/UCL/dataset2/ArgumentAnnotatedEssays-2.0'\n",
+ "processor = TUDarmstadtProcessor(path).preprocess()\n",
+ "processor.process('bio', processors=[synonym_processor])\n",
+ "processor = processor.postprocess()\n",
+ "processor.dataframe"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/Dataloader.ipynb b/experiments/yousef/Dataloader.ipynb
new file mode 100644
index 0000000..6607339
--- /dev/null
+++ b/experiments/yousef/Dataloader.ipynb
@@ -0,0 +1,713 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "16a27453",
+ "metadata": {},
+ "source": [
+ "# Dataloader"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fb1a001a",
+ "metadata": {},
+ "source": [
+ "### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "302f6244",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# -- public imports\n",
+ "\n",
+ "import os\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "7652c457",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# -- private import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "2f39f2f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- dev imports\n",
+ "\n",
+ "from python_files.data import ArgumentMiningDataset\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "41778bc3",
+ "metadata": {},
+ "source": [
+ "### Load the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "1cd366a7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | doc_id | \n", + "text | \n", + "predictionString | \n", + "labels | \n", + "
|---|---|---|---|---|
| 0 | \n", + "essay001 | \n", + "Should pupils be taught to compete or to coope... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 1 | \n", + "essay002 | \n", + "More people are migrating to other countries t... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 2 | \n", + "essay003 | \n", + "International tourism is now more common than ... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 3 | \n", + "essay004 | \n", + "International tourism is now more common than ... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 4 | \n", + "essay005 | \n", + "Living and studying overseas It is every stude... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 397 | \n", + "essay398 | \n", + "We can not forcedly put the same numbers of ma... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 398 | \n", + "essay399 | \n", + "Drugs, alcohol and messy sex lives Celebrities... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 399 | \n", + "essay400 | \n", + "A greater proportion of the budget should be a... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 400 | \n", + "essay401 | \n", + "Fatherhood should be as present as motherhood ... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 401 | \n", + "essay402 | \n", + "Children should studying hard or playing sport... | \n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
402 rows × 4 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
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+ ],
+ "text/plain": [
+ " label_type label text \\\n",
+ "0 O Other Should students be taught to compete or to coo... \n",
+ "1 T1 MajorClaim we should attach more importance to cooperatio... \n",
+ "2 O Other .\\nFirst of all, \n",
+ "3 T3 Claim through cooperation, children can learn about ... \n",
+ "4 O Other . \n",
+ "\n",
+ " span_start span_end doc_id \\\n",
+ "0 0 503 essay001 \n",
+ "1 503 575 essay001 \n",
+ "2 575 591 essay001 \n",
+ "3 591 714 essay001 \n",
+ "4 714 716 essay001 \n",
+ "\n",
+ " predictionString \\\n",
+ "0 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14... \n",
+ "1 [83, 84, 85, 86, 87, 88, 89, 90, 91, 92] \n",
+ "2 [93, 94, 95, 96] \n",
+ "3 [97, 98, 99, 100, 101, 102, 103, 104, 105, 106... \n",
+ "4 [115] \n",
+ "\n",
+ " label_bio \\\n",
+ "0 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "1 [B-MajorClaim, I-MajorClaim, I-MajorClaim, I-M... \n",
+ "2 [O, O, O, O] \n",
+ "3 [B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, ... \n",
+ "4 [O] \n",
+ "\n",
+ " label_bieo \n",
+ "0 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "1 [B-MajorClaim, I-MajorClaim, I-MajorClaim, I-M... \n",
+ "2 [O, O, O, O] \n",
+ "3 [B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, ... \n",
+ "4 [O] "
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "base_path = '../../data/UCL/dataset2'\n",
+ "dataset_path = os.path.join(base_path, 'df_labels_general.json')\n",
+ "\n",
+ "label_map_path = os.path.join(base_path, 'df_label_map_bio.json')\n",
+ "df_label_map_bio = pd.read_json(label_map_path)\n",
+ "\n",
+ "label_map_path = os.path.join(base_path, 'df_label_map_bieo.json')\n",
+ "df_label_map_bieo = pd.read_json(label_map_path)\n",
+ "\n",
+ "label_map_path = os.path.join(base_path, 'df_label_map_bixo.json')\n",
+ "df_label_map_bixo = pd.read_json(label_map_path)\n",
+ "\n",
+ "df = pd.read_json(dataset_path)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1a57c3e7",
+ "metadata": {},
+ "source": [
+ "### Develop Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "79d65ddd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | label_type | \n", + "label | \n", + "text | \n", + "span_start | \n", + "span_end | \n", + "doc_id | \n", + "predictionString | \n", + "label_bio | \n", + "label_bieo | \n", + "
|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "O | \n", + "Other | \n", + "Should students be taught to compete or to coo... | \n", + "0 | \n", + "503 | \n", + "essay001 | \n", + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 1 | \n", + "T1 | \n", + "MajorClaim | \n", + "we should attach more importance to cooperatio... | \n", + "503 | \n", + "575 | \n", + "essay001 | \n", + "[83, 84, 85, 86, 87, 88, 89, 90, 91, 92] | \n", + "[B-MajorClaim, I-MajorClaim, I-MajorClaim, I-M... | \n", + "[B-MajorClaim, I-MajorClaim, I-MajorClaim, I-M... | \n", + "
| 2 | \n", + "O | \n", + "Other | \n", + ".\\nFirst of all, | \n", + "575 | \n", + "591 | \n", + "essay001 | \n", + "[93, 94, 95, 96] | \n", + "[O, O, O, O] | \n", + "[O, O, O, O] | \n", + "
| 3 | \n", + "T3 | \n", + "Claim | \n", + "through cooperation, children can learn about ... | \n", + "591 | \n", + "714 | \n", + "essay001 | \n", + "[97, 98, 99, 100, 101, 102, 103, 104, 105, 106... | \n", + "[B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, ... | \n", + "[B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, ... | \n", + "
| 4 | \n", + "O | \n", + "Other | \n", + ". | \n", + "714 | \n", + "716 | \n", + "essay001 | \n", + "[115] | \n", + "[O] | \n", + "[O] | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " text \\\n",
+ "0 Should students be taught to compete or to coo... \n",
+ "1 we should attach more importance to cooperatio... \n",
+ "2 .\\nFirst of all, \n",
+ "3 through cooperation, children can learn about ... \n",
+ "4 . \n",
+ "... ... \n",
+ "12575 . Secondly, \n",
+ "12576 playing sport makes children getting healthy a... \n",
+ "12577 . \\nIn conclusion, \n",
+ "12578 both studying hard at school and playing sport... \n",
+ "12579 . \n",
+ "\n",
+ " labels \n",
+ "0 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "1 [B-MajorClaim, I-MajorClaim, I-MajorClaim, I-M... \n",
+ "2 [O, O, O, O] \n",
+ "3 [B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, ... \n",
+ "4 [O] \n",
+ "... ... \n",
+ "12575 [O, O] \n",
+ "12576 [B-Premise, I-Premise, I-Premise, I-Premise, I... \n",
+ "12577 [O, O, O] \n",
+ "12578 [B-MajorClaim, I-MajorClaim, I-MajorClaim, I-M... \n",
+ "12579 [O] \n",
+ "\n",
+ "[12580 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_text = df[['text', 'label_bio']].rename(columns={'label_bio':'labels'})\n",
+ "df_text"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "2a27c39f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | text | \n", + "labels | \n", + "
|---|---|---|
| 0 | \n", + "Should students be taught to compete or to coo... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 1 | \n", + "we should attach more importance to cooperatio... | \n", + "[B-MajorClaim, I-MajorClaim, I-MajorClaim, I-M... | \n", + "
| 2 | \n", + ".\\nFirst of all, | \n", + "[O, O, O, O] | \n", + "
| 3 | \n", + "through cooperation, children can learn about ... | \n", + "[B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, ... | \n", + "
| 4 | \n", + ". | \n", + "[O] | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "
| 12575 | \n", + ". Secondly, | \n", + "[O, O] | \n", + "
| 12576 | \n", + "playing sport makes children getting healthy a... | \n", + "[B-Premise, I-Premise, I-Premise, I-Premise, I... | \n", + "
| 12577 | \n", + ". \\nIn conclusion, | \n", + "[O, O, O] | \n", + "
| 12578 | \n", + "both studying hard at school and playing sport... | \n", + "[B-MajorClaim, I-MajorClaim, I-MajorClaim, I-M... | \n", + "
| 12579 | \n", + ". | \n", + "[O] | \n", + "
12580 rows × 2 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " label_id label\n",
+ "0 0 O\n",
+ "1 1 B-MajorClaim\n",
+ "2 2 I-MajorClaim\n",
+ "3 3 B-Claim\n",
+ "4 4 I-Claim\n",
+ "5 5 B-Premise\n",
+ "6 6 I-Premise"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_label_map_bio"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "4f757015",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[list([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
+ " list([1, 2, 2, 2, 2, 2, 2, 2, 2, 2]) list([0, 0, 0, 0]) ...\n",
+ " list([0, 0, 0]) list([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) list([0])]\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset = ArgumentMiningDataset(df_label_map_bio, df_text, strategy='bio', tokenizer=0, max_length=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "05c979e7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 O\n",
+ "1 B-MajorClaim\n",
+ "2 I-MajorClaim\n",
+ "3 B-Claim\n",
+ "4 I-Claim\n",
+ "5 B-Premise\n",
+ "6 I-Premise\n",
+ "Name: label, dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df_label_map_bio.label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "id": "6fd34c1b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "a 1\n",
+ "Name: 0, dtype: int64"
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.DataFrame(data={'a':[1,2,3]}).loc[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "id": "703c025a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([ 0, 4, 5, 9, 10])"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "x = torch.tensor([0,0,0,0,1,2,2,2,2,3,4,4,4,])\n",
+ "unique, inverse = torch.unique(x, sorted=True, return_inverse=True)\n",
+ "perm = torch.arange(inverse.size(0), dtype=inverse.dtype, device=inverse.device)\n",
+ "inverse, perm = inverse.flip([0]), perm.flip([0])\n",
+ "perm = inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)\n",
+ "perm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "id": "0bba4335",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "words = 'Another sentence. NLP is the coolest Learning. According to research. I like NLP'\n",
+ "targets = [0, 0, 1, 2, 2, 2, 2, 3, 4, 4, 0, 0, 0]\n",
+ "actual_targets = [0, 0, 0, 0, 1, 2, 2, 2, -1, 2, -1, 3, -1, 4, 4, -1, 0, 0, 0]\n",
+ "targets = torch.as_tensor(targets, dtype=torch.long)\n",
+ "word_ids = [0, 0, 1, 1, 2, 3, 4, 5, 5, 6, 6, 7, 7, 8, 9, 9, 10, 11, 12]\n",
+ "word_id_mask = [True] * len(word_ids)\n",
+ "\n",
+ "targets_end = torch.tensor([\n",
+ " 0, 0, 1, 2, 2, 2, 3, 4, 5, 6, 0, 0, 0\n",
+ "])\n",
+ "actual_targets_end =[0, 0, 0, 0, 1, 2, 2, 2, -1, -1, 3, 4, -1, 5, 6, -1, 0, 0, 0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "id": "a2af8691",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def _first_appearance_of_unique_item(x):\n",
+ " unique, inverse = torch.unique(x, sorted=True, return_inverse=True)\n",
+ " perm = torch.arange(inverse.size(0), dtype=inverse.dtype, device=inverse.device)\n",
+ " inverse, perm = inverse.flip([0]), perm.flip([0])\n",
+ " perm = inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)\n",
+ " return perm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "id": "b818e6e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def _label_standard(targets, word_id_mask, word_ids):\n",
+ " max_length = len(word_ids)\n",
+ " \n",
+ " expanded_targets = torch.zeros(max_length, dtype=torch.long)\n",
+ " expanded_targets[word_id_mask] = targets[word_ids]\n",
+ " word_start_ids = _first_appearance_of_unique_item(torch.as_tensor(word_ids))\n",
+ " unique_word_ids, word_id_counts = torch.unique(torch.as_tensor(word_ids), return_counts=True)\n",
+ " \n",
+ " # here define the start and end labels\n",
+ " end = True\n",
+ " for i, (word_start_id, word_id, word_id_count) in enumerate(zip(word_start_ids, unique_word_ids, word_id_counts)):\n",
+ " curr_target = expanded_targets[word_start_id]\n",
+ " if curr_target: # step to filter the orhers\n",
+ " if word_id_count > 1:\n",
+ " ids = list(range(word_start_id, word_start_id+word_id_count))\n",
+ " \n",
+ " # TODO can make robust by adding string condition 'E-'\n",
+ " if end and curr_target % 3 == 0: # TODO condition needs to change to read end from the strateg \n",
+ " ids = ids[:-1]\n",
+ " else:\n",
+ " ids = ids[1:]\n",
+ " \n",
+ " expanded_targets[ids] = -1 # this label needs to be changed!\n",
+ " # need to have case bie\n",
+ " # need to have case bio\n",
+ " # need to have case bix\n",
+ " print(expanded_targets)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "id": "53cf5d53",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor(3) tensor(4)\n",
+ "tensor(6) tensor(0)\n",
+ "tensor([ 0, 0, 0, 0, 1, 2, 2, 2, -1, -1, 3, 4, -1, 5, -1, 6, 0, 0,\n",
+ " 0])\n"
+ ]
+ }
+ ],
+ "source": [
+ "_label_standard(targets_end, word_id_mask, word_ids)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "id": "6442dc0c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "actual_targets_end =[0, 0, 0, 0, 1, 2, 2, 2, -1, -1, 3, 4, -1, 5, -1, 6, 0, 0, 0]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "id": "d2c646ab",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([1])"
+ ]
+ },
+ "execution_count": 168,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.tensor([1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c1af9cbd",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/End-to-end_GPU.ipynb b/experiments/yousef/End-to-end_GPU.ipynb
new file mode 100644
index 0000000..f7c16b0
--- /dev/null
+++ b/experiments/yousef/End-to-end_GPU.ipynb
@@ -0,0 +1,518 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1c2ec16d",
+ "metadata": {
+ "id": "1c2ec16d"
+ },
+ "source": [
+ "# End-to-end\n",
+ "\n",
+ "This notebook should form the core skeleton of the 'run' function"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Colab Set up"
+ ],
+ "metadata": {
+ "id": "XWQS_TUhKKej"
+ },
+ "id": "XWQS_TUhKKej"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# -- env setup\n",
+ "import os\n",
+ "import gc\n",
+ "\n",
+ "!python3.7 -m pip install git+https://github.com/namiyousef/colab-utils.git\n",
+ "from colabtools.utils import get_gpu_utilization, mount_drive, install_private_library\n",
+ "\n",
+ "drive_path = mount_drive()\n",
+ "project_path = os.path.join(drive_path, 'argument-mining')\n",
+ "development_dir = os.path.join(drive_path, 'argument-mining/argminer')\n",
+ "\n",
+ "install_private_library(os.path.join(project_path, 'data/github_config.json'), 'argument-mining')"
+ ],
+ "metadata": {
+ "id": "X7OwoLnBKHQB",
+ "outputId": "1a8516e4-4d63-4fde-b736-717f6fe3357e",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "id": "X7OwoLnBKHQB",
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting git+https://github.com/namiyousef/colab-utils.git\n",
+ " Cloning https://github.com/namiyousef/colab-utils.git to /tmp/pip-req-build-5i7ydzp6\n",
+ " Running command git clone -q https://github.com/namiyousef/colab-utils.git /tmp/pip-req-build-5i7ydzp6\n",
+ "Requirement already satisfied: nvidia-ml-py3 in /usr/local/lib/python3.7/dist-packages (from colabtools==0.0.5) (7.352.0)\n",
+ "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from colabtools==0.0.5) (1.10.0+cu111)\n",
+ "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->colabtools==0.0.5) (3.10.0.2)\n",
+ "Building wheels for collected packages: colabtools\n",
+ " Building wheel for colabtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for colabtools: filename=colabtools-0.0.5-py3-none-any.whl size=3585 sha256=b0808ec10b9aba31db65c105148d19c652618702710623944c7f38fcac29e020\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-t4irkesa/wheels/1c/35/c0/364531e4ff0f0fe0f3296c80f1ee668b03ae6c6c378c5a44bf\n",
+ "Successfully built colabtools\n",
+ "Installing collected packages: colabtools\n",
+ "Successfully installed colabtools-0.0.5\n",
+ "Google Drive import successful.\n",
+ "CUDA device detected. Using GPU...\n",
+ "Mounted at /content/drive\n",
+ "Google Drive mount successful.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c1302e4f",
+ "metadata": {
+ "id": "c1302e4f"
+ },
+ "source": [
+ "### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7d8ed729",
+ "metadata": {
+ "id": "7d8ed729",
+ "outputId": "efb3ba2f-ec41-4a60-d4fb-7a4fdd2b51dc",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Import of ArgMiner successful\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- public imports\n",
+ "\n",
+ "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
+ "import pandas as pd\n",
+ "from torch.utils.data import DataLoader\n",
+ "import torch\n",
+ "from pandas.testing import assert_frame_equal\n",
+ "import time\n",
+ "\n",
+ "# -- private imports\n",
+ "from colabtools.utils import move_to_device\n",
+ "from colabtools.config import DEVICE\n",
+ "\n",
+ "# -- dev imports\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "\n",
+ "from argminer.data import ArgumentMiningDataset, TUDarmstadtProcessor, PersuadeProcessor\n",
+ "from argminer.evaluation import inference\n",
+ "from argminer.utils import encode_model_name\n",
+ "from argminer.config import LABELS_MAP_DICT\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "59272e27",
+ "metadata": {
+ "id": "59272e27"
+ },
+ "outputs": [],
+ "source": [
+ "# constants (these will be abstracted away by inputs that you give to run)\n",
+ "\n",
+ "# -- model specific configurations\n",
+ "model_name = 'google/bigbird-roberta-base'\n",
+ "max_length = 1024\n",
+ "\n",
+ "# -- training configurations\n",
+ "epochs = 5\n",
+ "batch_size = 2\n",
+ "verbose = 2\n",
+ "save_freq = 2\n",
+ "\n",
+ "# -- dataset configurations\n",
+ "dataset_name = 'Persuade'\n",
+ "\n",
+ "# -- experiment configurations\n",
+ "strategy = 'standard_bieo'\n",
+ "strat_name, strat_label = strategy.split('_')\n",
+ "\n",
+ "# -- inferred configurations\n",
+ "df_label_map = LABELS_MAP_DICT[dataset_name][strat_label]\n",
+ "num_labels = len(set(df_label_map.label))\n",
+ "Processor = eval(f'{dataset_name}Processor')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f5b81b26",
+ "metadata": {
+ "id": "f5b81b26"
+ },
+ "source": [
+ "### Tokenizer, Model and Optimizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "882197f3",
+ "metadata": {
+ "id": "882197f3",
+ "outputId": "1c6866c9-e63f-414a-8218-80d7484a4f59",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the model checkpoint at google/bigbird-roberta-base were not used when initializing BigBirdForTokenClassification: ['cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight']\n",
+ "- This IS expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of BigBirdForTokenClassification were not initialized from the model checkpoint at google/bigbird-roberta-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained(model_name, add_prefix_space=True)\n",
+ "model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=num_labels) \n",
+ "# TODO force_download\n",
+ "# TODO add option for optimizer\n",
+ "optimizer = torch.optim.Adam(params=model.parameters())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cb3e225a",
+ "metadata": {
+ "id": "cb3e225a"
+ },
+ "source": [
+ "### Dataset \n",
+ "Note this will change as the Processor develops. On the cluster you will need to use different options"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "ccc67877",
+ "metadata": {
+ "id": "ccc67877",
+ "outputId": "22ba1471-aed5-42ca-aebc-6bbc9fdcf1bc",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "| \n", + " | label_id | \n", + "label | \n", + "
|---|---|---|
| 0 | \n", + "0 | \n", + "O | \n", + "
| 1 | \n", + "1 | \n", + "B-MajorClaim | \n", + "
| 2 | \n", + "2 | \n", + "I-MajorClaim | \n", + "
| 3 | \n", + "3 | \n", + "B-Claim | \n", + "
| 4 | \n", + "4 | \n", + "I-Claim | \n", + "
| 5 | \n", + "5 | \n", + "B-Premise | \n", + "
| 6 | \n", + "6 | \n", + "I-Premise | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "72gEKBK2Ck4V",
+ "outputId": "2ad06bf4-6282-4efc-e81b-5f1e1ece5ffb"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting git+https://github.com/namiyousef/colab-utils.git\n",
+ " Cloning https://github.com/namiyousef/colab-utils.git to /tmp/pip-req-build-zdd9jwpb\n",
+ " Running command git clone -q https://github.com/namiyousef/colab-utils.git /tmp/pip-req-build-zdd9jwpb\n",
+ "Requirement already satisfied: nvidia-ml-py3 in /usr/local/lib/python3.7/dist-packages (from colabtools==0.0.1) (7.352.0)\n",
+ "Google Drive import successful.\n",
+ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
+ "Google Drive mount successful.\n",
+ "Import of ArgMiner successful\n",
+ "CUDA device detected. Using GPU...\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- env setup\n",
+ "import os\n",
+ "import gc\n",
+ "\n",
+ "!python3.7 -m pip install git+https://github.com/namiyousef/colab-utils.git\n",
+ "from colabtools.utils import get_gpu_utilization, mount_drive, install_private_library\n",
+ "\n",
+ "drive_path = mount_drive()\n",
+ "project_path = os.path.join(drive_path, 'argument-mining')\n",
+ "development_dir = os.path.join(drive_path, 'argument-mining/experiments/yousef')\n",
+ "\n",
+ "install_private_library(os.path.join(project_path, 'data/github_config.json'), 'argument-mining')\n",
+ "\n",
+ "# -- public imports\n",
+ "import sys\n",
+ "from transformers import BigBirdTokenizer, AutoTokenizer, AutoModelForTokenClassification, AutoConfig\n",
+ "import pandas as pd\n",
+ "from torch.utils.data import DataLoader\n",
+ "import torch\n",
+ "\n",
+ "# -- private imports\n",
+ "from argminer.data import KaggleDataset\n",
+ "\n",
+ "# -- dev imports\n",
+ "# add working package to system path\n",
+ "sys.path.append(development_dir)\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "from python_files.data import create_labels_doc_level\n",
+ "from python_files.run import train_longformer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "MODEL_NAME = 'allenai/longformer-base-4096'\n",
+ "print(f'GPU usage: {get_gpu_utilization()}')\n",
+ "try:\n",
+ " ma = torch.cuda.memory_allocated()\n",
+ " print(ma)\n",
+ "\n",
+ " mc = torch.cuda.memory_reserved()\n",
+ " print(mc)\n",
+ "\n",
+ " del tokenizer, model, optimizer, dataset, train_loader, df_kaggle, config_model\n",
+ " gc.collect()\n",
+ " torch.cuda.empty_cache()\n",
+ " ma = torch.cuda.memory_allocated()\n",
+ " print(ma)\n",
+ "\n",
+ " mc = torch.cuda.memory_cached()\n",
+ " print(mc)\n",
+ " print(f'GPU usage after deletion: {get_gpu_utilization()}')\n",
+ "except:\n",
+ " tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, add_prefix_space=True)\n",
+ "\n",
+ " config_model = AutoConfig.from_pretrained(MODEL_NAME) \n",
+ " config_model.num_labels = 15\n",
+ "\n",
+ " model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME, config=config_model)\n",
+ " optimizer = torch.optim.Adam(params=model.parameters())\n",
+ "\n",
+ " df_train = pd.read_csv(os.path.join(project_path, 'data/kaggle/test.csv'))\n",
+ " df_kaggle = df_train[['text', 'labels']].copy(); del df_train\n",
+ " df_kaggle.labels = df_kaggle.labels.apply(lambda x: [int(num) for num in x[1:-1].split(', ')])\n",
+ " df_kaggle.head(), df_kaggle.info()\n",
+ " \n",
+ " dataset = KaggleDataset(df_kaggle, tokenizer, 512)\n",
+ " train_loader = DataLoader(dataset, shuffle=True, batch_size=1)\n",
+ "\n",
+ " print(f'GPU usage after creation: {get_gpu_utilization()}')\n",
+ " torch.backends.cudnn.benchmark = True\n",
+ " torch.backends.cudnn.enabled = True\n",
+ " print(f'GPU mem: {get_gpu_utilization()}')\n",
+ " model.to('cuda:0')\n",
+ " print(f'GPU mem after model sent: {get_gpu_utilization()}')\n",
+ " for i, (inputs, targets) in enumerate(train_loader):\n",
+ " inputs = {key: val.to('cuda:0') for key, val in inputs.items()}\n",
+ " targets = targets.to('cuda:0')\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " loss, outputs = model(labels=targets, **inputs, return_dict=False)\n",
+ " print(f'GPU mem modelling batch {i+1}: {get_gpu_utilization()}')\n",
+ " del targets, inputs, loss, outputs\n",
+ " gc.collect()\n",
+ " torch.cuda.empty_cache()\n",
+ " print(f'GPU mem batch {i+1}: {get_gpu_utilization()}')\n",
+ " break\n",
+ " model.to('cpu')\n",
+ "\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JXbK-lQACvlQ",
+ "outputId": "c78af27e-c9ca-4cc5-8a21-da26e95ca8a4"
+ },
+ "execution_count": 31,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "GPU usage: 1330\n",
+ "0\n",
+ "0\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the model checkpoint at allenai/longformer-base-4096 were not used when initializing LongformerForTokenClassification: ['lm_head.dense.weight', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.decoder.weight']\n",
+ "- This IS expected if you are initializing LongformerForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing LongformerForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of LongformerForTokenClassification were not initialized from the model checkpoint at allenai/longformer-base-4096 and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " label_type label text \\\n",
+ "0 O Other Should students be taught to compete or to coo... \n",
+ "1 T1 MajorClaim we should attach more importance to cooperatio... \n",
+ "2 O Other .\\nFirst of all, \n",
+ "3 T3 Claim through cooperation, children can learn about ... \n",
+ "4 O Other . \n",
+ "\n",
+ " span_start span_end doc_id \\\n",
+ "0 0 503 essay001 \n",
+ "1 503 575 essay001 \n",
+ "2 575 591 essay001 \n",
+ "3 591 714 essay001 \n",
+ "4 714 716 essay001 \n",
+ "\n",
+ " predictionString \\\n",
+ "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... \n",
+ "1 83 84 85 86 87 88 89 90 91 92 \n",
+ "2 93 94 95 96 \n",
+ "3 97 98 99 100 101 102 103 104 105 106 107 108 1... \n",
+ "4 115 \n",
+ "\n",
+ " label_bio \\\n",
+ "0 O O O O O O O O O O O O O O O O O O O O O O O ... \n",
+ "1 B-MajorClaim I-MajorClaim I-MajorClaim I-Major... \n",
+ "2 O O O O \n",
+ "3 B-Claim I-Claim I-Claim I-Claim I-Claim I-Clai... \n",
+ "4 O \n",
+ "\n",
+ " label_bieo \n",
+ "0 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "1 [B-MajorClaim, I-MajorClaim, I-MajorClaim, I-M... \n",
+ "2 [O, O, O, O] \n",
+ "3 [B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, ... \n",
+ "4 [O] "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path_label_map = '../../data/UCL/dataset2/df_label_map_bio.json'\n",
+ "path_dataset = '../../data/UCL/dataset2/df_labels_general.json'\n",
+ "\n",
+ "df_label_map = pd.read_json(path_label_map)\n",
+ "df_text = pd.read_json(path_dataset)\n",
+ "df_text.predictionString = df_text.predictionString.apply(lambda x: ' '.join([str(item) for item in x]))\n",
+ "df_text.label_bio = df_text.label_bio.apply(lambda x: ' '.join([str(item) for item in x]))\n",
+ "df_text.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "b3719a13",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "doc_id \n",
+ "essay001 Should students be taught to compete or to coo... \n",
+ "essay002 More people are migrating to other countries t... \n",
+ "essay003 International tourism is now more common than ... \n",
+ "essay004 International tourism is now more common than ... \n",
+ "essay005 Living and studying overseas\\n\\nIt is every st... \n",
+ "... ... \n",
+ "essay398 We can not forcedly put the same numbers of ma... \n",
+ "essay399 Drugs, alcohol and messy sex lives\\n\\nCelebrit... \n",
+ "essay400 A greater proportion of the budget should be a... \n",
+ "essay401 Fatherhood should be as present as motherhood ... \n",
+ "essay402 Children should studying hard or playing sport... \n",
+ "\n",
+ " predictionString \\\n",
+ "doc_id \n",
+ "essay001 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... \n",
+ "essay002 388 389 390 391 392 393 394 395 396 397 398 39... \n",
+ "essay003 734 735 736 737 738 739 740 741 742 743 744 74... \n",
+ "essay004 1004 1005 1006 1007 1008 1009 1010 1011 1012 1... \n",
+ "essay005 1303 1304 1305 1306 1307 1308 1309 1310 1311 1... \n",
+ "... ... \n",
+ "essay398 135846 135847 135848 135849 135850 135851 1358... \n",
+ "essay399 136187 136188 136189 136190 136191 136192 1361... \n",
+ "essay400 136459 136460 136461 136462 136463 136464 1364... \n",
+ "essay401 136752 136753 136754 136755 136756 136757 1367... \n",
+ "essay402 137042 137043 137044 137045 137046 137047 1370... \n",
+ "\n",
+ " label_bio \n",
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+ "essay001 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "essay002 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "essay003 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "essay004 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "essay005 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "... ... \n",
+ "essay398 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "essay399 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "essay400 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "essay401 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "essay402 [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... \n",
+ "\n",
+ "[402 rows x 3 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_input = df_text.groupby('doc_id').agg({\n",
+ " 'text':lambda x: ' '.join(x),\n",
+ " 'predictionString': lambda x: ' '.join(x),\n",
+ " 'label_bio': lambda x: ' '.join(x)\n",
+ "})\n",
+ "df_input.label_bio = df_input.label_bio.str.split()\n",
+ "df_input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "7fe294b1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['O',\n",
+ " 'O',\n",
+ " 'O',\n",
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+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
+ "df_input.label_bio.values[0]"
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+ "id": "c8964fff",
+ "metadata": {},
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+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "d8d6af91",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of the model checkpoint at google/bigbird-roberta-base were not used when initializing BigBirdForTokenClassification: ['cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias']\n",
+ "- This IS expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of BigBirdForTokenClassification were not initialized from the model checkpoint at google/bigbird-roberta-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
+ "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_name = 'google/bigbird-roberta-base'\n",
+ "num_labels = df_label_map.shape[0]\n",
+ "\n",
+ "model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=num_labels)\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+ "optimizer = torch.optim.Adam(params=model.parameters())\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "31b5d792",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "limit_size = 2\n",
+ "\n",
+ "dataset = ArgumentMiningDataset(\n",
+ " df_label_map,\n",
+ " df_input[['text', 'label_bio']].rename(columns={'label_bio':'labels'}),\n",
+ " tokenizer,\n",
+ " max_length=256,\n",
+ " strategy='standard_bio'\n",
+ ")\n",
+ "\n",
+ "trainloader = DataLoader(dataset, batch_size=2, shuffle=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "6387ca18",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Attention type 'block_sparse' is not possible if sequence_length: 256 <= num global tokens: 2 * config.block_size + min. num sliding tokens: 3 * config.block_size + config.num_random_blocks * config.block_size + additional buffer: config.num_random_blocks * config.block_size = 704 with config.block_size = 64, config.num_random_blocks = 3. Changing attention type to 'original_full'...\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.902972936630249\n",
+ "1.422654628753662\n"
+ ]
+ }
+ ],
+ "source": [
+ "epochs = 2\n",
+ "for epoch in range(epochs):\n",
+ " training_loss = 0\n",
+ " for i, (inputs, targets) in enumerate(trainloader):\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " loss, outputs = model(\n",
+ " labels=targets,\n",
+ " input_ids=inputs['input_ids'],\n",
+ " attention_mask=inputs['attention_mask'],\n",
+ " return_dict=False\n",
+ " )\n",
+ " \n",
+ " training_loss += loss.item()\n",
+ "\n",
+ " # backward pass\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " break\n",
+ " print(training_loss/(i+1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "6714e61c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "387\n",
+ "tensor([0, 1, 2, 3, 4, 5, 6])\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_val = df_input[['text', 'label_bio']].rename(columns={'label_bio':'labels'})\n",
+ "val_dataset = ArgumentMiningDataset(\n",
+ " df_label_map,\n",
+ " df_val,\n",
+ " tokenizer,\n",
+ " max_length=512,\n",
+ " strategy='standard_bio',\n",
+ " is_train = False\n",
+ ")\n",
+ "print(df_val.labels.values[0].__len__())\n",
+ "valloader = DataLoader(val_dataset, batch_size=2, shuffle=False)\n",
+ "for (inputs, targets) in valloader:\n",
+ " print(torch.unique(targets))\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 213,
+ "id": "d11e9a4b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def evaluate(df_outputs, df_targets):\n",
+ " # TODO problem of duplicates? Think about this with test cases\n",
+ " \"\"\"\n",
+ " Calculates the F-Score given prediction strings\n",
+ " \"\"\"\n",
+ " \n",
+ " # -- Constants\n",
+ " gt, pred = '_gt', '_pred'\n",
+ " \n",
+ " # Merge constants\n",
+ " merge_on = ['id', 'class']\n",
+ " suffixes = (gt, pred)\n",
+ "\n",
+ " # Columns labels\n",
+ " gt_id = f'index{gt}'\n",
+ " pred_id = f'index{pred}'\n",
+ " \n",
+ " overlap_pred_gt = f'overlap{pred}{gt}' # how much of ground truth in prediction\n",
+ " overlap_gt_pred = f'overlap{gt}{pred}' # how much of prediction in ground truth\n",
+ " pred_col_name = 'predictionString'\n",
+ " gt = f'{pred_col_name}{gt}'\n",
+ " pred = f'{pred_col_name}{pred}'\n",
+ "\n",
+ "\n",
+ " \n",
+ " # TODO is this even correct?? Would you not be repeating items??\n",
+ " # find all combinations\n",
+ " # REally really not sure of this assumption..\n",
+ " \n",
+ " df_targets = df_targets.reset_index()\n",
+ " df_outputs = df_outputs.reset_index()\n",
+ " \n",
+ " df_targets = df_targets.merge(\n",
+ " df_outputs, on=merge_on, how='outer', suffixes=suffixes\n",
+ " )\n",
+ " \n",
+ " df_targets[[gt, pred]] = df_targets[[gt, pred]].where(~df_targets[[gt, pred]].isnull(), {-1})\n",
+ "\n",
+ " # find intersection and normalise against each item\n",
+ " df_targets[overlap_pred_gt] = df_targets[[gt, pred]].apply(\n",
+ " lambda x: len(x[gt].intersection(x[pred])) / len(x[pred]), axis=1\n",
+ " )\n",
+ " \n",
+ " df_targets[overlap_gt_pred] = df_targets[[pred, gt]].apply(\n",
+ " lambda x: len(x[gt].intersection(x[pred])) / len(x[gt]), axis=1\n",
+ " )\n",
+ " \n",
+ " \n",
+ " df_targets['tp'] = df_targets[[overlap_pred_gt, overlap_gt_pred]].apply(\n",
+ " lambda x: int(x.overlap_pred_gt >= 0.5 and x.overlap_gt_pred >= 0.5), axis=1\n",
+ " )\n",
+ " df_targets['overlap_max'] = df_targets[[overlap_pred_gt, overlap_gt_pred]].max(axis=1)\n",
+ "\n",
+ " df_targets[pred] = df_targets[pred].apply(lambda x: tuple(x))\n",
+ " df_targets[gt] = df_targets[gt].apply(lambda x: tuple(x))\n",
+ " \n",
+ " # only keep the ones with the highest overall score\n",
+ " \n",
+ " \n",
+ " # I'm not convinced by this tbh? What about cases of perfect match on one but not on the other?\n",
+ " # e.g. case when you have 0.5 and 1 and then 0.8 and 1\n",
+ " df_targets_match = df_targets[df_targets['tp'] == 1].sort_values('overlap_max', ascending=False).groupby(\n",
+ " ['id', 'class', gt] # group by pred or gt?\n",
+ " ).first()\n",
+ "\n",
+ " TP = df_targets_match.groupby('class')[pred_id].nunique().to_frame('tp').reset_index()\n",
+ " \n",
+ " df_false_positive = df_targets[\n",
+ " # they didn't do this filter?\n",
+ " #(df_targets['tp'] == 0) & \n",
+ " (~df_targets.set_index(pred_id).index.isin(df_targets_match[pred_id]))\n",
+ " \n",
+ " ]\n",
+ " \n",
+ " FP = df_false_positive.groupby('class')[pred_id].nunique().to_frame('fp').reset_index()\n",
+ "\n",
+ "\n",
+ " matched_gt_id = df_targets[df_targets['tp'] == 1][gt_id].unique()\n",
+ " \n",
+ " print(df_targets[df_targets[gt_id].isin(matched_gt_id)].sort_values('class'))\n",
+ "\n",
+ " df_false_negative = df_targets[\n",
+ " #(df_targets['tp'] == 0) & \n",
+ " (~df_targets.set_index(gt_id).index.isin(matched_gt_id))\n",
+ " ]\n",
+ " \n",
+ " FN = df_false_negative.groupby('class')[gt_id].nunique().to_frame('fn').reset_index()\n",
+ " \n",
+ " #TP = df_targets_match.reset_index().groupby(['id', 'class']).size().to_frame('tp').reset_index()\n",
+ " #FN = df_false_negatives.groupby(['id', 'class']).size().to_frame('fn').reset_index()\n",
+ " #FP = df_false_positives.groupby(['id', 'class']).size().to_frame('fp').reset_index()\n",
+ " \n",
+ " scores = TP.merge(\n",
+ " FN.merge(FP, how='outer'), how='outer'\n",
+ " ).fillna(0)\n",
+ " scores = scores.assign(f1=lambda x: x['tp'] / (x['tp'] + 1/2 * (x['fp'] + x['fn'])))\n",
+ " print(scores)\n",
+ "\n",
+ "\n",
+ " return scores['f1'].mean()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 214,
+ "id": "f9ae8a20",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " index_gt id class predictionString_gt index_pred predictionString_pred \\\n",
+ "3 1.0 1 1 (8, 6, 7) 0 (1, 2) \n",
+ "4 1.0 1 1 (8, 6, 7) 1 (6, 7) \n",
+ "5 1.0 1 1 (8, 6, 7) 4 (4, 5, 6, 7) \n",
+ "9 3.0 2 1 (1, 2, 3) 2 (1, 2, 3) \n",
+ "10 4.0 1 2 (1, 2) 3 (1, 2) \n",
+ "11 5.0 3 3 (16, 17, 18, 19) 5 (16, 17) \n",
+ "12 5.0 3 3 (16, 17, 18, 19) 6 (18, 19) \n",
+ "\n",
+ " overlap_pred_gt overlap_gt_pred tp overlap_max \n",
+ "3 0.0 0.000000 0 0.000000 \n",
+ "4 1.0 0.666667 1 1.000000 \n",
+ "5 0.5 0.666667 1 0.666667 \n",
+ "9 1.0 1.000000 1 1.000000 \n",
+ "10 1.0 1.000000 1 1.000000 \n",
+ "11 1.0 0.500000 1 1.000000 \n",
+ "12 1.0 0.500000 1 1.000000 \n",
+ " class tp fn fp f1\n",
+ "0 1 2.0 2.0 2.0 0.500000\n",
+ "1 2 1.0 0.0 0.0 1.000000\n",
+ "2 3 1.0 0.0 1.0 0.666667\n",
+ "3 4 0.0 0.0 1.0 0.000000\n",
+ "0.5416666666666666\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_outputs = pd.DataFrame(data={\n",
+ " 'id':[1,1,2,1, 1, 3, 3, 4],\n",
+ " 'class':[1,1,1, 2, 1, 3, 3, 4],\n",
+ " 'predictionString':[{1,2}, {6,7}, {1,2,3}, {1,2}, {4,5,6,7}, {16,17}, {18,19}, {1}] # TODO WHAT HAPPENS WITH THIS?\n",
+ "})\n",
+ "df_targets = pd.DataFrame(data={\n",
+ " 'id':[1,1,1,2, 1,3],\n",
+ " 'class':[1,1,1,1, 2,3],\n",
+ " 'predictionString':[{1,2,3, 4, 5}, {6,7,8}, {21,22,23,24,25}, {1,2,3}, {1,2}, {16,17,18,19}]\n",
+ "})\n",
+ "\n",
+ "print(evaluate(df_outputs, df_targets))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 221,
+ "id": "135a73c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " id class new_predictionstring_pred pred_id discourse_type \\\n",
+ "1 1 1 1 2 0 1 \n",
+ "4 1 1 6 7 1 1 \n",
+ "7 1 1 4 5 6 7 3 1 \n",
+ "9 2 1 1 2 3 2 1 \n",
+ "\n",
+ " new_predictionstring_gt gt_id overlaps overlap1 \\\n",
+ "1 8 6 7 1 [0.0, 0.0] 0.000000 \n",
+ "4 8 6 7 1 [0.6666666666666666, 1.0] 0.666667 \n",
+ "7 8 6 7 1 [0.6666666666666666, 0.5] 0.666667 \n",
+ "9 1 2 3 3 [1.0, 1.0] 1.000000 \n",
+ "\n",
+ " overlap2 potential_TP max_overlap \n",
+ "1 0.0 False 0.000000 \n",
+ "4 1.0 True 1.000000 \n",
+ "7 0.5 True 0.666667 \n",
+ "9 1.0 True 1.000000 \n",
+ "2 2 2\n",
+ "1 : 0.500000\n",
+ " id class new_predictionstring_pred pred_id discourse_type \\\n",
+ "0 1 2 1 2 0 2 \n",
+ "\n",
+ " new_predictionstring_gt gt_id overlaps overlap1 overlap2 \\\n",
+ "0 1 2 0 [1.0, 1.0] 1.0 1.0 \n",
+ "\n",
+ " potential_TP max_overlap \n",
+ "0 True 1.0 \n",
+ "1 0 0\n",
+ "2 : 1.000000\n",
+ " id class new_predictionstring_pred pred_id discourse_type \\\n",
+ "0 3 3 16 17 0 3 \n",
+ "1 3 3 18 19 1 3 \n",
+ "\n",
+ " new_predictionstring_gt gt_id overlaps overlap1 overlap2 \\\n",
+ "0 16 17 18 19 0 [0.5, 1.0] 0.5 1.0 \n",
+ "1 16 17 18 19 0 [0.5, 1.0] 0.5 1.0 \n",
+ "\n",
+ " potential_TP max_overlap \n",
+ "0 True 1.0 \n",
+ "1 True 1.0 \n",
+ "1 0 1\n",
+ "3 : 0.666667\n",
+ "Empty DataFrame\n",
+ "Columns: [id, class, new_predictionstring_pred, pred_id, discourse_type, new_predictionstring_gt, gt_id, overlaps, overlap1, overlap2, potential_TP, max_overlap]\n",
+ "Index: []\n",
+ "0 1 1\n",
+ "4 : 0.000000\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "0.5416666666666666"
+ ]
+ },
+ "execution_count": 221,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def calc_overlap(row):\n",
+ " \"\"\"\n",
+ " 计算 prediction and ground truth的重叠部分\n",
+ " \"\"\"\n",
+ " set_pred = set(row.new_predictionstring_pred.split(' '))\n",
+ " set_gt = set(row.new_predictionstring_gt.split(' '))\n",
+ " # length of each end intersection\n",
+ " len_pred = len(set_pred)\n",
+ " len_gt = len(set_gt)\n",
+ " intersection = len(set_gt.intersection(set_pred)) # 两者交集\n",
+ " overlap_1 = intersection / len_gt # 交集占ground truth比例\n",
+ " overlap_2 = intersection / len_pred # 交集占prediction比例\n",
+ " return [overlap_1, overlap_2]\n",
+ "\n",
+ "def score_feedback_comp(pred_df, gt_df):\n",
+ " \n",
+ " \"\"\"\n",
+ " A function that scores for the kaggle\n",
+ " Student Writing Competition\n",
+ " Uses the steps in the evaluation page here:\n",
+ " https://www.kaggle.com/c/feedback-prize-2021/overview/evaluation\n",
+ " 关于评价指标的介绍，详见讲义的第一周。\n",
+ " \"\"\"\n",
+ " pred_df.new_predictionstring = pred_df.new_predictionstring.apply(\n",
+ " lambda x: ' '.join([str(item) for item in x])\n",
+ " )\n",
+ " gt_df.new_predictionstring = gt_df.new_predictionstring.apply(\n",
+ " lambda x: ' '.join([str(item) for item in x])\n",
+ " )\n",
+ " \n",
+ " gt_df = gt_df[['id', 'discourse_type', 'new_predictionstring']].reset_index(drop = True).copy() # ground truth\n",
+ " pred_df = pred_df[['id', 'class', 'new_predictionstring']].reset_index(drop = True).copy() # prediction\n",
+ " gt_df['gt_id'] = gt_df.index\n",
+ " pred_df['pred_id'] = pred_df.index\n",
+ " joined = pred_df.merge( \n",
+ " gt_df, \n",
+ " left_on = ['id', 'class'],\n",
+ " right_on = ['id', 'discourse_type'], \n",
+ " how = 'outer', # outer join\n",
+ " suffixes = ['_pred', '_gt'] # 重复列的后缀\n",
+ " )\n",
+ " joined['new_predictionstring_gt'] = joined['new_predictionstring_gt'].fillna(' ') # 填充空格\n",
+ " joined['new_predictionstring_pred'] = joined['new_predictionstring_pred'].fillna(' ') # 填充空格\n",
+ " joined['overlaps'] = joined.apply(calc_overlap, axis = 1) # \n",
+ " # overlap over 0.5: true positive\n",
+ " # If nultiple overlaps exists, the higher is taken.\n",
+ " joined['overlap1'] = joined['overlaps'].apply(lambda x: eval(str(x))[0])\n",
+ " joined['overlap2'] = joined['overlaps'].apply(lambda x: eval(str(x))[1])\n",
+ " \n",
+ " # 两种overlap都大于0.5，则为TP\n",
+ " joined['potential_TP'] = (joined['overlap1'] >= 0.5) & (joined['overlap2'] >= 0.5)\n",
+ " joined['max_overlap'] = joined[['overlap1', 'overlap2']].max(axis = 1) # 取大的值\n",
+ "\n",
+ " # query: 只取'potential_TP'==True的样本\n",
+ " # sort_values: 按max_overlap降序\n",
+ " # groupby: 按['id', 'new_predictionstring_gt'] 分组获取index\n",
+ " tp_pred_ids = joined.query('potential_TP').sort_values('max_overlap', ascending = False)\\\n",
+ " .groupby(['id', 'new_predictionstring_gt']).first()['pred_id'].values\n",
+ " \n",
+ " fp_pred_ids = [p for p in joined['pred_id'].unique() if p not in tp_pred_ids] # FP数据\n",
+ " matched_gt_ids = joined.query('potential_TP')['gt_id'].unique()\n",
+ " print(joined[joined.gt_id.isin(matched_gt_ids)])\n",
+ " unmatched_gt_ids = [c for c in joined['gt_id'].unique() if c not in matched_gt_ids] # FN数据\n",
+ "\n",
+ " TP = len(tp_pred_ids)\n",
+ " FP = len(fp_pred_ids)\n",
+ " FN = len(unmatched_gt_ids)\n",
+ " print(TP, FN, FP)\n",
+ " # macro_f1_score 公式\n",
+ " macro_f1_score = TP / (TP + 1/2 * (FP + FN))\n",
+ " return macro_f1_score\n",
+ "\n",
+ "import numpy as np\n",
+ "def oof_score(df_val, oof):\n",
+ " '''\n",
+ " OOF分数计算\n",
+ " '''\n",
+ " f1score = []\n",
+ " classes = [1,2,3,4]\n",
+ " # 计算每个class的分数\n",
+ " for c in classes: \n",
+ " pred_df = oof.loc[oof['class'] == c].copy()\n",
+ " gt_df = df_val.loc[df_val['discourse_type'] == c].copy()\n",
+ " f1 = score_feedback_comp(pred_df, gt_df)\n",
+ " print(f'{c:<10}: {f1:4f}')\n",
+ " f1score.append(f1)\n",
+ " f1avg = np.mean(f1score)\n",
+ " return f1avg\n",
+ "\n",
+ "oof_score(\n",
+ " df_targets.rename(columns={\n",
+ " 'class':'discourse_type',\n",
+ " 'predictionString': 'new_predictionstring'\n",
+ " }),\n",
+ " df_outputs.rename(columns={\n",
+ " 'predictionString': 'new_predictionstring'\n",
+ " })\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 196,
+ "id": "45dfa452",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'valloader' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [196]\u001b[0m, in \u001b[0;36m| \n", + " | text | \n", + "predictionString | \n", + "label_bio | \n", + "
|---|---|---|---|
| doc_id | \n", + "\n", + " | \n", + " | \n", + " |
| essay001 | \n", + "Should students be taught to compete or to coo... | \n", + "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| essay002 | \n", + "More people are migrating to other countries t... | \n", + "388 389 390 391 392 393 394 395 396 397 398 39... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| essay003 | \n", + "International tourism is now more common than ... | \n", + "734 735 736 737 738 739 740 741 742 743 744 74... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| essay004 | \n", + "International tourism is now more common than ... | \n", + "1004 1005 1006 1007 1008 1009 1010 1011 1012 1... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| essay005 | \n", + "Living and studying overseas\\n\\nIt is every st... | \n", + "1303 1304 1305 1306 1307 1308 1309 1310 1311 1... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| essay398 | \n", + "We can not forcedly put the same numbers of ma... | \n", + "135846 135847 135848 135849 135850 135851 1358... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| essay399 | \n", + "Drugs, alcohol and messy sex lives\\n\\nCelebrit... | \n", + "136187 136188 136189 136190 136191 136192 1361... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| essay400 | \n", + "A greater proportion of the budget should be a... | \n", + "136459 136460 136461 136462 136463 136464 1364... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| essay401 | \n", + "Fatherhood should be as present as motherhood ... | \n", + "136752 136753 136754 136755 136756 136757 1367... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| essay402 | \n", + "Children should studying hard or playing sport... | \n", + "137042 137043 137044 137045 137046 137047 1370... | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
402 rows × 3 columns
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " tp fp\n",
+ "0 True 1\n",
+ "1 True 2"
+ ]
+ },
+ "execution_count": 140,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.DataFrame({'tp':[True,True,False, False], 'fp':[1,2,3,4]}).query('tp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "id": "b01f066d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | tp | \n", + "fp | \n", + "
|---|---|---|
| 0 | \n", + "True | \n", + "1 | \n", + "
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+ "text/plain": [
+ " label_type label text \\\n",
+ "0 O Other Should students be taught to compete or to coo... \n",
+ "1 T1 MajorClaim we should attach more importance to cooperatio... \n",
+ "2 O Other .\\nFirst of all, \n",
+ "3 T3 Claim through cooperation, children can learn about ... \n",
+ "4 O Other . \n",
+ "5 T4 Premise What we acquired from team work is not only ho... \n",
+ "6 O Other . \n",
+ "7 T5 Premise During the process of cooperation, children ca... \n",
+ "8 O Other . \n",
+ "9 T6 Premise All of these skills help them to get on well w... \n",
+ "10 O Other .\\nOn the other hand, \n",
+ "11 T8 Premise the significance of competition is that how to... \n",
+ "12 O Other . Hence it is always said that \n",
+ "13 T7 Claim competition makes the society more effective \n",
+ "14 O Other . However, \n",
+ "15 T9 Premise when we consider about the question that how t... \n",
+ "16 O Other . The greater our goal is, the more competitio... \n",
+ "17 T10 Premise Take Olympic games which is a form of competit... \n",
+ "18 O Other . The winner is the athlete but the success be... \n",
+ "19 T11 Claim without the cooperation, there would be no vic... \n",
+ "20 O Other .\\nConsequently, no matter from the view of in... \n",
+ "21 T2 MajorClaim a more cooperative attitudes towards life is m... \n",
+ "22 O Other . \n",
+ "\n",
+ " span_start span_end doc_id \\\n",
+ "0 0 503 essay001 \n",
+ "1 503 575 essay001 \n",
+ "2 575 591 essay001 \n",
+ "3 591 714 essay001 \n",
+ "4 714 716 essay001 \n",
+ "5 716 851 essay001 \n",
+ "6 851 853 essay001 \n",
+ "7 853 1086 essay001 \n",
+ "8 1086 1088 essay001 \n",
+ "9 1088 1191 essay001 \n",
+ "10 1191 1212 essay001 \n",
+ "11 1212 1301 essay001 \n",
+ "12 1301 1332 essay001 \n",
+ "13 1332 1376 essay001 \n",
+ "14 1376 1387 essay001 \n",
+ "15 1387 1492 essay001 \n",
+ "16 1492 1549 essay001 \n",
+ "17 1549 1846 essay001 \n",
+ "18 1846 1927 essay001 \n",
+ "19 1927 1992 essay001 \n",
+ "20 1992 2154 essay001 \n",
+ "21 2154 2231 essay001 \n",
+ "22 2231 2232 essay001 \n",
+ "\n",
+ " predictionString \\\n",
+ "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... \n",
+ "1 83 84 85 86 87 88 89 90 91 92 \n",
+ "2 93 94 95 96 \n",
+ "3 97 98 99 100 101 102 103 104 105 106 107 108 1... \n",
+ "4 115 \n",
+ "5 116 117 118 119 120 121 122 123 124 125 126 12... \n",
+ "6 142 \n",
+ "7 143 144 145 146 147 148 149 150 151 152 153 15... \n",
+ "8 180 \n",
+ "9 181 182 183 184 185 186 187 188 189 190 191 19... \n",
+ "10 202 203 204 205 206 \n",
+ "11 207 208 209 210 211 212 213 214 215 216 217 21... \n",
+ "12 222 223 224 225 226 227 228 \n",
+ "13 229 230 231 232 233 234 \n",
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+ "execution_count": 144,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_text[df_text.doc_id == 'essay001']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 157,
+ "id": "332cc1e5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_predictionString(labels, doc_ids):\n",
+ " \"\"\" \n",
+ " \"\"\"\n",
+ " # TODO need to ensure that we are mapping to the correct class names and doc IDs!\n",
+ " ids = []\n",
+ " classes = []\n",
+ " prediction_strings = []\n",
+ " for doc_id, label in zip(doc_ids, labels):\n",
+ " unique_classes, unique_class_counts = torch.unique_consecutive(label, return_counts=True)\n",
+ " start_id = 1\n",
+ " for unique_class, unique_class_count in zip(unique_classes, unique_class_counts):\n",
+ " ids.append(doc_id.item())\n",
+ " classes.append(unique_class.item())\n",
+ " end_id = start_id + unique_class_count + 1 \n",
+ " prediction_strings.append(set(range(start_id, end_id)))\n",
+ " start_id = end_id\n",
+ " return pd.DataFrame(data={'id': ids, 'class': classes, 'predictionString': prediction_strings})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 127,
+ "id": "fd592852",
+ "metadata": {},
+ "outputs": [
+ {
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+ "18 O Other . The winner is the athlete but the success be... \n",
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+ "20 O Other .\\nConsequently, no matter from the view of in... \n",
+ "21 T2 MajorClaim a more cooperative attitudes towards life is m... \n",
+ "22 O Other . \n",
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+ "execution_count": 127,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
+ "df_text[df_text.doc_id == 'essay001']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b9c6aeb9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_predictionString(labels, doc_ids):\n",
+ " for label in labels:\n",
+ " unique_classes, unique_class_counts = torch.unique_consecutive(return_counts=True)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "id": "894f7bfa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "doc_id\n",
+ "essay001 387\n",
+ "essay002 346\n",
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+ },
+ "execution_count": 99,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_input.iloc[[0,1]].label_bio.apply(lambda x: len(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "7852a499",
+ "metadata": {},
+ "outputs": [
+ {
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| 2 | \n", + "O | \n", + "Other | \n", + ".\\nFirst of all, | \n", + "575 | \n", + "591 | \n", + "essay001 | \n", + "93 94 95 96 | \n", + "O O O O | \n", + "[O, O, O, O] | \n", + "
| 3 | \n", + "T3 | \n", + "Claim | \n", + "through cooperation, children can learn about ... | \n", + "591 | \n", + "714 | \n", + "essay001 | \n", + "97 98 99 100 101 102 103 104 105 106 107 108 1... | \n", + "B-Claim I-Claim I-Claim I-Claim I-Claim I-Clai... | \n", + "[B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, ... | \n", + "
| 4 | \n", + "O | \n", + "Other | \n", + ". | \n", + "714 | \n", + "716 | \n", + "essay001 | \n", + "115 | \n", + "O | \n", + "[O] | \n", + "
| 5 | \n", + "T4 | \n", + "Premise | \n", + "What we acquired from team work is not only ho... | \n", + "716 | \n", + "851 | \n", + "essay001 | \n", + "116 117 118 119 120 121 122 123 124 125 126 12... | \n", + "B-Premise I-Premise I-Premise I-Premise I-Prem... | \n", + "[B-Premise, I-Premise, I-Premise, I-Premise, I... | \n", + "
| 6 | \n", + "O | \n", + "Other | \n", + ". | \n", + "851 | \n", + "853 | \n", + "essay001 | \n", + "142 | \n", + "O | \n", + "[O] | \n", + "
| 7 | \n", + "T5 | \n", + "Premise | \n", + "During the process of cooperation, children ca... | \n", + "853 | \n", + "1086 | \n", + "essay001 | \n", + "143 144 145 146 147 148 149 150 151 152 153 15... | \n", + "B-Premise I-Premise I-Premise I-Premise I-Prem... | \n", + "[B-Premise, I-Premise, I-Premise, I-Premise, I... | \n", + "
| 8 | \n", + "O | \n", + "Other | \n", + ". | \n", + "1086 | \n", + "1088 | \n", + "essay001 | \n", + "180 | \n", + "O | \n", + "[O] | \n", + "
| 9 | \n", + "T6 | \n", + "Premise | \n", + "All of these skills help them to get on well w... | \n", + "1088 | \n", + "1191 | \n", + "essay001 | \n", + "181 182 183 184 185 186 187 188 189 190 191 19... | \n", + "B-Premise I-Premise I-Premise I-Premise I-Prem... | \n", + "[B-Premise, I-Premise, I-Premise, I-Premise, I... | \n", + "
| 10 | \n", + "O | \n", + "Other | \n", + ".\\nOn the other hand, | \n", + "1191 | \n", + "1212 | \n", + "essay001 | \n", + "202 203 204 205 206 | \n", + "O O O O O | \n", + "[O, O, O, O, O] | \n", + "
| 11 | \n", + "T8 | \n", + "Premise | \n", + "the significance of competition is that how to... | \n", + "1212 | \n", + "1301 | \n", + "essay001 | \n", + "207 208 209 210 211 212 213 214 215 216 217 21... | \n", + "B-Premise I-Premise I-Premise I-Premise I-Prem... | \n", + "[B-Premise, I-Premise, I-Premise, I-Premise, I... | \n", + "
| 12 | \n", + "O | \n", + "Other | \n", + ". Hence it is always said that | \n", + "1301 | \n", + "1332 | \n", + "essay001 | \n", + "222 223 224 225 226 227 228 | \n", + "O O O O O O O | \n", + "[O, O, O, O, O, O, O] | \n", + "
| 13 | \n", + "T7 | \n", + "Claim | \n", + "competition makes the society more effective | \n", + "1332 | \n", + "1376 | \n", + "essay001 | \n", + "229 230 231 232 233 234 | \n", + "B-Claim I-Claim I-Claim I-Claim I-Claim I-Claim | \n", + "[B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, ... | \n", + "
| 14 | \n", + "O | \n", + "Other | \n", + ". However, | \n", + "1376 | \n", + "1387 | \n", + "essay001 | \n", + "235 236 | \n", + "O O | \n", + "[O, O] | \n", + "
| 15 | \n", + "T9 | \n", + "Premise | \n", + "when we consider about the question that how t... | \n", + "1387 | \n", + "1492 | \n", + "essay001 | \n", + "237 238 239 240 241 242 243 244 245 246 247 24... | \n", + "B-Premise I-Premise I-Premise I-Premise I-Prem... | \n", + "[B-Premise, I-Premise, I-Premise, I-Premise, I... | \n", + "
| 16 | \n", + "O | \n", + "Other | \n", + ". The greater our goal is, the more competitio... | \n", + "1492 | \n", + "1549 | \n", + "essay001 | \n", + "257 258 259 260 261 262 263 264 265 266 267 | \n", + "O O O O O O O O O O O | \n", + "[O, O, O, O, O, O, O, O, O, O, O] | \n", + "
| 17 | \n", + "T10 | \n", + "Premise | \n", + "Take Olympic games which is a form of competit... | \n", + "1549 | \n", + "1846 | \n", + "essay001 | \n", + "268 269 270 271 272 273 274 275 276 277 278 27... | \n", + "B-Premise I-Premise I-Premise I-Premise I-Prem... | \n", + "[B-Premise, I-Premise, I-Premise, I-Premise, I... | \n", + "
| 18 | \n", + "O | \n", + "Other | \n", + ". The winner is the athlete but the success be... | \n", + "1846 | \n", + "1927 | \n", + "essay001 | \n", + "326 327 328 329 330 331 332 333 334 335 336 33... | \n", + "O O O O O O O O O O O O O O O | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O] | \n", + "
| 19 | \n", + "T11 | \n", + "Claim | \n", + "without the cooperation, there would be no vic... | \n", + "1927 | \n", + "1992 | \n", + "essay001 | \n", + "341 342 343 344 345 346 347 348 349 350 | \n", + "B-Claim I-Claim I-Claim I-Claim I-Claim I-Clai... | \n", + "[B-Claim, I-Claim, I-Claim, I-Claim, I-Claim, ... | \n", + "
| 20 | \n", + "O | \n", + "Other | \n", + ".\\nConsequently, no matter from the view of in... | \n", + "1992 | \n", + "2154 | \n", + "essay001 | \n", + "351 352 353 354 355 356 357 358 359 360 361 36... | \n", + "O O O O O O O O O O O O O O O O O O O O O O O O | \n", + "[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ... | \n", + "
| 21 | \n", + "T2 | \n", + "MajorClaim | \n", + "a more cooperative attitudes towards life is m... | \n", + "2154 | \n", + "2231 | \n", + "essay001 | \n", + "375 376 377 378 379 380 381 382 383 384 385 386 | \n", + "B-MajorClaim I-MajorClaim I-MajorClaim I-Major... | \n", + "[B-MajorClaim, I-MajorClaim, I-MajorClaim, I-M... | \n", + "
| 22 | \n", + "O | \n", + "Other | \n", + ". | \n", + "2231 | \n", + "2232 | \n", + "essay001 | \n", + "387 | \n", + "O | \n", + "[O] | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 0 1\n",
+ "0 1 2\n",
+ "1 3 4"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "54f2da94",
+ "metadata": {},
+ "source": [
+ "# Full Inference"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "ee10760f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{0: 'O',\n",
+ " 1: 'B-MajorClaim',\n",
+ " 2: 'I-MajorClaim',\n",
+ " 3: 'B-Claim',\n",
+ " 4: 'I-Claim',\n",
+ " 5: 'B-Premise',\n",
+ " 6: 'I-Premise'}"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "valloader.dataset.id_to_label"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "d41356f4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " MultiAccuracy_no_agg MultiAccuracy_agg MultiAccuracy_agg_reduced\n",
+ "0 0.515625 0.397933 0.397933\n",
+ "1 0.480469 0.291908 0.291908\n",
+ " tp class fn fp f1\n",
+ "0 0.0 0 27 2 0.0\n",
+ "1 0.0 1 4 0 0.0\n",
+ "2 0.0 2 6 0 0.0\n",
+ "3 0.0 3 15 0 0.0\n"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [72]\u001b[0m, in \u001b[0;36m| \n", + " | 0 | \n", + "1 | \n", + "
|---|---|---|
| 0 | \n", + "1 | \n", + "2 | \n", + "
| 1 | \n", + "3 | \n", + "4 | \n", + "
\n",
+ "
\n",
+ " "
+ ],
+ "text/plain": [
+ " id text \\\n",
+ "0 3321A3E87AD3 I do agree that some students would benefit fr... \n",
+ "1 DFEAEC512BAB Should students design a summer project for sc... \n",
+ "2 2E4AFCD3987F Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
+ "0 [I, do, agree, that, some, students, would, be... \n",
+ "1 [Should, students, design, a, summer, project,... \n",
+ "2 [Dear, State, Senator, ,, In, the, ruels, of, ... \n",
+ "3 [People, sometimes, have, a, different, opinio... \n",
+ "4 [Dear, senator,, As, you, know, the, Electoral... \n",
+ "\n",
+ " entities kfold \n",
+ "0 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 2 \n",
+ "1 [O, O, O, O, O, O, O, O, B-Position, I-Positio... 4 \n",
+ "2 [O, O, O, O, B-Position, I-Position, I-Positio... 0 \n",
+ "3 [B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... 3 \n",
+ "4 [O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... 1 "
+ ]
+ },
+ "metadata": {},
+ "execution_count": 13
+ }
+ ],
+ "source": [
+ "# Visualize preprocessing result:\n",
+ "parse_string = lambda x: [string[1:-1] for string in x[1:-1].split(', ')]\n",
+ "alltrain_texts.entities = alltrain_texts.entities.apply(parse_string)\n",
+ "alltrain_texts.text_split = alltrain_texts.text_split.apply(parse_string)\n",
+ "\n",
+ "alltrain_texts.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DRFI_KLMUxuN"
+ },
+ "source": [
+ "# Preparing cross validation\n",
+ "\n",
+ "Generate proper folds so that the essays we use in each fold have roughly the same number of discourse types overall.\n",
+ "Only compute if we don't have the file in directory already."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "id": "IOkXJ5UTUxuN"
+ },
+ "outputs": [],
+ "source": [
+ "if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \n",
+ " # Transform categorical labels to dummy variables. Group by id. Sum over dummy. \n",
+ " dfx = pd.get_dummies(df_alltrain, columns=[\"discourse_type\"]).groupby([\"id\"], as_index=False).sum()\n",
+ "\n",
+ " # Generate name for the dummy columns\n",
+ " dummy_cols = [c for c in dfx.columns if c.startswith(\"discourse_type_\") or c == \"id\" and c != \"discourse_type_num\"]\n",
+ " # dfx is now only the dataset with dummy columns selected: don't need to pass the data to do the splits\n",
+ " dfx = dfx[dummy_cols]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "a1AuTst8UxuN"
+ },
+ "outputs": [],
+ "source": [
+ "if not os.path.exists(f\"{HyperParameters.data_dir}/train_folds.csv\"): \n",
+ " # Generate cross validation object\n",
+ " mskf = MultilabelStratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+ "\n",
+ " # Extract labels\n",
+ " labels = [c for c in dfx.columns if c != \"id\"]\n",
+ " dfx_labels = dfx[labels]\n",
+ "\n",
+ " # Dummy kfold assignment\n",
+ " dfx[\"kfold\"] = -1\n",
+ "\n",
+ " # Split\n",
+ " for fold, (trn_, val_) in enumerate(mskf.split(dfx, dfx_labels)):\n",
+ " print(len(trn_), len(val_))\n",
+ " \n",
+ " # Change the value of the kfold column at the validation index to the value of the fold\n",
+ " # This will tell us when to use the current entry in the validation set\n",
+ " dfx.loc[val_, \"kfold\"] = fold\n",
+ "\n",
+ " # merge back to original dataframe\n",
+ " alltrain_texts = alltrain_texts.merge(dfx[[\"id\", \"kfold\"]], on=\"id\", how=\"left\")\n",
+ " print(alltrain_texts.kfold.value_counts())\n",
+ "\n",
+ " # Save so next time we import it directly\n",
+ " alltrain_texts.to_csv(f\"{HyperParameters.data_dir}/train_folds.csv\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "K7fcRANPUxuO"
+ },
+ "source": [
+ "# Model and Dataset classes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7gDGkTBtUxuO"
+ },
+ "source": [
+ "### Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "id": "APIkM7YwUxuO"
+ },
+ "outputs": [],
+ "source": [
+ "# need help with this\n",
+ "class FeedbackPrizeDataset(Dataset):\n",
+ " def __init__(self, dataframe, tokenizer, max_len, has_labels):\n",
+ " self.len = len(dataframe)\n",
+ " self.data = dataframe\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_len = max_len\n",
+ " self.has_labels = has_labels\n",
+ " \n",
+ " def __getitem__(self, index):\n",
+ " text = self.data.text[index]\n",
+ " encoding = self.tokenizer(\n",
+ " text.split(),\n",
+ " is_split_into_words = True,\n",
+ " padding = 'max_length',\n",
+ " truncation = True,\n",
+ " max_length = self.max_len\n",
+ " )\n",
+ " word_ids = encoding.word_ids()\n",
+ "\n",
+ " # targets\n",
+ " if self.has_labels:\n",
+ " word_labels = self.data.entities[index]\n",
+ " prev_word_idx = None\n",
+ " labels_ids = []\n",
+ " for word_idx in word_ids:\n",
+ " if word_idx is None:\n",
+ " labels_ids.append(IGNORE_INDEX)\n",
+ " elif word_idx != prev_word_idx:\n",
+ " labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\n",
+ " else:\n",
+ " if HyperParameters.label_subtokens:\n",
+ " labels_ids.append(LABELS_TO_IDS[word_labels[word_idx]])\n",
+ " else:\n",
+ " labels_ids.append(IGNORE_INDEX)\n",
+ " prev_word_idx = word_idx\n",
+ " encoding['labels'] = labels_ids\n",
+ " # convert to torch.tensor\n",
+ " item = {k: torch.as_tensor(v) for k, v in encoding.items()}\n",
+ " word_ids2 = [w if w is not None else NON_LABEL for w in word_ids]\n",
+ " item['word_ids'] = torch.as_tensor(word_ids2)\n",
+ " return item\n",
+ "\n",
+ " def __len__(self):\n",
+ " return self.len"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eE6pC1LdUxuP"
+ },
+ "source": [
+ "### Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "id": "X9UQQRt2UxuP"
+ },
+ "outputs": [],
+ "source": [
+ "class FeedbackModel(nn.Module):\n",
+ " def __init__(self):\n",
+ " super(FeedbackModel, self).__init__()\n",
+ " \n",
+ " # init config of transformer model of choice:\n",
+ " # NOTE: All hyperparameters of the transformer, INCLUDING THE SLIDING WINDOW, are accessible in here!\n",
+ " model_config = AutoConfig.from_pretrained(HyperParameters.model_name)\n",
+ " print(model_config)\n",
+ " self.backbone = AutoModel.from_pretrained(HyperParameters.model_name, config=model_config)\n",
+ " \n",
+ " # There's a paper on why this weird dropout strategy is beneficial: https://arxiv.org/abs/1905.09788\n",
+ " self.model_config = model_config\n",
+ " self.dropout1 = nn.Dropout(0.1)\n",
+ " self.dropout2 = nn.Dropout(0.2)\n",
+ " self.dropout3 = nn.Dropout(0.3)\n",
+ " self.dropout4 = nn.Dropout(0.4)\n",
+ " self.dropout5 = nn.Dropout(0.5)\n",
+ " self.head = nn.Linear(model_config.hidden_size, HyperParameters.num_labels)\n",
+ " \n",
+ " def forward(self, input_ids, mask):\n",
+ " x = self.backbone(input_ids, mask)\n",
+ " logits1 = self.head(self.dropout1(x[0]))\n",
+ " logits2 = self.head(self.dropout2(x[0]))\n",
+ " logits3 = self.head(self.dropout3(x[0]))\n",
+ " logits4 = self.head(self.dropout4(x[0]))\n",
+ " logits5 = self.head(self.dropout5(x[0]))\n",
+ " logits = (logits1 + logits2 + logits3 + logits4 + logits5) / 5\n",
+ " return logits"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "RslNIsyMUxuP"
+ },
+ "outputs": [],
+ "source": [
+ "def build_model_tokenizer():\n",
+ " tokenizer = AutoTokenizer.from_pretrained(HyperParameters.model_name, add_prefix_space = True)\n",
+ " model = FeedbackModel()\n",
+ " return model, tokenizer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PriptyGpUxuP"
+ },
+ "source": [
+ "### Utilities"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CmRQM_TPUxuQ"
+ },
+ "source": [
+ "What does this do?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "id": "_CJQ0r-yUxuQ"
+ },
+ "outputs": [],
+ "source": [
+ "# Need help with this: used in training to transform raw logits to labels needed\n",
+ "def active_logits(raw_logits, word_ids):\n",
+ " word_ids = word_ids.view(-1)\n",
+ " active_mask = word_ids.unsqueeze(1).expand(word_ids.shape[0], HyperParameters.num_labels)\n",
+ " active_mask = active_mask != NON_LABEL\n",
+ " active_logits = raw_logits.view(-1, HyperParameters.num_labels)\n",
+ " active_logits = torch.masked_select(active_logits, active_mask) # return 1dTensor\n",
+ " active_logits = active_logits.view(-1, HyperParameters.num_labels) \n",
+ " return active_logits\n",
+ "\n",
+ "def active_labels(labels):\n",
+ " active_mask = labels.view(-1) != IGNORE_INDEX\n",
+ " active_labels = torch.masked_select(labels.view(-1), active_mask)\n",
+ " return active_labels\n",
+ "\n",
+ "def active_preds_prob(active_logits):\n",
+ " active_preds = torch.argmax(active_logits, axis = 1)\n",
+ " active_preds_prob, _ = torch.max(active_logits, axis = 1)\n",
+ " return active_preds, active_preds_prob"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YkokQcdsUxuQ"
+ },
+ "source": [
+ "F1 scoring functions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "id": "SfhEUmMcUxuQ"
+ },
+ "outputs": [],
+ "source": [
+ "def calculate_overlap(set_pred, set_gt):\n",
+ " \"\"\"\n",
+ " Calculates if the overlap between prediction and\n",
+ " ground truth is enough fora potential True positive\n",
+ " \"\"\"\n",
+ " # Length of each and intersection\n",
+ " try:\n",
+ " len_gt = len(set_gt)\n",
+ " len_pred = len(set_pred)\n",
+ " inter = len(set_gt & set_pred)\n",
+ " overlap_1 = inter / len_gt\n",
+ " overlap_2 = inter/ len_pred\n",
+ " return overlap_1 >= 0.5 and overlap_2 >= 0.5\n",
+ " except: # at least one of the input is NaN\n",
+ " return False\n",
+ "\n",
+ "def score_feedback_comp_micro(pred_df, gt_df, discourse_type):\n",
+ " \"\"\"\n",
+ " A function that scores for the kaggle\n",
+ " Student Writing Competition\n",
+ " \n",
+ " Uses the steps in the evaluation page here:\n",
+ " https://www.kaggle.com/c/feedback-prize-2021/overview/evaluation\n",
+ " \"\"\"\n",
+ " gt_df = gt_df.loc[gt_df['discourse_type'] == discourse_type, \n",
+ " ['id', 'predictionstring']].reset_index(drop=True)\n",
+ " pred_df = pred_df.loc[pred_df['class'] == discourse_type,\n",
+ " ['id', 'predictionstring']].reset_index(drop=True)\n",
+ " pred_df['pred_id'] = pred_df.index\n",
+ " gt_df['gt_id'] = gt_df.index\n",
+ " pred_df['predictionstring'] = [set(pred.split(' ')) for pred in pred_df['predictionstring']]\n",
+ " gt_df['predictionstring'] = [set(pred.split(' ')) for pred in gt_df['predictionstring']]\n",
+ " \n",
+ " # Step 1. all ground truths and predictions for a given class are compared.\n",
+ " joined = pred_df.merge(gt_df,\n",
+ " left_on='id',\n",
+ " right_on='id',\n",
+ " how='outer',\n",
+ " suffixes=('_pred','_gt')\n",
+ " )\n",
+ " overlaps = [calculate_overlap(*args) for args in zip(joined.predictionstring_pred, \n",
+ " joined.predictionstring_gt)]\n",
+ " \n",
+ " # 2. If the overlap between the ground truth and prediction is >= 0.5, \n",
+ " # and the overlap between the prediction and the ground truth >= 0.5,\n",
+ " # the prediction is a match and considered a true positive.\n",
+ " # If multiple matches exist, the match with the highest pair of overlaps is taken.\n",
+ " # we don't need to compute the match to compute the score\n",
+ " TP = joined.loc[overlaps]['gt_id'].nunique()\n",
+ "\n",
+ " # 3. Any unmatched ground truths are false negatives\n",
+ " # and any unmatched predictions are false positives.\n",
+ " TPandFP = len(pred_df)\n",
+ " TPandFN = len(gt_df)\n",
+ " \n",
+ " #calc microf1\n",
+ " my_f1_score = 2*TP / (TPandFP + TPandFN)\n",
+ " return my_f1_score\n",
+ "\n",
+ "def score_feedback_comp(pred_df, gt_df, return_class_scores=False):\n",
+ " \"\"\"\n",
+ " Final helper function for model evaluation.\n",
+ " \n",
+ " Args:\n",
+ " pred_df (pandas.DataFrame): dataframe containing model predictions. Needs to have columns: ['id','class','predictionstring']\n",
+ " gt_df (pandas.DataFrame): dataframe of ground truth used for model training\n",
+ " return_class_scores (bool): Boolean indicating if we want to return the F1 score for each predicted class.\n",
+ " \n",
+ " Returns:\n",
+ " f1 (float): F1 score of the model\n",
+ " (optional) class_scores (dict): Dictionary of per-class F1 score\n",
+ " \"\"\"\n",
+ " class_scores = {}\n",
+ " for discourse_type in gt_df.discourse_type.unique():\n",
+ " class_score = score_feedback_comp_micro(pred_df, gt_df, discourse_type)\n",
+ " class_scores[discourse_type] = class_score\n",
+ " f1 = np.mean([v for v in class_scores.values()])\n",
+ " if return_class_scores:\n",
+ " return f1, class_scores\n",
+ " return f1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IIQdqtzoUxuR"
+ },
+ "source": [
+ "# Training and validation functions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7GrUCjcbUxuR"
+ },
+ "source": [
+ "### Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "id": "WSeGDvufUxuR"
+ },
+ "outputs": [],
+ "source": [
+ "def train_fn(model, train_data_loader, optimizer, epoch, criterion):\n",
+ " print(f'Training for epoch {epoch} started. GPU utilisation: {get_gpu_utilization()}')\n",
+ " model.train()\n",
+ " train_loss = 0\n",
+ " train_accuracy = 0\n",
+ " stream = tqdm(train_data_loader)\n",
+ " # Init gradscaler to ensure everything works smoothly on cuda\n",
+ " scaler = GradScaler()\n",
+ " print(f'GradScaler initialised. GPU utilisation: {get_gpu_utilization()}')\n",
+ " for batch_idx, batch in enumerate(stream, start = 1):\n",
+ " ids = batch['input_ids'].to(device, dtype = torch.long)\n",
+ " mask = batch['attention_mask'].to(device, dtype = torch.long)\n",
+ " raw_labels = batch['labels'].to(device, dtype = torch.long)\n",
+ " word_ids = batch['word_ids'].to(device, dtype = torch.long)\n",
+ " print(f'Tensors added to GPU. GPU utilisation: {get_gpu_utilization()}')\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " print(f'Optimizer Reset. GPU utilisation: {get_gpu_utilization()}')\n",
+ "\n",
+ " # Calculate output with autocast for cuda support\n",
+ " with autocast():\n",
+ " raw_logits = model(input_ids = ids, mask = mask)\n",
+ " print(f'Model trained. GPU utilisation: {get_gpu_utilization()}')\n",
+ "\n",
+ " #logits = active_logits(raw_logits, word_ids)\n",
+ " #labels = active_labels(raw_labels)\n",
+ "\n",
+ " logits = raw_logits\n",
+ " labels = raw_labels\n",
+ " sf_logits = torch.softmax(logits, dim=-1)\n",
+ " preds, preds_prob = active_preds_prob(sf_logits)\n",
+ " train_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\n",
+ " criterion = nn.CrossEntropyLoss()\n",
+ " loss = criterion(logits, labels)\n",
+ "\n",
+ " print(f'Loss calculated. GPU utilisation: {get_gpu_utilization()}')\n",
+ "\n",
+ " scaler.scale(loss).backward()\n",
+ " scaler.step(optimizer)\n",
+ " scaler.update()\n",
+ " train_loss += loss.item()\n",
+ " print(f'Optimization step occured. GPU utilisation: {get_gpu_utilization()}')\n",
+ "\n",
+ " \n",
+ " if batch_idx % HyperParameters.verbose_steps == 0:\n",
+ " loss_step = train_loss / batch_idx\n",
+ " print(f'Training loss after {batch_idx:04d} training steps: {loss_step}')\n",
+ "\n",
+ " if batch_idx == 3:\n",
+ " raise Exception()\n",
+ " \n",
+ " epoch_loss = train_loss / batch_idx\n",
+ " epoch_accuracy = train_accuracy / batch_idx \n",
+ " print_gpu_utilization()\n",
+ " # Cleanup\n",
+ " del train_data_loader, raw_logits, logits, raw_labels, preds, labels\n",
+ " torch.cuda.empty_cache()\n",
+ " gc.collect()\n",
+ " \n",
+ " print(f'epoch {epoch} - training loss: {epoch_loss:.4f}')\n",
+ " print(f'epoch {epoch} - training accuracy: {epoch_accuracy:.4f}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fRR3l8npUxuR"
+ },
+ "source": [
+ "### Validate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "Mj0dSxx0UxuR"
+ },
+ "outputs": [],
+ "source": [
+ "def valid_fn(model, df_val, df_val_eval, dl_val, epoch, criterion):\n",
+ " oof, valid_loss, valid_acc = get_preds_onefold(model, df_val, dl_val, criterion, valid_flg=True)\n",
+ " f1score =[]\n",
+ " # classes = oof['class'].unique()\n",
+ " classes = ['Lead', 'Position', 'Claim','Counterclaim', 'Rebuttal','Evidence','Concluding Statement']\n",
+ " print(f\"Validation F1 scores\")\n",
+ "\n",
+ " for c in classes:\n",
+ " pred_df = oof.loc[oof['class'] == c].copy()\n",
+ " gt_df = df_val_eval.loc[df_val_eval['discourse_type'] == c].copy()\n",
+ " f1 = score_feedback_comp(pred_df, gt_df)\n",
+ " print(f' * {c:<10}: {f1:4f}')\n",
+ " f1score.append(f1)\n",
+ " f1avg = np.mean(f1score)\n",
+ " print(f'Overall Validation avg F1: {f1avg:.4f} val_loss:{valid_loss:.4f} val_accuracy:{valid_acc:.4f}')\n",
+ " return valid_loss, oof"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xSZ3iq8dUxuS"
+ },
+ "source": [
+ "### Infer on validation data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "3qtgxPTlUxuS"
+ },
+ "outputs": [],
+ "source": [
+ "def inference(model, data_loader, criterion, valid_flg):\n",
+ " stream = tqdm(data_loader)\n",
+ " model.eval()\n",
+ " \n",
+ " valid_loss = 0\n",
+ " valid_accuracy = 0\n",
+ " all_logits = None\n",
+ " for batch_idx, batch in enumerate(stream, start = 1):\n",
+ " ids = batch['input_ids'].to(device, dtype = torch.long)\n",
+ " mask = batch['attention_mask'].to(device, dtype = torch.long)\n",
+ " with torch.no_grad():\n",
+ " raw_logits = model(input_ids=ids, mask = mask)\n",
+ " del ids, mask\n",
+ " \n",
+ " word_ids = batch['word_ids'].to(device, dtype = torch.long)\n",
+ " logits = active_logits(raw_logits, word_ids)\n",
+ " sf_logits = torch.softmax(logits, dim= -1)\n",
+ " sf_raw_logits = torch.softmax(raw_logits, dim=-1)\n",
+ " if valid_flg: \n",
+ " raw_labels = batch['labels'].to(device, dtype = torch.long)\n",
+ " labels = active_labels(raw_labels)\n",
+ " preds, preds_prob = active_preds_prob(sf_logits)\n",
+ " valid_accuracy += accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\n",
+ " loss = criterion(logits, labels)\n",
+ " valid_loss += loss.item()\n",
+ " \n",
+ " if batch_idx == 1:\n",
+ " all_logits = sf_raw_logits.cpu().numpy()\n",
+ " else:\n",
+ " all_logits = np.append(all_logits, sf_raw_logits.cpu().numpy(), axis=0)\n",
+ "\n",
+ " \n",
+ " if valid_flg: \n",
+ " epoch_loss = valid_loss / batch_idx\n",
+ " epoch_accuracy = valid_accuracy / batch_idx\n",
+ " else:\n",
+ " epoch_loss, epoch_accuracy = 0, 0\n",
+ " return all_logits, epoch_loss, epoch_accuracy\n",
+ "\n",
+ "\n",
+ "def preds_class_prob(all_logits, data_loader):\n",
+ " print(\"predict target class and its probabilty\")\n",
+ " final_predictions = []\n",
+ " final_predictions_score = []\n",
+ " stream = tqdm(data_loader)\n",
+ " len_sample = all_logits.shape[0]\n",
+ "\n",
+ " for batch_idx, batch in enumerate(stream, start=0):\n",
+ " for minibatch_idx in range(HyperParameters.valid_batch_size):\n",
+ " sample_idx = int(batch_idx * HyperParameters.valid_batch_size + minibatch_idx)\n",
+ " if sample_idx > len_sample - 1 : break\n",
+ " word_ids = batch['word_ids'][minibatch_idx].numpy()\n",
+ " predictions =[]\n",
+ " predictions_prob = []\n",
+ " pred_class_id = np.argmax(all_logits[sample_idx], axis=1)\n",
+ " pred_score = np.max(all_logits[sample_idx], axis=1)\n",
+ " pred_class_labels = [IDS_TO_LABELS[i] for i in pred_class_id]\n",
+ " prev_word_idx = -1\n",
+ " for idx, word_idx in enumerate(word_ids):\n",
+ " if word_idx == -1:\n",
+ " pass\n",
+ " elif word_idx != prev_word_idx:\n",
+ " predictions.append(pred_class_labels[idx])\n",
+ " predictions_prob.append(pred_score[idx])\n",
+ " prev_word_idx = word_idx\n",
+ " final_predictions.append(predictions)\n",
+ " final_predictions_score.append(predictions_prob)\n",
+ " return final_predictions, final_predictions_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "E5dGJEzDUxuS"
+ },
+ "outputs": [],
+ "source": [
+ "def get_preds_onefold(model, df, dl, criterion, valid_flg):\n",
+ " logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\n",
+ " all_preds, all_preds_prob = preds_class_prob(logits, dl)\n",
+ " df_pred = post_process_pred(df, all_preds, all_preds_prob)\n",
+ " return df_pred, valid_loss, valid_acc\n",
+ "\n",
+ "def get_preds_folds(model, df, dl, criterion, valid_flg=False):\n",
+ " for i_fold in range(HyperParameters.n_fold):\n",
+ " model_filename = os.path.join(HyperParameters.model_dir, f\"{HyperParameters.model_savename}_{i_fold}.bin\")\n",
+ " print(f\"{model_filename} inference\")\n",
+ " model = model.to(device)\n",
+ " model.load_state_dict(torch.load(model_filename))\n",
+ " logits, valid_loss, valid_acc = inference(model, dl, criterion, valid_flg)\n",
+ " if i_fold == 0:\n",
+ " avg_pred_logits = logits\n",
+ " else:\n",
+ " avg_pred_logits += logits\n",
+ " avg_pred_logits /= HyperParameters.n_fold\n",
+ " all_preds, all_preds_prob = preds_class_prob(avg_pred_logits, dl)\n",
+ " df_pred = post_process_pred(df, all_preds, all_preds_prob)\n",
+ " return df_pred\n",
+ "\n",
+ "def post_process_pred(df, all_preds, all_preds_prob):\n",
+ " final_preds = []\n",
+ " for i in range(len(df)):\n",
+ " idx = df.id.values[i]\n",
+ " pred = all_preds[i]\n",
+ " pred_prob = all_preds_prob[i]\n",
+ " j = 0\n",
+ " while j < len(pred):\n",
+ " cls = pred[j]\n",
+ " if cls == 'O': j += 1\n",
+ " else: cls = cls.replace('B', 'I')\n",
+ " end = j + 1\n",
+ " while end < len(pred) and pred[end] == cls:\n",
+ " end += 1\n",
+ " if cls != 'O' and cls !='':\n",
+ " avg_score = np.mean(pred_prob[j:end])\n",
+ " if end - j > MIN_THRESH[cls] and avg_score > PROB_THRESH[cls]:\n",
+ " final_preds.append((idx, cls.replace('I-', ''), ' '.join(map(str, list(range(j, end))))))\n",
+ " j = end\n",
+ " df_pred = pd.DataFrame(final_preds)\n",
+ " df_pred.columns = ['id', 'class', 'new_predictionstring']\n",
+ " return df_pred"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NAmJyKnHUxuS"
+ },
+ "source": [
+ "# Finally getting some action"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print_gpu_utilization()"
+ ],
+ "metadata": {
+ "id": "eNRB8mFsufoL",
+ "outputId": "52736f74-53aa-4d0f-f5e8-881d96bcc99d",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "GPU memory occupied: 3 MB.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def pretty_size(size):\n",
+ "\t\"\"\"Pretty prints a torch.Size object\"\"\"\n",
+ "\tassert(isinstance(size, torch.Size))\n",
+ "\treturn \" × \".join(map(str, size))\n",
+ "\n",
+ "def dump_tensors(gpu_only=True):\n",
+ "\t\"\"\"Prints a list of the Tensors being tracked by the garbage collector.\"\"\"\n",
+ "\timport gc\n",
+ "\ttotal_size = 0\n",
+ "\tfor obj in gc.get_objects():\n",
+ "\t\ttry:\n",
+ "\t\t\tif torch.is_tensor(obj):\n",
+ "\t\t\t\tif not gpu_only or obj.is_cuda:\n",
+ "\t\t\t\t\tprint(\"%s:%s%s %s\" % (type(obj).__name__, \n",
+ "\t\t\t\t\t\t\t\t\t\t \" GPU\" if obj.is_cuda else \"\",\n",
+ "\t\t\t\t\t\t\t\t\t\t \" pinned\" if obj.is_pinned else \"\",\n",
+ "\t\t\t\t\t\t\t\t\t\t pretty_size(obj.size())))\n",
+ "\t\t\t\t\ttotal_size += obj.numel()\n",
+ "\t\t\telif hasattr(obj, \"data\") and torch.is_tensor(obj.data):\n",
+ "\t\t\t\tif not gpu_only or obj.is_cuda:\n",
+ "\t\t\t\t\tprint(\"%s → %s:%s%s%s%s %s\" % (type(obj).__name__, \n",
+ "\t\t\t\t\t\t\t\t\t\t\t\t type(obj.data).__name__, \n",
+ "\t\t\t\t\t\t\t\t\t\t\t\t \" GPU\" if obj.is_cuda else \"\",\n",
+ "\t\t\t\t\t\t\t\t\t\t\t\t \" pinned\" if obj.data.is_pinned else \"\",\n",
+ "\t\t\t\t\t\t\t\t\t\t\t\t \" grad\" if obj.requires_grad else \"\", \n",
+ "\t\t\t\t\t\t\t\t\t\t\t\t \" volatile\" if obj.volatile else \"\",\n",
+ "\t\t\t\t\t\t\t\t\t\t\t\t pretty_size(obj.data.size())))\n",
+ "\t\t\t\t\ttotal_size += obj.data.numel()\n",
+ "\t\texcept Exception as e:\n",
+ "\t\t\tpass \n",
+ "\tprint(\"Total size:\", total_size)"
+ ],
+ "metadata": {
+ "id": "WVHmI_NzvTPy"
+ },
+ "execution_count": 26,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dump_tensors()"
+ ],
+ "metadata": {
+ "id": "U-gD0UN7vVMu",
+ "outputId": "02a1068e-c816-487d-bcd3-a47b02e4f188",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "execution_count": 27,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: DeprecationWarning: astor.all_symbols is deprecated. Please use astor.symbol_data.\n",
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: DeprecationWarning: astor.treewalk is deprecated. Please use astor.tree_walk.\n",
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:19: DeprecationWarning: astor.codegen is deprecated. Please use astor.code_gen.\n",
+ "/usr/local/lib/python3.7/dist-packages/torch/distributed/distributed_c10d.py:171: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead\n",
+ " \"torch.distributed.reduce_op is deprecated, please use \"\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Total size: 0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "class KaggleDataset(Dataset):\n",
+ " \"\"\"\n",
+ " Class for loading data in batches after it has been processed\n",
+ " \"\"\"\n",
+ " def __init__(self, dataframe, tokenizer, max_length):\n",
+ "\n",
+ " super().__init__()\n",
+ "\n",
+ " # -- prepare data\n",
+ " assert sorted(dataframe.columns) == ['labels', 'text'], f\"Please make sure input dataframe has the columns (text, labels)\"\n",
+ " # data must be in the correct format\n",
+ " self.inputs = dataframe.text.values\n",
+ " self.targets = dataframe.labels.values\n",
+ " #if not is_string_dtype(self.inputs): raise TypeError('Text data must be string type')\n",
+ " # TODO assertion below is bug; not deleting so remember to add correct assertions\n",
+ " #if not is_integer_dtype(self.targets): raise TypeError('Label data must be integer type')\n",
+ "\n",
+ " # -- prepare tokenizer\n",
+ " self.tokenizer = tokenizer\n",
+ " self.max_length = max_length\n",
+ "\n",
+ " def __len__(self):\n",
+ " return len(self.inputs)\n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " # self.inputs anf self.targets must be of a type that is indexible as shown\n",
+ " inputs = self.inputs[index]\n",
+ " targets = self.targets[index]\n",
+ "\n",
+ " inputs = self.tokenizer(\n",
+ " # consider parametrising these\n",
+ " inputs.split(),\n",
+ " is_split_into_words=True, # this means that extra \\n should be ignored\n",
+ " padding='max_length',\n",
+ " truncation=True,\n",
+ " max_length=self.max_length\n",
+ " )\n",
+ "\n",
+ " word_ids = inputs.word_ids()\n",
+ " word_id_mask = [word_id is not None for word_id in word_ids]\n",
+ " word_ids = [word_id for word_id in word_ids if word_id is not None]\n",
+ "\n",
+ " inputs = {\n",
+ " key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()\n",
+ " }\n",
+ " targets = torch.as_tensor(targets, dtype=torch.long)\n",
+ " expanded_targets = torch.zeros(self.max_length, dtype=torch.long)\n",
+ " expanded_targets[word_id_mask] = targets[word_ids]\n",
+ " inputs['labels'] = expanded_targets\n",
+ " inputs['word_ids'] = torch.tensor(word_ids, dtype=torch.long)\n",
+ " return inputs\n",
+ "\n",
+ " "
+ ],
+ "metadata": {
+ "id": "-bNSOZ2t01cS"
+ },
+ "execution_count": 35,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "id": "7dHGvjpPUxuS",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
+ "referenced_widgets": [
+ "eef7a90afeb44c448532e10c8ef5a20e",
+ "ca3198d71a3f4887874da99cc3e78882",
+ "c4d33c2a02644f85a146f390d6aa0736",
+ "9d549d2487dd4197a978761bd72be2ad",
+ "ec2dd4649f114c6fad2430c245fe68aa",
+ "81cfe2eb173e40fea93d20dc2f171f03",
+ "433b7e11f87442198edef448a8c16074",
+ "22c91851758d425383c6653f877dbc1b",
+ "25ffd2bde996468a9c003a1883191813",
+ "45645e8eb5af4fca8c6a84fa3b99dab3",
+ "d81a205322c84e7e8830374196e00a36"
+ ]
+ },
+ "outputId": "c2bc7157-8d6d-44fd-f2c4-5bb29bf72079"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[', line 32, code train_fn>, {'model': FeedbackModel(\n",
+ " (backbone): LongformerModel(\n",
+ " (embeddings): LongformerEmbeddings(\n",
+ " (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
+ " (position_embeddings): Embedding(4098, 768, padding_idx=1)\n",
+ " (token_type_embeddings): Embedding(1, 768)\n",
+ " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " (encoder): LongformerEncoder(\n",
+ " (layer): ModuleList(\n",
+ " (0): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
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+ " )\n",
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+ " (attention): LongformerAttention(\n",
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+ " )\n",
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+ " (4): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
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+ " )\n",
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+ " (7): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
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+ " )\n",
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+ " (8): LongformerLayer(\n",
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+ " (10): LongformerLayer(\n",
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+ " )\n",
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+ " (11): LongformerLayer(\n",
+ " (attention): LongformerAttention(\n",
+ " (self): LongformerSelfAttention(\n",
+ " (query): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (query_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (key_global): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (value_global): Linear(in_features=768, out_features=768, bias=True)\n",
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+ " (dropout): Dropout(p=0.1, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (pooler): LongformerPooler(\n",
+ " (dense): Linear(in_features=768, out_features=768, bias=True)\n",
+ " (activation): Tanh()\n",
+ " )\n",
+ " )\n",
+ " (dropout1): Dropout(p=0.1, inplace=False)\n",
+ " (dropout2): Dropout(p=0.2, inplace=False)\n",
+ " (dropout3): Dropout(p=0.3, inplace=False)\n",
+ " (dropout4): Dropout(p=0.4, inplace=False)\n",
+ " (dropout5): Dropout(p=0.5, inplace=False)\n",
+ " (head): Linear(in_features=768, out_features=15, bias=True)\n",
+ "), 'train_data_loader': \n",
+ "
\n",
+ " \n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " | \n", + " | id | \n", + "text | \n", + "text_split | \n", + "entities | \n", + "kfold | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "3321A3E87AD3 | \n", + "I do agree that some students would benefit fr... | \n", + "[I, do, agree, that, some, students, would, be... | \n", + "[B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... | \n", + "2 | \n", + "
| 1 | \n", + "DFEAEC512BAB | \n", + "Should students design a summer project for sc... | \n", + "[Should, students, design, a, summer, project,... | \n", + "[O, O, O, O, O, O, O, O, B-Position, I-Positio... | \n", + "4 | \n", + "
| 2 | \n", + "2E4AFCD3987F | \n", + "Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... | \n", + "[Dear, State, Senator, ,, In, the, ruels, of, ... | \n", + "[O, O, O, O, B-Position, I-Position, I-Positio... | \n", + "0 | \n", + "
| 3 | \n", + "EB6C2AF20BFE | \n", + "People sometimes have a different opinion than... | \n", + "[People, sometimes, have, a, different, opinio... | \n", + "[B-Lead, I-Lead, I-Lead, I-Lead, I-Lead, I-Lea... | \n", + "3 | \n", + "
| 4 | \n", + "A91A08E523D5 | \n", + "Dear senator,\\n\\nAs you know the Electoral Col... | \n", + "[Dear, senator,, As, you, know, the, Electoral... | \n", + "[O, O, B-Lead, I-Lead, I-Lead, I-Lead, I-Lead,... | \n", + "1 | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "C7n9O8Rxn2X2",
+ "outputId": "58589851-2595-4857-8617-aecc0001565c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting colab-dev-tools\n",
+ " Downloading colab_dev_tools-0.0.7-py3-none-any.whl (3.7 kB)\n",
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from colab-dev-tools) (1.21.5)\n",
+ "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from colab-dev-tools) (1.10.0+cu111)\n",
+ "Requirement already satisfied: nvidia-ml-py3 in /usr/local/lib/python3.7/dist-packages (from colab-dev-tools) (7.352.0)\n",
+ "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->colab-dev-tools) (4.1.1)\n",
+ "Installing collected packages: colab-dev-tools\n",
+ "Successfully installed colab-dev-tools-0.0.7\n",
+ "Google Drive import successful.\n",
+ "CUDA device detected. Using GPU...\n",
+ "Mounted at /content/drive\n",
+ "Google Drive mount successful.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "!python3.7 -m pip install colab-dev-tools\n",
+ "from colabtools.utils import get_gpu_utilization, mount_drive, install_private_library\n",
+ "\n",
+ "drive_path = mount_drive()\n",
+ "project_path = os.path.join(drive_path, 'argument-mining')\n",
+ "development_dir = os.path.join(drive_path, 'argument-mining/argminer')\n",
+ "\n",
+ "install_private_library(os.path.join(project_path, 'data/github_config.json'), 'argument-mining')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# -*- coding: utf-8 -*-\n",
+ "\"\"\"This is imported from End_to_end_GPU.ipynb\n",
+ "\n",
+ "Automatically generated by Colaboratory.\n",
+ "\n",
+ "Original file is located at\n",
+ " https://colab.research.google.com/github/namiyousef/argument-mining/blob/develop/experiments/changmao/End-to-end_GPU_v1.ipynb\n",
+ "\n",
+ "# End-to-end\n",
+ "\n",
+ "This notebook should form the core skeleton of the 'run' function\n",
+ "\n",
+ "## Colab Set up\n",
+ "\"\"\"\n",
+ "# -- public imports\n",
+ "\n",
+ "# base\n",
+ "import os\n",
+ "import gc\n",
+ "import json\n",
+ "import time\n",
+ "import warnings\n",
+ "\n",
+ "# pytorch\n",
+ "import torch\n",
+ "from torch.utils.data import DataLoader\n",
+ "\n",
+ "# huggingface\n",
+ "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
+ "\n",
+ "# other libs\n",
+ "import pandas as pd\n",
+ "from pandas.testing import assert_frame_equal\n",
+ "\n",
+ "import plac\n",
+ "\n",
+ "\n",
+ "# -- private imports\n",
+ "\n",
+ "# ml-utils\n",
+ "from mlutils.torchtools.metrics import FScore\n",
+ "\n",
+ "# colab-dev-tools\n",
+ "from colabtools.utils import move_to_device, get_gpu_utilization\n",
+ "from colabtools.config import DEVICE\n",
+ "\n",
+ "# argminer\n",
+ "from argminer.data import ArgumentMiningDataset, TUDarmstadtProcessor, PersuadeProcessor\n",
+ "from argminer.evaluation import inference\n",
+ "from argminer.utils import encode_model_name\n",
+ "from argminer.config import LABELS_MAP_DICT, MAX_NORM\n",
+ "\n",
+ "\n",
+ "# -- globals\n",
+ "TEST_SIZE = 0.3\n",
+ "\n",
+ "# -- define plac inputs\n",
+ "@plac.annotations(\n",
+ " dataset=(\"name of dataset to use\", \"positional\", None, str),\n",
+ " strategy=(\"name of training strategy to use in form {level}_{labellin scheme}\", \"positional\", None, str),\n",
+ " model_name=(\"name of HuggingFace model to use\", \"positional\", None, str),\n",
+ " max_length=(\"max number of tokens\", \"positional\", None, int),\n",
+ " test_size=(\"test size to use (as a percentage of entire dataset)\", \"option\", None, float),\n",
+ " batch_size=(\"Batch size for training\", \"option\", \"b\", int),\n",
+ " epochs=(\"Number of epochs to train for\", \"option\", \"e\", int),\n",
+ " save_freq=(\"How frequently to save model, in epochs\", \"option\", None, int),\n",
+ " verbose=(\"Set model verbosity\", \"option\", None, int),\n",
+ " run_inference=(\"Flag to run inference or not\", \"option\", 'i', bool)\n",
+ ")\n",
+ "def main(dataset, strategy, model_name, max_length, test_size, batch_size, epochs, save_freq, verbose, run_inference):\n",
+ " \"\"\"\n",
+ " Function to run models as a script. This function expects the following directory structure:\n",
+ " - cwd (usually $TMPDIR on cluster)\n",
+ " - df_{dataset}_postprocessed.json\n",
+ " - train-test-split.csv (for TUDarmstadt)\n",
+ " :param dataset:\n",
+ " :param strategy:\n",
+ " :param model_name:\n",
+ " :param max_length:\n",
+ " :param batch_size:\n",
+ " :param epochs:\n",
+ " :param save_freq:\n",
+ " :param verbose:\n",
+ " :return:\n",
+ " \"\"\"\n",
+ " # TODO: imports sklearn to check f1 score\n",
+ " from sklearn.metrics import f1_score\n",
+ " # configs\n",
+ " strat_name, strat_label = strategy.split('_')\n",
+ " df_label_map = LABELS_MAP_DICT[dataset][strat_label]\n",
+ " num_labels = len(set(df_label_map.label))\n",
+ "\n",
+ " # get data processor and train_test_splits\n",
+ " try:\n",
+ " # TODO change made here to fix paths\n",
+ " path = os.path.join(project_path, f'data/{strategy.split(\"_\")[1]}')\n",
+ " processor = TUDarmstadtProcessor(path) if dataset == 'TUDarmstadt' else PersuadeProcessor(path)\n",
+ " processor = processor.from_json()\n",
+ " if dataset == 'TUDarmstadt':\n",
+ " if test_size == 0:\n",
+ " warnings.warn(\n",
+ " f\"Detected test_size={test_size}. \"\n",
+ " f\"No splits being made. \"\n",
+ " f\"Train and test data treated identicallu.\",\n",
+ " UserWarning, stacklevel=2\n",
+ " )\n",
+ " df_total = processor.dataframe\n",
+ " df_dict = {\n",
+ " 'train': df_total,\n",
+ " 'test': df_total\n",
+ " }\n",
+ " else:\n",
+ " df_dict = processor.get_tts(test_size=test_size)\n",
+ " else:\n",
+ " df_dict = processor.get_tts(test_size=test_size)\n",
+ "\n",
+ " df_train = df_dict.get('train')[['text', 'labels']] # TODO standardise this, so that you don't need to apply this..\n",
+ " df_test = df_dict.get('test')[['text', 'labels']]\n",
+ "\n",
+ " except Exception as e: # TODO granularise errors\n",
+ " raise Exception(f'Error occurred during data processing and splitting. Full logs: {e}')\n",
+ "\n",
+ " # get tokenizer, model and optimizer\n",
+ " try:\n",
+ " tokenizer = AutoTokenizer.from_pretrained(model_name, add_prefix_space=True)\n",
+ " model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=num_labels)\n",
+ " # TODO force_download\n",
+ " # TODO add option for optimizer\n",
+ " optimizer = torch.optim.Adam(params=model.parameters())\n",
+ " except Exception as e:\n",
+ " raise Exception(f'Error from tokenizer and model loading. Full logs {e}')\n",
+ "\n",
+ " try:\n",
+ " train_set = ArgumentMiningDataset(df_label_map, df_train, tokenizer, max_length, strategy)\n",
+ " test_set = ArgumentMiningDataset(df_label_map, df_test, tokenizer, max_length, strategy, is_train=False)\n",
+ "\n",
+ " # TODO delete this\n",
+ " all_ids = set()\n",
+ " for label in df_train['labels'].values:\n",
+ " all_ids = all_ids.union(set(label))\n",
+ "\n",
+ " # TODO delete above\n",
+ " train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n",
+ " test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True)\n",
+ " except Exception as e:\n",
+ " raise Exception(f'Error occured during dataset/loader creation. Full logs {e}')\n",
+ "\n",
+ " if not os.path.exists('models'):\n",
+ " os.makedirs('models')\n",
+ " print('models directory created!')\n",
+ "\n",
+ "\n",
+ " metrics = [FScore(average='macro')]\n",
+ " scores = {\n",
+ " 'scores': {\n",
+ " metric.__class__.__name__: [] for metric in metrics\n",
+ " },\n",
+ " 'epoch_scores': {\n",
+ " metric.__class__.__name__: [] for metric in metrics\n",
+ " },\n",
+ " 'epoch_batch_ids': {\n",
+ " metric.__class__.__name__: [] for metric in metrics\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " model.to(DEVICE)\n",
+ " print(f'Model pushed to device: {DEVICE}')\n",
+ " for epoch in range(epochs):\n",
+ " model.train()\n",
+ " start_epoch_message = f'EPOCH {epoch + 1} STARTED'\n",
+ " print(start_epoch_message)\n",
+ " print(f'{\"-\" * len(start_epoch_message)}')\n",
+ " start_epoch = time.time()\n",
+ "\n",
+ " start_load = time.time()\n",
+ " training_loss = 0\n",
+ " for i, (inputs, targets) in enumerate(train_loader):\n",
+ " start_train = time.time()\n",
+ " inputs = move_to_device(inputs, DEVICE)\n",
+ " targets = move_to_device(targets, DEVICE)\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ "\n",
+ " loss, outputs = model(\n",
+ " labels=targets,\n",
+ " input_ids=inputs['input_ids'],\n",
+ " attention_mask=inputs['attention_mask'],\n",
+ " return_dict=False\n",
+ " )\n",
+ " gpu_util = 'NO GPU' if DEVICE == 'cpu' else get_gpu_utilization()\n",
+ " training_loss += loss.item()\n",
+ "\n",
+ " # backward pass\n",
+ "\n",
+ " torch.nn.utils.clip_grad_norm_(\n",
+ " parameters=model.parameters(), max_norm=MAX_NORM\n",
+ " )\n",
+ "\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " # metrics\n",
+ " for metric in metrics:\n",
+ " score = metric(outputs, targets)\n",
+ " scores['scores'][metric.__class__.__name__].append(score.item())\n",
+ "\n",
+ " # TODO delete\n",
+ " targets = targets.flatten()\n",
+ " ignore_ids = targets == -100\n",
+ " outputs = torch.argmax(outputs, dim=-1).flatten()[~ignore_ids].cpu().tolist()\n",
+ " targets = targets[~ignore_ids].cpu().tolist()\n",
+ " print('OUTPUTS: ', outputs)\n",
+ " print('TARGETS: ', targets)\n",
+ " sklearn_score = f1_score(targets, outputs, average='macro')\n",
+ " del targets, inputs, loss, outputs\n",
+ " gc.collect()\n",
+ " torch.cuda.empty_cache()\n",
+ "\n",
+ " end_train = time.time()\n",
+ "\n",
+ " if verbose > 1:\n",
+ " print(\n",
+ " f'Batch {i + 1} complete. '\n",
+ " f'Time taken: load({start_train - start_load:.3g}),'\n",
+ " f'train({end_train - start_train:.3g}),'\n",
+ " f'total({end_train - start_load:.3g}). '\n",
+ " f'GPU util. after train: {gpu_util}. '\n",
+ " f'Metrics: {\" \".join([f\"{metric_name}({score_list[-1]:.3g})\" for metric_name, score_list in scores[\"scores\"].items()])}. '\n",
+ " f'F1 from sklearn: {sklearn_score}' # TODO delete this\n",
+ " )\n",
+ " start_load = time.time()\n",
+ "\n",
+ " for metric in metrics:\n",
+ " score = scores['scores'][metric.__class__.__name__][:i+1]\n",
+ " avg_score = sum(score)/len(score)\n",
+ " scores['epoch_scores'][metric.__class__.__name__].append(avg_score)\n",
+ " scores['epoch_batch_ids'][metric.__class__.__name__].append(i)\n",
+ "\n",
+ " print_message = f'Epoch {epoch + 1}/{epochs} complete. ' \\\n",
+ " f'Time taken: {start_load - start_epoch:.3g}. ' \\\n",
+ " f'Loss: {training_loss/(i+1): .3g}. ' \\\n",
+ " f'Metrics: {\" \".join([f\"{metric_name}({score_list[-1]:.3g})\" for metric_name, score_list in scores[\"epoch_scores\"].items()])}'\n",
+ "\n",
+ " if verbose:\n",
+ " print(f'{\"-\" * len(print_message)}')\n",
+ " print(print_message)\n",
+ " print(f'{\"-\" * len(print_message)}')\n",
+ "\n",
+ "\n",
+ " if epoch % save_freq == 0:\n",
+ " encoded_model_name = encode_model_name(model_name, epoch+1)\n",
+ " save_path = f'models/{encoded_model_name}'\n",
+ " model.save_pretrained(save_path)\n",
+ " print(f'Model saved at epoch {epoch+1} at: {save_path}')\n",
+ "\n",
+ " encoded_model_name = encode_model_name(model_name, 'final')\n",
+ " save_path = f'models/{encoded_model_name}'\n",
+ " model.save_pretrained(save_path)\n",
+ " print(f'Model saved at epoch {epoch + 1} at: {save_path}')\n",
+ "\n",
+ " with open('training_scores.json', 'w') as f:\n",
+ " json.dump(scores, f)\n",
+ " print('Saved scores.')\n",
+ "\n",
+ " # load trained model\n",
+ " if run_inference:\n",
+ " trained_model = AutoModelForTokenClassification.from_pretrained(save_path)\n",
+ " df_metrics, df_scores = inference(trained_model, test_loader)\n",
+ " df_scores.to_json('scores.json')"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JmKpZhZen7ju",
+ "outputId": "286f2d11-24bc-4ba0-dd23-0d8b3fdda0c3"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Import of ArgMiner successful\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "main(\n",
+ " dataset=\"Persuade\",\n",
+ " strategy='standard_bieo',\n",
+ " model_name=\"google/bigbird-roberta-base\",\n",
+ " max_length=1024,\n",
+ " test_size=0.3,\n",
+ " batch_size=2,\n",
+ " epochs=5,\n",
+ " save_freq=1,\n",
+ " verbose=2,\n",
+ " run_inference=1,\n",
+ ")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
+ "referenced_widgets": [
+ "46fc8eb6ce7a4ed68a9b26b8a1056f06",
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+ "d102f545c14548498742dfdef94dc2ea",
+ "b2c9b97d48f3437eb27a3829a0223dfa"
+ ]
+ },
+ "id": "K9kQZrgSoVPe",
+ "outputId": "85af58ef-ccaf-443c-cc5f-5b135cb4cc6e"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading: 0%| | 0.00/0.99k [00:00\u001b[0;36m\u001b[0m\n\u001b[0;31m from python_files.data import create_labels_doc_level #, KaggleDataset\u001b[0m\n",
+ "\u001b[0;36m File \u001b[0;32m~/Desktop/Main/0.Education/2.UCL/Courses/NLP/argument-mining/experiments/yousef/python_files/data.py:115\u001b[0;36m\u001b[0m\n\u001b[0;31m pass\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- dev imports\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "from python_files.data import create_labels_doc_level #, KaggleDataset\n",
+ "from python_files.run import train_longformer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50a80de8",
+ "metadata": {},
+ "source": [
+ "## Model Configuration"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e459e0d",
+ "metadata": {},
+ "source": [
+ "### Tokenizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "e00fe9f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n",
+ "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = 'google/bigbird-roberta-large'\n",
+ "\n",
+ "tokenizer = BigBirdTokenizer.from_pretrained(model)\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model, add_prefix_space=True)\n",
+ "# def need an encoding function to categoize the inputs or at least to release them, maybe need to store them together tbh?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "f9061003",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[None, 0, 1, None]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenizer('humans humans').word_ids()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1fd05899",
+ "metadata": {},
+ "source": [
+ "### Model and Optmizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "5c5fc460",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of the model checkpoint at google/bigbird-roberta-large were not used when initializing BigBirdForTokenClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight']\n",
+ "- This IS expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of BigBirdForTokenClassification were not initialized from the model checkpoint at google/bigbird-roberta-large and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "config_model = AutoConfig.from_pretrained(model) \n",
+ "config_model.num_labels = 3 # might have to change if we're using the numbered discourse type as well\n",
+ "#config_model.save_pretrained('model')\n",
+ "\n",
+ "model = AutoModelForTokenClassification.from_pretrained(model, config=config_model)\n",
+ "optimizer = torch.optim.Adam(params=model.parameters())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4c4f0c3",
+ "metadata": {},
+ "source": [
+ "## Data Preparation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "id": "990172d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "texts = [\n",
+ " 'Hi my name is Yousef Nami',\n",
+ " 'Hi I am Shirin'\n",
+ " ]\n",
+ "labels = [\n",
+ " ['O', 'O', 'O', 'O', 'B-PERS', 'I-PERS'],\n",
+ " ['O', 'O', 'O', 'B-PERS']\n",
+ "]\n",
+ "\n",
+ "# TODO see if bert can accept text inputs?\n",
+ "labels_numeric = [\n",
+ " [0, 0, 0, 0, 1, 2],\n",
+ " [0, 0, 0, 1]\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "id": "9c3bb28e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame({\n",
+ " 'text': texts,\n",
+ " 'labels': labels_numeric\n",
+ " })"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "id": "ad91646e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset = KaggleDataset(\n",
+ " df,\n",
+ " tokenizer,\n",
+ " max_length=20\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "id": "6db005dd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'input_ids': tensor([ 65, 16003, 717, 1539, 419, 676, 617, 992, 500, 6378,\n",
+ " 66, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'doc_ids': 0, 'word_ids': [0, 1, 2, 3, 4, 4, 4, 5, 5]}\n",
+ "{'input_ids': tensor([ 65, 16003, 415, 817, 1012, 47489, 66, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'doc_ids': 1, 'word_ids': [0, 1, 2, 3, 3]}\n"
+ ]
+ }
+ ],
+ "source": [
+ "for (inputs, targets) in dataset:\n",
+ " print(inputs)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6e583f70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"You sef Na mi\" -> \"BNAME, BNAME, INAME, INAME\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29c86ed8",
+ "metadata": {},
+ "source": [
+ "## Data Training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "6564d266",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'dataset' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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+ "\n",
+ "\n",
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+ "3 EB6C2AF20BFE People sometimes have a different opinion than... \n",
+ "4 A91A08E523D5 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " text_split \\\n",
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+ "4 [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1... \n",
+ "\n",
+ " labels_temp \n",
+ "0 [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... \n",
+ "1 [3, 4, 4, 4, 4, 4, 4, 7, 8, 8, 8, 8, 8, 8, 8, ... \n",
+ "2 [3, 4, 4, 4, 4, 4, 4, 4, 4, 7, 8, 8, 8, 8, 8, ... \n",
+ "3 [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... \n",
+ "4 [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... "
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "PATH = '../../data/kaggle/feedback-prize-2021/train'\n",
+ "FILE_PATH = '../../data/kaggle/feedback-prize-2021/train.csv'\n",
+ "\n",
+ "df_texts = create_labels_doc_level(PATH, FILE_PATH)\n",
+ "#df_texts[['text', 'labels']].to_csv('../../../data/kaggle/df_cleaned.csv')\n",
+ "\n",
+ "#df_kaggle = pd.read_csv('../../data/kaggle/df_cleaned.csv')\n",
+ "#df_kaggle.head()\n",
+ "df_texts.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "38591aa3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "text | \n", + "text_split | \n", + "labels | \n", + "range | \n", + "labels_temp | \n", + "
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| 4 | \n", + "A91A08E523D5 | \n", + "Dear senator,\\n\\nAs you know the Electoral Col... | \n", + "[Dear, senator,, As, you, know, the, Electoral... | \n", + "[0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1... | \n", + "[1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "
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+ ],
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+ " text \\\n",
+ "0 I do agree that some students would benefit fr... \n",
+ "1 Should students design a summer project for sc... \n",
+ "2 Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... \n",
+ "3 People sometimes have a different opinion than... \n",
+ "4 Dear senator,\\n\\nAs you know the Electoral Col... \n",
+ "\n",
+ " labels \n",
+ "0 [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... \n",
+ "1 [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, ... \n",
+ "2 [0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 7, 8, ... \n",
+ "3 [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... \n",
+ "4 [0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... "
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_kaggle = df_texts[['text', 'labels']]\n",
+ "df_kaggle.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "id": "01b42959",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test =pd.DataFrame(data={'text':['hi my name is Yousef'], 'labels': [1]})\n",
+ "\n",
+ "kaggle_dataset = KaggleDataset(df_test, tokenizer, max_length=20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "id": "3429cf53",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for (inputs, targets) in kaggle_dataset:\n",
+ " print(inputs)\n",
+ " print(targets)\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "id": "fd5b1225",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "IndexError",
+ "evalue": "too many indices for tensor of dimension 0",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [153]\u001b[0m, in \u001b[0;36m| \n", + " | text | \n", + "labels | \n", + "
|---|---|---|
| 0 | \n", + "I do agree that some students would benefit fr... | \n", + "[1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "
| 1 | \n", + "Should students design a summer project for sc... | \n", + "[0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, ... | \n", + "
| 2 | \n", + "Dear State Senator\\n\\n,\\n\\nIn the ruels of vot... | \n", + "[0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 7, 8, ... | \n", + "
| 3 | \n", + "People sometimes have a different opinion than... | \n", + "[1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "
| 4 | \n", + "Dear senator,\\n\\nAs you know the Electoral Col... | \n", + "[0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ... | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
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+ ],
+ "text/plain": [
+ " id probas values\n",
+ "0 0 6 0\n",
+ "1 0 5 1\n",
+ "2 0 9 1\n",
+ "3 0 6 0\n",
+ "0 1 8 0\n",
+ "1 1 7 1\n",
+ "2 1 9 2\n",
+ "3 1 9 1"
+ ]
+ },
+ "execution_count": 161,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 164,
+ "id": "5e953688",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.concat(\n",
+ " [pd.DataFrame(data={'probas':probas, 'values':values, 'word_ids':[0,1,1,2]}).groupby('word_ids').head(1) for i, (probas, values) in enumerate(zip(probas, values))]\n",
+ ")\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 166,
+ "id": "7b0bb388",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CUDA device NOT detected. Using CPU...\n"
+ ]
+ }
+ ],
+ "source": [
+ "df['ids'] = [0,0,0,1,1,1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 193,
+ "id": "9d3d0925",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "probas | \n", + "values | \n", + "
|---|---|---|---|
| 0 | \n", + "0 | \n", + "6 | \n", + "0 | \n", + "
| 1 | \n", + "0 | \n", + "5 | \n", + "1 | \n", + "
| 2 | \n", + "0 | \n", + "9 | \n", + "1 | \n", + "
| 3 | \n", + "0 | \n", + "6 | \n", + "0 | \n", + "
| 0 | \n", + "1 | \n", + "8 | \n", + "0 | \n", + "
| 1 | \n", + "1 | \n", + "7 | \n", + "1 | \n", + "
| 2 | \n", + "1 | \n", + "9 | \n", + "2 | \n", + "
| 3 | \n", + "1 | \n", + "9 | \n", + "1 | \n", + "
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+ "\n",
+ "\n",
+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " probas values word_ids\n",
+ "ids \n",
+ "0 [6, 5, 6] [0, 1, 0] [0, 1, 2]\n",
+ "1 [8, 7, 9] [0, 1, 1] [0, 1, 2]"
+ ]
+ },
+ "execution_count": 193,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "g = df.groupby('ids').agg({col: lambda x: x.values.flatten().tolist() for col in df.columns}).drop('ids', axis=1)\n",
+ "g"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "9015b0c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import base64\n",
+ "def encode_model_name(model_name, epoch):\n",
+ " model_name_b = model_name.encode('ascii')\n",
+ " encoded_model_name = base64.b64encode(model_name_b).decode('ascii')\n",
+ " return encoded_model_name + f'_{epoch}'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "937bafb7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Z29vZ2xlL2JpZ2JpcmQ=_1'"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "encode_model_name('google/bigbird', 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d1598cdb",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/inference.ipynb b/experiments/yousef/inference.ipynb
new file mode 100644
index 0000000..ea51c77
--- /dev/null
+++ b/experiments/yousef/inference.ipynb
@@ -0,0 +1,350 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e625f2f2",
+ "metadata": {},
+ "source": [
+ "# quick inference poc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4709017a",
+ "metadata": {},
+ "source": [
+ "### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "5e522863",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# -- public imports\n",
+ "\n",
+ "from transformers import BigBirdTokenizer, AutoTokenizer, AutoModelForTokenClassification, AutoConfig\n",
+ "import pandas as pd\n",
+ "from torch.utils.data import DataLoader\n",
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "89b1b3df",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Import of ArgMiner successful\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- private import\n",
+ "from argminer.data import KaggleDataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "e77ce1f1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- dev imports\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3eff33ef",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n",
+ "Some weights of the model checkpoint at google/bigbird-roberta-large were not used when initializing BigBirdForTokenClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias']\n",
+ "- This IS expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BigBirdForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of BigBirdForTokenClassification were not initialized from the model checkpoint at google/bigbird-roberta-large and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = 'google/bigbird-roberta-large'\n",
+ "\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model, add_prefix_space=True)\n",
+ "config_model = AutoConfig.from_pretrained(model) \n",
+ "config_model.num_labels = 3\n",
+ "model = AutoModelForTokenClassification.from_pretrained(model, config=config_model)\n",
+ "optimizer = torch.optim.Adam(params=model.parameters())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "4ee5553f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "texts = [\n",
+ " 'Hi my name is Yousef Nami',\n",
+ " 'Hi I am Shirin'\n",
+ " ]\n",
+ "labels = [\n",
+ " ['O', 'O', 'O', 'O', 'B-PERS', 'I-PERS'],\n",
+ " ['O', 'O', 'O', 'B-PERS']\n",
+ "]\n",
+ "\n",
+ "# TODO see if bert can accept text inputs?\n",
+ "labels_numeric = [\n",
+ " [0, 0, 0, 0, 1, 2],\n",
+ " [0, 0, 0, 1]\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "9dd5d84e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame({\n",
+ " 'text': texts,\n",
+ " 'labels': labels_numeric\n",
+ " })"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "f971c54d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset = KaggleDataset(\n",
+ " df,\n",
+ " tokenizer,\n",
+ " max_length=20\n",
+ ")\n",
+ "\n",
+ "loader = torch.utils.data.DataLoader(dataset, batch_size=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "65e8b6f6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'input_ids': tensor([[ 65, 16003, 717, 1539, 419, 676, 617, 992, 500, 6378,\n",
+ " 66, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [ 65, 16003, 415, 817, 1012, 47489, 66, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'word_ids': tensor([[-1, 0, 1, 2, 3, 4, 4, 4, 5, 5, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+ " -1, -1],\n",
+ " [-1, 0, 1, 2, 3, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
+ " -1, -1]]), 'word_id_mask': [tensor([False, False]), tensor([True, True]), tensor([True, True]), tensor([True, True]), tensor([True, True]), tensor([True, True]), tensor([ True, False]), tensor([ True, False]), tensor([ True, False]), tensor([ True, False]), tensor([False, False]), tensor([False, False]), tensor([False, False]), tensor([False, False]), tensor([False, False]), tensor([False, False]), tensor([False, False]), tensor([False, False]), tensor([False, False]), tensor([False, False])]}\n",
+ "tensor([[[-0.1642, 0.0992, 0.3482],\n",
+ " [-0.0166, -0.3431, 0.7926],\n",
+ " [ 0.0151, 0.1921, 0.6123],\n",
+ " [ 0.0664, -0.0830, 0.5713],\n",
+ " [ 0.0521, -0.2243, 0.7010],\n",
+ " [ 0.2475, -0.2039, 0.3427],\n",
+ " [ 0.0468, -0.2063, 0.4431],\n",
+ " [ 0.3500, -0.1089, 0.4890],\n",
+ " [-0.1642, 0.0992, 0.3482],\n",
+ " [ 0.2735, -0.3232, 0.6088],\n",
+ " [ 0.0164, -0.2838, 0.7500],\n",
+ " [-0.1642, 0.0992, 0.3482],\n",
+ " [-0.1642, 0.0992, 0.3482],\n",
+ " [-0.1642, 0.0992, 0.3482],\n",
+ " [-0.1642, 0.0992, 0.3482],\n",
+ " [-0.1642, 0.0992, 0.3482],\n",
+ " [-0.3618, -0.0890, 0.4821],\n",
+ " [-0.1642, 0.0992, 0.3482],\n",
+ " [ 0.0758, -0.3785, 0.5925],\n",
+ " [-0.1642, 0.0992, 0.3482]],\n",
+ "\n",
+ " [[-0.1558, 0.0520, 0.3420],\n",
+ " [-0.1565, 0.0522, 0.3423],\n",
+ " [-0.1566, 0.0523, 0.3424],\n",
+ " [-0.1557, 0.0502, 0.3406],\n",
+ " [-0.1565, 0.0522, 0.3423],\n",
+ " [-0.1569, 0.0525, 0.3428],\n",
+ " [-0.1566, 0.0523, 0.3425],\n",
+ " [-0.1564, 0.0522, 0.3422],\n",
+ " [-0.1564, 0.0522, 0.3422],\n",
+ " [-0.1564, 0.0522, 0.3422],\n",
+ " [-0.1565, 0.0523, 0.3424],\n",
+ " [-0.1565, 0.0522, 0.3423],\n",
+ " [-0.1565, 0.0523, 0.3424],\n",
+ " [-0.1564, 0.0522, 0.3423],\n",
+ " [-0.1564, 0.0522, 0.3423],\n",
+ " [-0.1564, 0.0522, 0.3422],\n",
+ " [-0.1564, 0.0522, 0.3422],\n",
+ " [-0.1564, 0.0522, 0.3422],\n",
+ " [-0.1564, 0.0522, 0.3422],\n",
+ " [-0.1564, 0.0522, 0.3422]]], grad_fn=| \n", + " | probas | \n", + "values | \n", + "word_ids | \n", + "
|---|---|---|---|
| ids | \n", + "\n", + " | \n", + " | \n", + " |
| 0 | \n", + "[6, 5, 6] | \n", + "[0, 1, 0] | \n", + "[0, 1, 2] | \n", + "
| 1 | \n", + "[8, 7, 9] | \n", + "[0, 1, 1] | \n", + "[0, 1, 2] | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id discourse_id discourse_start discourse_end \\\n",
+ "0 423A1CA112E2 1.622628e+12 8.0 229.0 \n",
+ "1 423A1CA112E2 1.622628e+12 230.0 312.0 \n",
+ "2 423A1CA112E2 1.622628e+12 313.0 401.0 \n",
+ "3 423A1CA112E2 1.622628e+12 402.0 758.0 \n",
+ "4 423A1CA112E2 1.622628e+12 759.0 886.0 \n",
+ "\n",
+ " discourse_text discourse_type \\\n",
+ "0 Modern humans today are always on their phone.... Lead \n",
+ "1 They are some really bad consequences when stu... Position \n",
+ "2 Some certain areas in the United States ban ph... Evidence \n",
+ "3 When people have phones, they know about certa... Evidence \n",
+ "4 Driving is one of the way how to get around. P... Claim \n",
+ "\n",
+ " discourse_type_num predictionstring \n",
+ "0 Lead 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... \n",
+ "1 Position 1 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 \n",
+ "2 Evidence 1 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 \n",
+ "3 Evidence 2 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9... \n",
+ "4 Claim 1 139 140 141 142 143 144 145 146 147 148 149 15... "
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train = pd.read_csv(path_train)\n",
+ "df_train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "e441e13c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_temp = df_train.copy()[['discourse_text', 'discourse_type']]\n",
+ "df_temp['discourse_type'] = df_temp['discourse_type'].astype('category').cat.codes\n",
+ "df_temp.head()\n",
+ "X = df_temp.discourse_text\n",
+ "y = df_temp.discourse_type"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "6f383089",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# functions\n",
+ "def split_paragraphs(text):\n",
+ " # for now very simply, just uses '\\n'\n",
+ " return text.split('\\n')\n",
+ "\n",
+ "def segmentor(text):\n",
+ " doc = nlp(text)\n",
+ " discourse_start = []\n",
+ " discourse_end = []\n",
+ " discourse_text = []\n",
+ " for sent in doc.sents:\n",
+ " discourse_start.append(sent.start_char)\n",
+ " discourse_end.append(sent.end_char)\n",
+ " discourse_text.append(sent)\n",
+ " df = pd.DataFrame(data=dict(\n",
+ " discourse_start=discourse_start,\n",
+ " discourse_end=discourse_end,\n",
+ " discourse_text=discourse_text,\n",
+ " ))\n",
+ " return df\n",
+ "\n",
+ "\n",
+ "class TextSegmentation:\n",
+ " \n",
+ " def __init__(self):\n",
+ " pass\n",
+ " \n",
+ " def fit(self, X, y):\n",
+ " \n",
+ " self.X = X\n",
+ " self.y = y\n",
+ " \n",
+ " def predict(X):\n",
+ " \n",
+ " segmentor(X)\n",
+ " \n",
+ " \n",
+ "def create_vocab(texts, nlp=None):\n",
+ " if nlp is None:\n",
+ " nlp = spacy.load('en_core_web_lg')\n",
+ " \n",
+ " vocab = Vo\n",
+ " for doc in nlp.pipe(texts):\n",
+ " pass\n",
+ " \n",
+ "\n",
+ "def get_avg_vector(text, nlp, counter):\n",
+ " \"\"\" Gets average word vectors of a piece of text \"\"\"\n",
+ " doc = nlp(text)\n",
+ " avg_vector = np.vstack([token.vector for token in doc]).mean(axis=0)\n",
+ " print(f'Completed text {counter}')\n",
+ " return avg_vector\n",
+ "\n",
+ "def generate_train_features(texts, nlp):\n",
+ " \"\"\" Generates training features from a sequence of texts \"\"\"\n",
+ " X = np.vstack([get_avg_vector(text, nlp, i) for i,text in enumerate(texts, 1)])\n",
+ " return X"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "2910a295",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
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+ ]
+ }
+ ],
+ "source": [
+ "nlp = spacy.load('en_core_web_lg')\n",
+ "\n",
+ "X = generate_train_features(X, nlp)\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "c6b923c7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RandomForestClassifier(n_estimators=10)"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model = RandomForestClassifier(n_estimators=10)\n",
+ "model.fit(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "0889606a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_pred = model.predict(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "54ee32c0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.56068656440584"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(y_pred == y_test).mean()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/kaggle_dataset_eda.ipynb b/experiments/yousef/kaggle_dataset_eda.ipynb
new file mode 100644
index 0000000..9ab2cc0
--- /dev/null
+++ b/experiments/yousef/kaggle_dataset_eda.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "a3a55015",
+ "metadata": {},
+ "source": [
+ "# kaggle dataset eda"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b166b44a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import pandas as pd\n",
+ "\n",
+ "\n",
+ "# paths\n",
+ "path_base = '../../data/kaggle/feedback-prize-2021'\n",
+ "dir_train = os.path.join(path_base, 'train')\n",
+ "dir_test = os.path.join(path_base, 'test')\n",
+ "path_train = os.path.join(path_base, 'train.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b8b5191c",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "discourse_id | \n", + "discourse_start | \n", + "discourse_end | \n", + "discourse_text | \n", + "discourse_type | \n", + "discourse_type_num | \n", + "predictionstring | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "423A1CA112E2 | \n", + "1.622628e+12 | \n", + "8.0 | \n", + "229.0 | \n", + "Modern humans today are always on their phone.... | \n", + "Lead | \n", + "Lead 1 | \n", + "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... | \n", + "
| 1 | \n", + "423A1CA112E2 | \n", + "1.622628e+12 | \n", + "230.0 | \n", + "312.0 | \n", + "They are some really bad consequences when stu... | \n", + "Position | \n", + "Position 1 | \n", + "45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | \n", + "
| 2 | \n", + "423A1CA112E2 | \n", + "1.622628e+12 | \n", + "313.0 | \n", + "401.0 | \n", + "Some certain areas in the United States ban ph... | \n", + "Evidence | \n", + "Evidence 1 | \n", + "60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | \n", + "
| 3 | \n", + "423A1CA112E2 | \n", + "1.622628e+12 | \n", + "402.0 | \n", + "758.0 | \n", + "When people have phones, they know about certa... | \n", + "Evidence | \n", + "Evidence 2 | \n", + "76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9... | \n", + "
| 4 | \n", + "423A1CA112E2 | \n", + "1.622628e+12 | \n", + "759.0 | \n", + "886.0 | \n", + "Driving is one of the way how to get around. P... | \n", + "Claim | \n", + "Claim 1 | \n", + "139 140 141 142 143 144 145 146 147 148 149 15... | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id discourse_id discourse_start discourse_end \\\n",
+ "0 423A1CA112E2 1.622628e+12 8.0 229.0 \n",
+ "1 423A1CA112E2 1.622628e+12 230.0 312.0 \n",
+ "2 423A1CA112E2 1.622628e+12 313.0 401.0 \n",
+ "3 423A1CA112E2 1.622628e+12 402.0 758.0 \n",
+ "4 423A1CA112E2 1.622628e+12 759.0 886.0 \n",
+ "\n",
+ " discourse_text discourse_type \\\n",
+ "0 Modern humans today are always on their phone.... Lead \n",
+ "1 They are some really bad consequences when stu... Position \n",
+ "2 Some certain areas in the United States ban ph... Evidence \n",
+ "3 When people have phones, they know about certa... Evidence \n",
+ "4 Driving is one of the way how to get around. P... Claim \n",
+ "\n",
+ " discourse_type_num predictionstring \n",
+ "0 Lead 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... \n",
+ "1 Position 1 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 \n",
+ "2 Evidence 1 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 \n",
+ "3 Evidence 2 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9... \n",
+ "4 Claim 1 139 140 141 142 143 144 145 146 147 148 149 15... "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train = pd.read_csv(path_train)\n",
+ "df_train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "fba21086",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# check that ids are the same as train files\n",
+ "assert sorted(set(df_train.id)) == sorted([file_name.strip('.txt') for file_name in set(os.listdir(dir_train))])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "28024fd5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "discourse_id | \n", + "discourse_start | \n", + "discourse_end | \n", + "discourse_text | \n", + "discourse_type | \n", + "discourse_type_num | \n", + "predictionstring | \n", + "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "423A1CA112E2 | \n", + "1.622628e+12 | \n", + "8.0 | \n", + "229.0 | \n", + "Modern humans today are always on their phone.... | \n", + "Lead | \n", + "Lead 1 | \n", + "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1... | \n", + "
| 1 | \n", + "423A1CA112E2 | \n", + "1.622628e+12 | \n", + "230.0 | \n", + "312.0 | \n", + "They are some really bad consequences when stu... | \n", + "Position | \n", + "Position 1 | \n", + "45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | \n", + "
| 2 | \n", + "423A1CA112E2 | \n", + "1.622628e+12 | \n", + "313.0 | \n", + "401.0 | \n", + "Some certain areas in the United States ban ph... | \n", + "Evidence | \n", + "Evidence 1 | \n", + "60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | \n", + "
| 3 | \n", + "423A1CA112E2 | \n", + "1.622628e+12 | \n", + "402.0 | \n", + "758.0 | \n", + "When people have phones, they know about certa... | \n", + "Evidence | \n", + "Evidence 2 | \n", + "76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 9... | \n", + "
| 4 | \n", + "423A1CA112E2 | \n", + "1.622628e+12 | \n", + "759.0 | \n", + "886.0 | \n", + "Driving is one of the way how to get around. P... | \n", + "Claim | \n", + "Claim 1 | \n", + "139 140 141 142 143 144 145 146 147 148 149 15... | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id discourse_id discourse_start discourse_end \\\n",
+ "96558 0A0AA9C21C5D 1.615667e+12 21.0 529.0 \n",
+ "96559 0A0AA9C21C5D 1.615667e+12 530.0 627.0 \n",
+ "96560 0A0AA9C21C5D 1.615667e+12 628.0 749.0 \n",
+ "96561 0A0AA9C21C5D 1.615818e+12 749.0 1434.0 \n",
+ "96562 0A0AA9C21C5D 1.615818e+12 1435.0 1603.0 \n",
+ "\n",
+ " discourse_text discourse_type \\\n",
+ "96558 Many people believe that the Electoral College... Lead \n",
+ "96559 I argue in favor of changing to election by po... Position \n",
+ "96560 The first reason as to why I favor in abolishi... Claim \n",
+ "96561 As shown back in 1960, segregationists in the ... Evidence \n",
+ "96562 Another reason as to why you can not trust the... Claim \n",
+ "\n",
+ " discourse_type_num predictionstring \n",
+ "96558 Lead 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20... \n",
+ "96559 Position 1 92 93 94 95 96 97 98 99 100 101 102 103 104 10... \n",
+ "96560 Claim 1 110 111 112 113 114 115 116 117 118 119 120 12... \n",
+ "96561 Evidence 1 132 133 134 135 136 137 138 139 140 141 142 14... \n",
+ "96562 Claim 2 254 255 256 257 258 259 260 261 262 263 264 26... "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# explore single id\n",
+ "df_temp = df_train.copy()\n",
+ "df_temp = df_temp[df_temp.id == '0A0AA9C21C5D']\n",
+ "sorted_disc_ids = sorted([id_ for id_ in df_temp.discourse_id])\n",
+ "df_temp.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "d6d5f525",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def test_prediction_string():\n",
+ " \"\"\" test to see if prediction string is created by whitespace separation \"\"\"\n",
+ " for text, prediction_string in zip(df_train.discourse_text, df_train.predictionstring):\n",
+ " text_ids = ' '.join([str(i) for i, _ in enumerate(text.split(), int(prediction_string.split()[0]))])\n",
+ " equality = text_ids == prediction_string\n",
+ " break\n",
+ " ##try:\n",
+ " # assert text_ids == prediction_string\n",
+ " #except:\n",
+ " # print(text, text_ids, prediction_string)\n",
+ "test_prediction_string()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "6683243c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "22\n",
+ "22\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(len(df_temp.discourse_text[96560].split()))\n",
+ "print(len(df_temp.predictionstring[96560].split()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "e46295c9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'As shown back in 1960, segregationists in the Louisiana legislature nearly succeeded in replacing Democratic electors with new electors who would oppose John F. Kennedy so that a popular vote for Kennedy would not have actually gone to Kennedy. Also, due to the winner-take-all system, candidates do not spend time in states they know they have no chance in winning, it is obvious that they just want to win and you can not trust those candidates, especially the people who promise to do things they know they can not fulfill. The electors that \"the people vote for\" do not want to help their state, they do not even care for the needs of the people, they are just there for the money '"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_temp.discourse_text[96561]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "babc2c50",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Lead 1\n",
+ "1 Position 1\n",
+ "2 Evidence 1\n",
+ "3 Evidence 2\n",
+ "4 Claim 1\n",
+ " ... \n",
+ "144288 Evidence 2\n",
+ "144289 Evidence 3\n",
+ "144290 Position 1\n",
+ "144291 Evidence 4\n",
+ "144292 Concluding Statement 1\n",
+ "Name: discourse_type_num, Length: 144293, dtype: object"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train.discourse_type_num"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5ea0aab7",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/kaggle_notebook_test.ipynb b/experiments/yousef/kaggle_notebook_test.ipynb
new file mode 100644
index 0000000..f5e8e23
--- /dev/null
+++ b/experiments/yousef/kaggle_notebook_test.ipynb
@@ -0,0 +1,114 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "427d5d28",
+ "metadata": {
+ "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+ "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+ "execution": {
+ "iopub.execute_input": "2022-02-15T20:02:00.708560Z",
+ "iopub.status.busy": "2022-02-15T20:02:00.706789Z",
+ "iopub.status.idle": "2022-02-15T20:02:00.715472Z",
+ "shell.execute_reply": "2022-02-15T20:02:00.716181Z"
+ },
+ "papermill": {
+ "duration": 0.020365,
+ "end_time": "2022-02-15T20:02:00.716546",
+ "exception": false,
+ "start_time": "2022-02-15T20:02:00.696181",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# This Python 3 environment comes with many helpful analytics libraries installed\n",
+ "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
+ "# For example, here's several helpful packages to load\n",
+ "\n",
+ "import numpy as np # linear algebra\n",
+ "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+ "\n",
+ "# Input data files are available in the read-only \"../input/\" directory\n",
+ "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
+ "\n",
+ "import os\n",
+ "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+ " for filename in filenames:\n",
+ " print(os.path.join(dirname, filename))\n",
+ "\n",
+ "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
+ "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2898f298",
+ "metadata": {
+ "papermill": {
+ "duration": 0.004644,
+ "end_time": "2022-02-15T20:02:00.727023",
+ "exception": false,
+ "start_time": "2022-02-15T20:02:00.722379",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "738dad7e",
+ "metadata": {
+ "papermill": {
+ "duration": 0.003832,
+ "end_time": "2022-02-15T20:02:00.735791",
+ "exception": false,
+ "start_time": "2022-02-15T20:02:00.731959",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "# test notebook save GitHub"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.12"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 9.451282,
+ "end_time": "2022-02-15T20:02:01.352533",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "__notebook__.ipynb",
+ "output_path": "__notebook__.ipynb",
+ "parameters": {},
+ "start_time": "2022-02-15T20:01:51.901251",
+ "version": "2.3.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/labelling_schemes.ipynb b/experiments/yousef/labelling_schemes.ipynb
new file mode 100644
index 0000000..11041f3
--- /dev/null
+++ b/experiments/yousef/labelling_schemes.ipynb
@@ -0,0 +1,538 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "a16d3f43",
+ "metadata": {},
+ "source": [
+ "# Labelling Schemes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4c4c0535",
+ "metadata": {},
+ "source": [
+ "### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "id": "db87fe2d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# -- public imports\n",
+ "\n",
+ "import os\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "8358055f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# -- private import"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "658532fc",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "# -- dev imports\n",
+ "%load_ext autoreload\n",
+ "%autoreload 2\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fdfafee3",
+ "metadata": {},
+ "source": [
+ "### Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "3d42f99b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | id | \n", + "discourse_id | \n", + "discourse_start | \n", + "discourse_end | \n", + "discourse_text | \n", + "discourse_type | \n", + "discourse_type_num | \n", + "predictionstring | \n", + "
|---|---|---|---|---|---|---|---|---|
| 96558 | \n", + "0A0AA9C21C5D | \n", + "1.615667e+12 | \n", + "21.0 | \n", + "529.0 | \n", + "Many people believe that the Electoral College... | \n", + "Lead | \n", + "Lead 1 | \n", + "3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20... | \n", + "
| 96559 | \n", + "0A0AA9C21C5D | \n", + "1.615667e+12 | \n", + "530.0 | \n", + "627.0 | \n", + "I argue in favor of changing to election by po... | \n", + "Position | \n", + "Position 1 | \n", + "92 93 94 95 96 97 98 99 100 101 102 103 104 10... | \n", + "
| 96560 | \n", + "0A0AA9C21C5D | \n", + "1.615667e+12 | \n", + "628.0 | \n", + "749.0 | \n", + "The first reason as to why I favor in abolishi... | \n", + "Claim | \n", + "Claim 1 | \n", + "110 111 112 113 114 115 116 117 118 119 120 12... | \n", + "
| 96561 | \n", + "0A0AA9C21C5D | \n", + "1.615818e+12 | \n", + "749.0 | \n", + "1434.0 | \n", + "As shown back in 1960, segregationists in the ... | \n", + "Evidence | \n", + "Evidence 1 | \n", + "132 133 134 135 136 137 138 139 140 141 142 14... | \n", + "
| 96562 | \n", + "0A0AA9C21C5D | \n", + "1.615818e+12 | \n", + "1435.0 | \n", + "1603.0 | \n", + "Another reason as to why you can not trust the... | \n", + "Claim | \n", + "Claim 2 | \n", + "254 255 256 257 258 259 260 261 262 263 264 26... | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " label_type label text \\\n",
+ "0 O Other Should students be taught to compete or to coo... \n",
+ "1 T1 MajorClaim we should attach more importance to cooperatio... \n",
+ "2 O Other .\\nFirst of all, \n",
+ "3 T3 Claim through cooperation, children can learn about ... \n",
+ "4 O Other . \n",
+ "\n",
+ " span_start span_end doc_id \n",
+ "0 0 503 essay001 \n",
+ "1 503 575 essay001 \n",
+ "2 575 591 essay001 \n",
+ "3 591 714 essay001 \n",
+ "4 714 716 essay001 "
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "base_path = '../../data/UCL/dataset2'\n",
+ "dataset_path = os.path.join(base_path, 'df_with_other.json')\n",
+ "df = pd.read_json(dataset_path)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "fd18c499",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['Other' 'MajorClaim' 'Claim' 'Premise']\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df.label.unique())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "id": "9a21c880",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_label_map = pd.DataFrame(data={\n",
+ " 'label': ['Other', 'MajorClaim', 'Claim', 'Premise']\n",
+ "}).reset_index().rename(columns={'index':'label_id'})\n",
+ "df_label_map\n",
+ "\n",
+ "df_label_map.to_json(\n",
+ " os.path.join(base_path, 'df_label_map_general.json'),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45fa4b68",
+ "metadata": {},
+ "source": [
+ "### Add Prediction String"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "id": "613ec6eb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prediction_strings = []\n",
+ "start_id = 1\n",
+ "for (label, text, doc_id) in df[['label', 'text', 'doc_id']].itertuples(index=False):\n",
+ " text_split = text.split()\n",
+ " end_id = start_id + len(text_split)\n",
+ " prediction_strings.append(\n",
+ " [num for num in range(start_id, end_id)]\n",
+ " )\n",
+ " start_id = end_id\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "id": "6dfafa4a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['predictionString'] = prediction_strings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "id": "8ab4cc96",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | label_type | \n", + "label | \n", + "text | \n", + "span_start | \n", + "span_end | \n", + "doc_id | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "O | \n", + "Other | \n", + "Should students be taught to compete or to coo... | \n", + "0 | \n", + "503 | \n", + "essay001 | \n", + "
| 1 | \n", + "T1 | \n", + "MajorClaim | \n", + "we should attach more importance to cooperatio... | \n", + "503 | \n", + "575 | \n", + "essay001 | \n", + "
| 2 | \n", + "O | \n", + "Other | \n", + ".\\nFirst of all, | \n", + "575 | \n", + "591 | \n", + "essay001 | \n", + "
| 3 | \n", + "T3 | \n", + "Claim | \n", + "through cooperation, children can learn about ... | \n", + "591 | \n", + "714 | \n", + "essay001 | \n", + "
| 4 | \n", + "O | \n", + "Other | \n", + ". | \n", + "714 | \n", + "716 | \n", + "essay001 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " label_type label text \\\n",
+ "0 O Other Should students be taught to compete or to coo... \n",
+ "1 T1 MajorClaim we should attach more importance to cooperatio... \n",
+ "2 O Other .\\nFirst of all, \n",
+ "3 T3 Claim through cooperation, children can learn about ... \n",
+ "4 O Other . \n",
+ "\n",
+ " span_start span_end doc_id \\\n",
+ "0 0 503 essay001 \n",
+ "1 503 575 essay001 \n",
+ "2 575 591 essay001 \n",
+ "3 591 714 essay001 \n",
+ "4 714 716 essay001 \n",
+ "\n",
+ " predictionString \n",
+ "0 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14... \n",
+ "1 [83, 84, 85, 86, 87, 88, 89, 90, 91, 92] \n",
+ "2 [93, 94, 95, 96] \n",
+ "3 [97, 98, 99, 100, 101, 102, 103, 104, 105, 106... \n",
+ "4 [115] "
+ ]
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "048cc521",
+ "metadata": {},
+ "source": [
+ "### Labelling strategy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "id": "0304ef2e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def _label_bio(length, label, add_end=False):\n",
+ " \"\"\"\n",
+ " For cases where argument segment is only 1 word long, beginning given preference over end\n",
+ " \"\"\"\n",
+ " labels = [f'I-{label}'] if label != 'Other' else ['O']\n",
+ " labels *= length\n",
+ " \n",
+ " if add_end:\n",
+ " if label != 'Other':\n",
+ " labels[-1] = f'E-{label}'\n",
+ " \n",
+ " if label != 'Other':\n",
+ " labels[0] = f'B-{label}'\n",
+ " \n",
+ " return labels\n",
+ " \n",
+ "df['label_bio'] = df[['label', 'predictionString']].apply(\n",
+ " lambda x: _label_bio(len(x['predictionString']), x['label']), axis=1\n",
+ ")\n",
+ "df['label_bieo'] = df[['label', 'predictionString']].apply(\n",
+ " lambda x: _label_bio(len(x['predictionString']), x['label'], True), axis=1\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "id": "90c5631b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[list(['B-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim'])\n",
+ " list(['B-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'I-MajorClaim', 'E-MajorClaim'])]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df[['label_bio', 'label_bieo']].values[1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "id": "94fc88bf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.to_json(\n",
+ " os.path.join(base_path, 'df_labels_general.json'),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "296308ee",
+ "metadata": {},
+ "source": [
+ "### Add label keys for other labelling strategies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "id": "2952cc64",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def _get_label_maps(unique_labels, strategy):\n",
+ " unique_labels = [label for label in unique_labels if label != 'Other']\n",
+ " labels = ['O']\n",
+ " if strategy == 'bio':\n",
+ " for label in unique_labels:\n",
+ " labels.append(f'B-{label}')\n",
+ " labels.append(f'I-{label}')\n",
+ " elif strategy == 'bieo':\n",
+ " for label in unique_labels:\n",
+ " labels.append(f'B-{label}')\n",
+ " labels.append(f'I-{label}')\n",
+ " labels.append(f'E-{label}')\n",
+ " elif strategy == 'bixo':\n",
+ " labels.append('X')\n",
+ " for label in unique_labels:\n",
+ " labels.append(f'B-{label}')\n",
+ " labels.append(f'I-{label}')\n",
+ " else:\n",
+ " raise NotImplementedError(f'Strategy {strategy} has not implementation yet.')\n",
+ " \n",
+ " return pd.DataFrame({\n",
+ " 'label': labels\n",
+ " }).reset_index().rename(columns={'index':'label_id'})\n",
+ " \n",
+ "# TODO should be do a biexo as well?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "id": "0f43d7e3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "_get_label_maps(unique_labels, 'bio').to_json(\n",
+ " os.path.join(base_path, 'df_label_map_bio.json')\n",
+ ")\n",
+ "_get_label_maps(unique_labels, 'bieo').to_json(\n",
+ " os.path.join(base_path, 'df_label_map_bieo.json')\n",
+ ")\n",
+ "_get_label_maps(unique_labels, 'bixo').to_json(\n",
+ " os.path.join(base_path, 'df_label_map_bixo.json')\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "aea31d6e",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "argument-mining",
+ "language": "python",
+ "name": "argument-mining"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/experiments/yousef/main.py b/experiments/yousef/main.py
new file mode 100644
index 0000000..e4b7c3a
--- /dev/null
+++ b/experiments/yousef/main.py
@@ -0,0 +1,3 @@
+import sys
+print(sys.path)
+import argminer
\ No newline at end of file
diff --git a/experiments/yousef/python_files/__init__.py b/experiments/yousef/python_files/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/experiments/yousef/python_files/data.py b/experiments/yousef/python_files/data.py
new file mode 100644
index 0000000..6dfcc8e
--- /dev/null
+++ b/experiments/yousef/python_files/data.py
@@ -0,0 +1,314 @@
+from torch.utils.data import DataLoader, Dataset
+import os
+import time
+import numpy as np
+import pandas as pd
+from pandas.api.types import is_integer_dtype, is_string_dtype
+import torch
+
+def _first_appearance_of_unique_item(x):
+ """ Torch function to get the first appearance of a unique item"""
+ unique, inverse = torch.unique(x, sorted=True, return_inverse=True)
+ perm = torch.arange(inverse.size(0), dtype=inverse.dtype, device=inverse.device)
+ inverse, perm = inverse.flip([0]), perm.flip([0])
+ perm = inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)
+ return perm
+
+class ArgumentMiningDataset(Dataset):
+ """
+ Class for loading data in batches and processing it
+ """
+ def __init__(self, df_label_map, df_text, tokenizer, max_length, strategy):
+ super().__init__()
+
+ assert sorted(df_text.columns) == ['labels', 'text'], f"Please make sure input dataframe has the columns (text, labels)"
+ assert 'O' in df_label_map.label.values, 'You are missing label "O"'
+ assert strategy in ['standard_bio', 'word_level_bio', 'standard_bixo', 'standard_bieo', 'word_level_bieo'], 'Please use a valid strategy'
+
+ strategy_level, strategy_name = strategy.split('_')
+
+ self.strategy_level = strategy_level
+ self.strategy_name = strategy_name
+
+ if strategy_name == 'bio':
+ labels = df_label_map.label[df_label_map.label.str.contains('-')].apply(lambda x: x[:1])
+ labels = list(labels.unique())
+ assert 'BI' == ''.join(sorted(labels)), 'You are missing one of labels "B" or "I"'
+ elif strategy_name == 'bieo':
+ labels = df_label_map.label[df_label_map.label.str.contains('-')].apply(lambda x: x[:1])
+ labels = list(labels.unique())
+ assert 'BIE' == ''.join(sorted(labels)), 'You are missing one of labels "B", "I" or "E"'
+ elif strategy_name == 'bixo':
+ assert 'X' in df_label_map.label.values, 'You are missing label "X"'
+ labels = df_label_map.label[df_label_map.label.str.contains('-')].apply(lambda x: x[:1])
+ labels = list(labels.unique())
+ assert 'BI' == ''.join(sorted(labels)), 'You are missing one of labels "B" or "I"'
+ else:
+ raise NotImplementedError(f'Support for labelling strategy {strategy} does not exist yet')
+
+
+ self.inputs = df_text.text.values
+ if not is_string_dtype(self.inputs): raise TypeError('Text data must be string type')
+
+ self.consider_end = 'e' in strategy_name # TODO make more robust
+ self.use_x = 'x' in strategy_name # TODO make more robust
+
+ # create hash table
+ self.label_to_id = {
+ label: id_ for label, id_ in df_label_map[['label', 'label_id']].values
+ }
+ self.id_to_label = {
+ id_: label for label, id_ in self.label_to_id.items()
+ }
+ self.targets = df_text.labels.apply(
+ lambda x: [self.label_to_id[label] for label in x]
+ ).values
+
+
+
+ # -- prepare tokenizer
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+
+ def __len__(self):
+ return len(self.inputs)
+
+ def __getitem__(self, index):
+ # self.inputs anf self.targets must be of a type that is indexible as shown
+ inputs = self.inputs[index]
+ targets = self.targets[index]
+
+ # TODO this is for the sentencepiece tokenizer, might have to change for wordpiece
+ inputs = self.tokenizer(
+ # consider parametrising these
+ inputs.split(),
+ is_split_into_words=True, # this means that extra \n should be ignored
+ padding='max_length',
+ truncation=True,
+ max_length=self.max_length
+ )
+ word_ids = inputs.word_ids()
+ word_id_mask = [word_id is not None for word_id in word_ids]
+ # TODO maybe this should go in the function?
+ word_ids = [word_id for word_id in word_ids if word_id is not None]
+
+ targets = torch.as_tensor(targets, dtype=torch.long)
+
+ labeller = getattr(self, f'_label_{self.strategy_level}')
+ targets = labeller(targets, word_id_mask, word_ids)
+
+ """# TODO need to think about sending things to the GPU, which ones to send
+ inputs = {
+ key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()
+ }
+ inputs['doc_ids'] = index # TODO may have to convert to type long
+ inputs['word_ids'] = word_ids
+
+ targets = torch.as_tensor(targets, dtype=torch.long)
+ expanded_targets = torch.zeros(self.max_length, dtype=torch.long)
+ expanded_targets[word_id_mask] = targets[word_ids]"""
+
+ return (inputs, targets)
+
+
+ def _label_word_level(self, targets, word_id_mask, word_ids):
+ expanded_targets = torch.zeros(self.max_length, dtype=torch.long)
+ expanded_targets[word_id_mask] = targets[word_ids]
+ return expanded_targets
+
+ def _label_standard(self, targets, word_id_mask, word_ids):
+ expanded_targets = torch.zeros(self.max_length, dtype=torch.long)
+ expanded_targets[word_id_mask] = targets[word_ids]
+ word_start_ids = _first_appearance_of_unique_item(torch.as_tensor(word_ids))
+ unique_word_ids, word_id_counts = torch.unique(torch.as_tensor(word_ids), return_counts=True)
+
+ # here define the start and end labels
+ for i, (word_start_id, word_id, word_id_count) in enumerate(
+ zip(word_start_ids, unique_word_ids, word_id_counts)):
+ curr_target = expanded_targets[word_start_id].item()
+ if curr_target: # step to filter the orhers
+ if word_id_count > 1:
+ ids = list(range(word_start_id, word_start_id + word_id_count))
+
+ # TODO can make robust by adding string condition 'E-'
+ position, target_label = self.id_to_label[curr_target].split('-')
+ if self.consider_end and 'E' == position:
+ ids = ids[:-1]
+ else:
+ ids = ids[1:]
+
+ expanded_target_label = self.label_to_id['X'] if self.use_x else self.label_to_id[f'I-{target_label}']
+ expanded_targets[ids] = expanded_target_label # this label needs to be changed!
+ return expanded_targets
+class KaggleDataset(Dataset):
+ """
+ Class for loading data in batches after it has been processed
+ """
+ def __init__(self, dataframe, tokenizer, max_length):
+
+ super().__init__()
+
+ # -- prepare data
+ assert sorted(dataframe.columns) == ['labels', 'text'], f"Please make sure input dataframe has the columns (text, labels)"
+ # data must be in the correct format
+ self.inputs = dataframe.text.values
+ self.targets = dataframe.labels.values
+ if not is_string_dtype(self.inputs): raise TypeError('Text data must be string type')
+ # TODO assertion below is bug; not deleting so remember to add correct assertions
+ #if not is_integer_dtype(self.targets): raise TypeError('Label data must be integer type')
+
+ # -- prepare tokenizer
+ self.tokenizer = tokenizer
+ self.max_length = max_length
+
+ def __len__(self):
+ return len(self.inputs)
+
+ def __getitem__(self, index):
+ # self.inputs anf self.targets must be of a type that is indexible as shown
+ inputs = self.inputs[index]
+ targets = self.targets[index]
+
+ inputs = self.tokenizer(
+ # consider parametrising these
+ inputs.split(),
+ is_split_into_words=True, # this means that extra \n should be ignored
+ padding='max_length',
+ truncation=True,
+ max_length=self.max_length
+ )
+
+ word_ids = inputs.word_ids()
+ word_id_mask = [word_id is not None for word_id in word_ids]
+ word_ids = [word_id for word_id in word_ids if word_id is not None]
+
+ # TODO need to think about sending things to the GPU, which ones to send
+ inputs = {
+ key: torch.as_tensor(val, dtype=torch.long) for key, val in inputs.items()
+ }
+ inputs['doc_ids'] = index # TODO may have to convert to type long
+ inputs['word_ids'] = word_ids
+
+ targets = torch.as_tensor(targets, dtype=torch.long)
+ expanded_targets = torch.zeros(self.max_length, dtype=torch.long)
+ expanded_targets[word_id_mask] = targets[word_ids]
+
+ return (inputs, expanded_targets)
+
+
+def generate_class_to_ent_map(unique_classes):
+ # sort classes
+ pass
+
+def df_from_text_files(path_to_dir):
+ filenames = [filename for filename in os.listdir(path_to_dir)]
+ records = [(filename.rstrip('.txt'), open(os.path.join(path_to_dir, filename), 'r').read()) for filename in filenames]
+ df = pd.DataFrame.from_records(records, columns=['id', 'text'])
+ return df
+
+def create_labels_doc_level(path_to_text_dir, path_to_ground_truth):
+ s = time.time()
+ df_ground_truth = pd.read_csv(path_to_ground_truth)
+ unique_labels = list(df_ground_truth.discourse_type.unique())
+ unique_labels = [f'{start_letter}-{label}' for label in unique_labels for start_letter in ['B', 'I']]
+ label_to_id_map = {
+ label: i for i, label in enumerate(
+ ['O'] + unique_labels
+ )
+ }
+
+ df_ground_truth.predictionstring = df_ground_truth.predictionstring.str.split()
+ df_ground_truth['label_ids'] = df_ground_truth.predictionstring.apply(lambda x: [int(x[0]), int(x[-1])])
+ df_ground_truth['labels'] = df_ground_truth[['discourse_type', 'label_ids']].apply(
+ lambda x: [f'B-{x.discourse_type}'] + [f'I-{x.discourse_type}']*(x.label_ids[-1] - x.label_ids[0]),
+ axis=1
+ )
+ df_ground_truth['range'] = df_ground_truth.label_ids.apply(lambda x: np.arange(x[0], x[1]+1))
+ df_ground_truth['labels'] = df_ground_truth.labels.apply(lambda x: [label_to_id_map[label] for label in x])
+
+ # this is kind of wrong?
+
+ df_texts = df_from_text_files(path_to_text_dir)
+ """df_texts.text = df_texts.text.str.replace('\n', ' ')
+ df_texts.text = df_texts.text.str.replace('\s+', ' ')
+ df_texts.text = df_texts.text.str.replace('(?<=\w) (?=[.,\/#!$%\^&\*;:{}=\-_`~()])', '')"""
+ df_texts.text = df_texts.text.str.strip()
+ df_texts['text_split'] = df_texts.text.str.split()
+ df_texts['labels'] = df_texts.text_split.apply(lambda x: len(x)*[label_to_id_map['O']])
+ df_texts = df_texts.merge(
+ df_ground_truth.groupby('id').agg({
+ 'range': lambda x: np.concatenate(list(x)),
+ 'labels': lambda x: np.concatenate(list(x))
+ }).rename(columns={'labels':'labels_temp'}),
+ on='id'
+ )
+ # TODO this needs to change
+ def update_inplace(x):
+ ids = x.range
+ new_labels = x.labels_temp
+ labels = np.array(x.labels, dtype=new_labels.dtype)
+ assert len(ids) == len(new_labels)
+ labels[ids] = new_labels
+ return list(labels)
+
+
+ df_texts.labels = df_texts.apply(lambda x: update_inplace(x), axis=1)
+
+ """df_texts.text = df_texts.text.str.replace('\n', ' ')
+ df_texts.text = df_texts.text.str.replace('\s+', ' ')
+ df_texts.text = df_texts.text.str.replace('(?<=\w) (?=[.,\/#!$%\^&\*;:{}=\-_`~()])', '')"""
+
+ #for i, (text, label) in df_texts[['text', 'labels']].iterrows():
+ # assert text.split().__len__() == label.__len__(), f'Failed because of size mismatch on id: {i}. Shape mismatch {text.split().__len__(), label.__len__()}'
+
+
+ """ids = np.stack(df_texts.range)
+ print(ids.shape)
+ labels = np.stack(df_texts.labels)
+ print(labels.shape)
+ new_labels = np.stack(df_texts.labels)
+ print(new_labels.shape)
+
+ labels[ids] = new_labels
+
+ df_texts.labels = labels"""
+
+ return df_texts
+
+
+
+if __name__ == '__main__':
+ """PATH = '../../../data/kaggle/feedback-prize-2021/train'
+ FILE_PATH = '../../../data/kaggle/feedback-prize-2021/train.csv'
+
+ df_texts = create_labels_doc_level(PATH, FILE_PATH)
+ df_texts[['text', 'labels']].to_csv('../../../data/kaggle/df_cleaned.csv')"""
+
+ PATH = '../../../data/kaggle/df_cleaned.csv'
+ df = pd.read_csv(PATH, index_col=0)
+ df.labels = df.labels.apply(lambda x: [int(x_) for x_ in x[1:-1].split(', ')])
+
+ def extend_labels_based_on_tokenizer(words, labels, tokenizer):
+ tokens = []
+ new_labels = []
+
+ for word, label in zip(words.split(), labels):
+ tokenized_word = tokenizer.tokenize(word)
+ l_tokenized = len(tokenized_word)
+ tokens += tokenized_word
+ new_labels += [label] * l_tokenized
+
+ return tokens, new_labels
+
+
+
+ from transformers import BigBirdTokenizer
+
+ tokenizer = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-large')
+
+
+
+ sentence = 'Yousef Nami is from Iran'
+ labels = 'B-PERS I-PERS O O B-Place'
+ sentence = '_You sef _Na mi is from _Ir an'
+ labels = 'B_PERS B-PERS I-PERS I-PERS'
\ No newline at end of file
diff --git a/experiments/yousef/python_files/run.py b/experiments/yousef/python_files/run.py
new file mode 100644
index 0000000..6e90658
--- /dev/null
+++ b/experiments/yousef/python_files/run.py
@@ -0,0 +1,243 @@
+import torch
+import time
+from python_files.utils import _move # TODO move this to pytorch utils
+from colabtools.utils import get_gpu_utilization
+import pandas as pd
+
+
+
+if torch.cuda.is_available():
+ DEVICE = 'cuda'
+ print('CUDA device detected. Using GPU...')
+else:
+ DEVICE = 'cpu'
+ print('CUDA device NOT detected. Using CPU...')
+
+def train_longformer(model, optimizer, epochs, train_loader, val_loader=None, verbose=2):
+ """
+ Function to train longformer
+ :return:
+ """
+ model.to(DEVICE)
+ model.train()
+
+ for epoch in range(epochs):
+ #print(f'GPU Utilisation at epoch {epoch}: {get_gpu_utilization()}')
+ # set model to train mode
+ model.train()
+ start_epoch_message = f'EPOCH {epoch + 1} STARTED'
+ print(start_epoch_message)
+ print(f'{"-" * len(start_epoch_message)}')
+
+ start_epoch = time.time()
+ # TODO model does not currently support saving
+ start_load = time.time()
+
+ training_loss = 0
+ for i, (inputs, targets) in enumerate(train_loader):
+ start_train = time.time()
+ inputs = _move(inputs)
+ targets = _move(targets)
+ #print(f'GPU Utilisation at batch {i+1} after data loading: {get_gpu_utilization()}')
+
+ optimizer.zero_grad()
+
+ loss, outputs = model(
+ labels=targets,
+ input_ids=inputs['input_ids'],
+ attention_mask=inputs['attention_mask'],
+ return_dict=False
+ )
+ #print(f'GPU Utilisation at batch {i+1} after training: {get_gpu_utilization()}')
+
+ print(loss)
+ print(outputs, outputs.shape)
+ training_loss += loss.item()
+
+ """def active_logits(raw_logits, word_ids):
+ word_ids = word_ids.view(-1)
+ active_mask = word_ids.unsqueeze(1).expand(word_ids.shape[0], HyperParameters.num_labels)
+ active_mask = active_mask != NON_LABEL
+ active_logits = raw_logits.view(-1, HyperParameters.num_labels)
+ active_logits = torch.masked_select(active_logits, active_mask) # return 1dTensor
+ active_logits = active_logits.view(-1, HyperParameters.num_labels)
+ return active_logits"""
+
+ # TODO evaluate code here
+
+ # backward pass
+ optimizer.zero_grad()
+ loss.backward()
+ optimizer.step()
+
+ end_train = time.time()
+
+ if verbose > 1:
+ print(
+ f'Batch {i + 1} complete. Time taken: load({start_train - start_load:.3g}), '
+ f'train({end_train - start_train:.3g}), total({end_train - start_load:.3g}). '
+ )
+ start_load = time.time()
+
+
+def _get_zero_start_ids(iterable, split_by=[0]):
+ """ TODO run unit tests on this """
+
+ split_by = set(split_by)
+ add_item = float(iterable[0]) in split_by
+ zero_start_ids = []
+ for i, item in enumerate(iterable):
+ if (float(item) in split_by) ^ add_item:
+ add_item = not add_item
+ zero_start_ids.append(i)
+ return zero_start_ids
+
+def chunk_into_contiguous_segments(tensor, split_ids, ignore=False):
+ tensor_split = torch.tensor_split(tensor, split_ids)
+ if ignore:
+ tensor_split = [tensor_split for i, chunk in enumerate(tensor_split) if i%2]
+ return tensor_split
+
+
+def get_predicted_label(outputs):
+ probas, labels = outputs.max(dim=-1)
+ return probas, labels
+
+def _get_predictionString_from_tensors(word_id_masks, word_ids, outputs, strategy='first'):
+ """ Function to convert predicted token labels back into words """
+ # outputs is of size batch_size, max_length, n_labels
+ if strategy == 'first':
+ # also you need to know what output to be expected here, if it's gonna be
+ # diff strategies you might have to give the WHOLE tensor, not just the highest pred and class
+ probas, labels = get_predicted_label(outputs)
+ df = pd.concat([
+ pd.DataFrame({
+ 'probas': proba[word_id_mask],
+ 'labels': label[word_id_mask],
+ 'word_ids': word_id,
+ 'doc_ids': i
+ }).groupby('word_ids').head(1) for i, (proba, label, word_id, word_id_mask) in enumerate(zip(probas, labels, word_ids, word_id_masks))
+ ])
+
+ df = df.groupby('ids').agg({col: lambda x: x.values.flatten().tolist() for col in df.columns}).drop('ids', axis=1)
+ # also you need to convert back top pytorch tensors
+ df['split_ids'] = df.labels.apply(lambda x: _get_zero_start_ids(x))
+ df.probas = df[['probas', 'split_ids']].apply(lambda x: c)
+ # continue this...
+
+
+ else:
+ raise NotImplementedError(f'Strategy {strategy} has not yet been implemented.')
+ # needs to return tensor/df of following format
+ # TODO need multiple strategies for calculating the CLASS. This might depend on the tokenisation
+ # DOC_ID, CLASS, predictionString (word level)
+ # find location of first zero section
+ zero_start_ids = [
+ _get_zero_start_ids(arr) for arr in arrs
+ ]
+ pass
+
+def _calculate_f1_score():
+ pass
+
+def test(model, test_loader, verbose=2):
+ """
+ Performs inference on a trained model
+ :param model:
+ :param test_loader:
+ :param verbose:
+ :return:
+ """
+ # TODO give option not to use GPU, in fact maybe you can't even
+ model.to(DEVICE)
+ model.eval()
+ with torch.no_grad():
+ start_load = time.time()
+ for i, (inputs, targets) in enumerate(test_loader):
+
+ start_get_predString = time.time()
+
+ start_calculate_f1 = time.time()
+
+ end_prediction = time.time()
+ if verbose > 1:
+ print(
+ f'Batch {i + 1} complete. Time taken: '
+ f'load({start_get_predString - start_load:.3g}), '
+ f'calc_predString({start_calculate_f1 - start_get_predString:.3g}), '
+ f'calc_f1({end_prediction - start_calculate_f1}), '
+ f'total({end_prediction - start_load:.3g}). '
+ )
+ # needs to convert the inputs to word level
+ # needs to calculate F1 score
+ # needs to average across the whole thing
+ pass
+
+ return None
+
+
+def evaluate_simple(outputs, targets):
+ """ Function that evaluates model predictions at word level based on an F1 Metric
+ This is a simple evaluation that checks a document level
+ """
+ ids_equal = outputs == targets
+ overlap = (ids_equal.float()).mean(dim=1)
+ ids_tp = overlap >= 0.5
+
+ return (ids_equal.float()).mean(dim=1)
+
+
+def _word_labels_to_df(passage_prediction_list, doc_ids):
+ ids = []
+ classes = []
+ prediction_strings = []
+ for doc_id, passage_prediction in zip(doc_ids, passage_prediction_list):
+ pred_string_list = []
+ for prediction_string, label in enumerate(passage_prediction, 1):
+ if label != 0:
+ pred_string_list.append(prediction_string)
+ else:
+ if pred_string_list:
+ ids.append(doc_id)
+ classes.append(label)
+ prediction_strings.append(set(pred_string_list))
+ pred_string_list = []
+
+ return pd.DataFrame(data={'id': ids, 'class': classes, 'predictionString': prediction_strings})
+
+
+def evaluate(output_passage_prediction, target_passage_prediction):
+
+ df_outputs = _word_labels_to_df(output_passage_prediction)
+ df_targets = _word_labels_to_df(target_passage_prediction)
+
+ # assume you can get dataframes
+ df_targets = df_targets.merge(
+ df_outputs, on=['id', 'class']
+ )
+
+ # make sure class_y is the correct one! e.g. the ground truth
+ df_targets['overlap_pred_gt'] = df_targets[['class_x', 'class_y']].apply(
+ lambda x: x.class_x.intersection(x.class_y) / len(x.class_y)
+ )
+ df_targets['overlap_gt_pred'] = df_targets[['class_y', 'class_x']].apply(
+ lambda x: x.class_x.intersection(x.class_y) / len(x.class_x)
+
+ )
+
+ df_targets['tp'] = df_targets[['overlap_pred_gt', 'overlap_gt_pred']].apply(
+ lambda x: int(x.overlap_pred_gt >= 0.5 and x.overlap_gt_pred >= 0.5)
+ )
+ df_targets['overlap_sum'] = df_targets.overlap_pred_gt + df_targets.overlap_gt_pred
+ df_targets_match = df_targets[df_targets['tp'] == 1].groupby(
+ 'class_y'
+ ).agg({'overlap_sum':'max'}).drop(['overlap_gt_pred', 'overlap_pred_gt'])
+
+ TP = df_targets.shape[0]
+
+ df_targets_no_match = df_targets[df_targets['tp'] == 0]
+ FN = len(df_targets_no_match.class_y.unique())
+ FP = len(df_targets_no_match.class_x.unique())
+ pass
+
+
diff --git a/experiments/yousef/python_files/utils.py b/experiments/yousef/python_files/utils.py
new file mode 100644
index 0000000..450af05
--- /dev/null
+++ b/experiments/yousef/python_files/utils.py
@@ -0,0 +1,11 @@
+import torch
+
+def _move(data, device='cpu'):
+ if torch.is_tensor(data):
+ return data.to(device)
+ elif isinstance(data, dict):
+ return {key: tensor.to(device) for key, tensor in data.items()}
+ elif isinstance(data, list):
+ raise NotImplementedError('Currently no support for tensors stored in lists.')
+ else:
+ raise TypeError('Invalid data type.')
\ No newline at end of file
diff --git a/experiments/yousef/requirements.txt b/experiments/yousef/requirements.txt
new file mode 100644
index 0000000..1411a4a
--- /dev/null
+++ b/experiments/yousef/requirements.txt
@@ -0,0 +1 @@
+pandas
\ No newline at end of file
diff --git a/experiments/yousef/test.ipynb b/experiments/yousef/test.ipynb
new file mode 100644
index 0000000..ea158d3
--- /dev/null
+++ b/experiments/yousef/test.ipynb
@@ -0,0 +1,159 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "iqUtMCs82b4U",
+ "metadata": {
+ "id": "iqUtMCs82b4U"
+ },
+ "source": [
+ "# Test File"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "6wYF-VaoYc_H",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6wYF-VaoYc_H",
+ "outputId": "cee38613-c1c7-40c6-f224-d362cc466f7a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mounted at /content/drive\n",
+ "Drive successfully mounted.\n",
+ "Processing ./drive/MyDrive/argument-mining\n",
+ "\u001b[33m DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
+ " pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
+ "Building wheels for collected packages: ArgMiner\n",
+ " Building wheel for ArgMiner (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for ArgMiner: filename=ArgMiner-0.0.1-py3-none-any.whl size=1644 sha256=dade86318530e2b97f8cbe27762285b510ec9d1c7a3135c3a5e859a97af3d7d9\n",
+ " Stored in directory: /root/.cache/pip/wheels/b3/4a/60/7e7788d68cd98674da14cff4bcdd1bc397f8d6ca34967d1251\n",
+ "Successfully built ArgMiner\n",
+ "Installing collected packages: ArgMiner\n",
+ "Successfully installed ArgMiner-0.0.1\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from -r drive/MyDrive/argument-mining/experiments/yousef/requirements.txt (line 1)) (1.3.5)\n",
+ "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas->-r drive/MyDrive/argument-mining/experiments/yousef/requirements.txt (line 1)) (1.19.5)\n",
+ "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->-r drive/MyDrive/argument-mining/experiments/yousef/requirements.txt (line 1)) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->-r drive/MyDrive/argument-mining/experiments/yousef/requirements.txt (line 1)) (2018.9)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->-r drive/MyDrive/argument-mining/experiments/yousef/requirements.txt (line 1)) (1.15.0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "REPOSITORY_NAME = 'argument-mining'; YOUR_NAME = 'yousef'\n",
+ "import os\n",
+ "\n",
+ "try:\n",
+ " from google.colab import drive\n",
+ " drive.mount('/content/drive')\n",
+ " print('Drive successfully mounted.')\n",
+ " base_path = f'drive/MyDrive/{REPOSITORY_NAME}'\n",
+ " requirements_path = os.path.join(base_path, f'experiments/{YOUR_NAME}/requirements.txt')\n",
+ " !pip install $base_path\n",
+ " !pip install -r $requirements_path\n",
+ "except:\n",
+ " print('Google Drive import failed... continuing anyways...')\n",
+ " base_path = '../../'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "6Teo6X3OY_JG",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6Teo6X3OY_JG",
+ "outputId": "c264c4f4-9ae7-403f-8c55-0859258ced1d"
+ },
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'argminer'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m| \n", + " | label_type | \n", + "label | \n", + "text | \n", + "span_start | \n", + "span_end | \n", + "doc_id | \n", + "predictionString | \n", + "
|---|---|---|---|---|---|---|---|
| 0 | \n", + "O | \n", + "Other | \n", + "Should students be taught to compete or to coo... | \n", + "0 | \n", + "503 | \n", + "essay001 | \n", + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14... | \n", + "
| 1 | \n", + "T1 | \n", + "MajorClaim | \n", + "we should attach more importance to cooperatio... | \n", + "503 | \n", + "575 | \n", + "essay001 | \n", + "[83, 84, 85, 86, 87, 88, 89, 90, 91, 92] | \n", + "
| 2 | \n", + "O | \n", + "Other | \n", + ".\\nFirst of all, | \n", + "575 | \n", + "591 | \n", + "essay001 | \n", + "[93, 94, 95, 96] | \n", + "
| 3 | \n", + "T3 | \n", + "Claim | \n", + "through cooperation, children can learn about ... | \n", + "591 | \n", + "714 | \n", + "essay001 | \n", + "[97, 98, 99, 100, 101, 102, 103, 104, 105, 106... | \n", + "
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+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "62c6c2e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_texts.to_json(\n",
+ " os.path.join(base_path, 'df_texts.json')\n",
+ ")\n",
+ "\n",
+ "df_annotated.reset_index(drop=True).to_json(\n",
+ " os.path.join(base_path, 'df_full_annotated.json')\n",
+ ")"
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+ },
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+ "df_arguments = df_annotated[ids_argument_segment]\n",
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+ "id": "a5af3ba7",
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+ ]
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+ "metadata": {},
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+ }
+ ],
+ "source": [
+ "df_arguments = df_arguments.rename(columns={'label_comp1':'span_start', 'label_comp2':'span_end'}).astype(\n",
+ " {'span_start': int, 'span_end':int}\n",
+ ")\n",
+ "df_arguments.dtypes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "32191521",
+ "metadata": {},
+ "outputs": [
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+ "output_type": "stream",
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+ "murdering criminals is therefore immoral and hard to accept\n",
+ "\"murdering\" criminals is therefore immoral and hard to accept\n",
+ "\n"
+ ]
+ },
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+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_14116/2945358557.py:12: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_arguments['text'][df_arguments.text == text_segment] = text[span_start: span_end]\n",
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_14116/2945358557.py:12: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_arguments['text'][df_arguments.text == text_segment] = text[span_start: span_end]\n",
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_14116/2945358557.py:12: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_arguments['text'][df_arguments.text == text_segment] = text[span_start: span_end]\n"
+ ]
+ },
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+ "name": "stdout",
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+ "Click is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\"Click\" is a very interesting comedy, with a serious approach about the importance of having a balanced life between family and work businesses\n",
+ "\n",
+ "Blood diamond, an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\"Blood diamond\", an adaptation of a real story in South Africa, focuses on the link between diamonds and conflict\n",
+ "\n",
+ "rush hours are usually not the direct consequence of drivers\n",
+ "\"rush hours\" are usually not the direct consequence of drivers\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_6/scw_50_57tb8mmbpq6ylnl6w0000gn/T/ipykernel_14116/2945358557.py:12: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df_arguments['text'][df_arguments.text == text_segment] = text[span_start: span_end]\n"
+ ]
+ }
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+ "records = []\n",
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+ "for (doc_id, text) in df_texts.sort_values('doc_id').itertuples(index=False):\n",
+ " df_argument = df_arguments[df_arguments.doc_id == doc_id]\n",
+ " prev_span = 0\n",
+ " for i, (text_segment, span_start, span_end) in enumerate(df_argument[['text', 'span_start', 'span_end']].itertuples(index=False)):\n",
+ " try:\n",
+ " assert text_segment == text[span_start: span_end]\n",
+ " except Exception as e:\n",
+ " print(f'{text_segment}\\n{text[span_start: span_end]}\\n')\n",
+ " # all the exception were manually checked. These are because of qutoe chars, this is a hot fix.!!!! TODO\n",
+ " df_arguments['text'][df_arguments.text == text_segment] = text[span_start: span_end]\n",
+ " records.append(('O', 'Other', text[prev_span: span_start], prev_span, span_start, doc_id))\n",
+ " prev_span = span_end\n",
+ " records.append(('O', 'Other', text[prev_span:], prev_span, len(text), doc_id))"
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+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_combined = pd.concat([df_other, df_arguments])\n",
+ "df_combined = df_combined.sort_values(['doc_id', 'span_start', 'span_end']).reset_index(drop=True)\n",
+ "df_combined[df_combined.doc_id == 'essay001']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "22cecd2d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_combined.to_json(\n",
+ " os.path.join(base_path, 'df_with_other.json')\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "1bb4c0b1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "\"Should students be taught to compete or to cooperate?\\n\\nIt is always said that competition can effectively promote the development of economy. In order to survive in the competition, companies continue to improve their products and service, and as a result, the whole society prospers. However, when we discuss the issue of competition or cooperation, what we are concerned about is not the whole society, but the development of an individual's whole life. From this point of view, I firmly believe that we should attach more importance to cooperation during primary education.\\nFirst of all, through cooperation, children can learn about interpersonal skills which are significant in the future life of all students. What we acquired from team work is not only how to achieve the same goal with others but more importantly, how to get along with others. During the process of cooperation, children can learn about how to listen to opinions of others, how to communicate with others, how to think comprehensively, and even how to compromise with other team members when conflicts occurred. All of these skills help them to get on well with other people and will benefit them for the whole life.\\nOn the other hand, the significance of competition is that how to become more excellence to gain the victory. Hence it is always said that competition makes the society more effective. However, when we consider about the question that how to win the game, we always find that we need the cooperation. The greater our goal is, the more competition we need. Take Olympic games which is a form of competition for instance, it is hard to imagine how an athlete could win the game without the training of his or her coach, and the help of other professional staffs such as the people who take care of his diet, and those who are in charge of the medical care. The winner is the athlete but the success belongs to the whole team. Therefore without the cooperation, there would be no victory of competition.\\nConsequently, no matter from the view of individual development or the relationship between competition and cooperation we can receive the same conclusion that a more cooperative attitudes towards life is more profitable in one's success.\""
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_essay001 = df_combined[df_combined.doc_id == 'essay001']\n",
+ "''.join(df_essay001.text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "4ef11459",
+ "metadata": {},
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+ " 503, 575, 'essay001'], dtype=object)"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_essay001.values[1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "2f6939ea",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['O', 'Other', '.\\nFirst of all, ', 575, 591, 'essay001'],\n",
+ " dtype=object)"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_essay001.values[2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "a81018d4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from argminer.data import DataProcessor, TUDarmstadtProcessor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "id": "dfd6c728",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "'DataProcessor' object has no attribute '_preprocess'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [78]\u001b[0m, in \u001b[0;36m| \n", + " | label_type | \n", + "label | \n", + "text | \n", + "span_start | \n", + "span_end | \n", + "doc_id | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "O | \n", + "Other | \n", + "Should students be taught to compete or to coo... | \n", + "0 | \n", + "503 | \n", + "essay001 | \n", + "
| 1 | \n", + "T1 | \n", + "MajorClaim | \n", + "we should attach more importance to cooperatio... | \n", + "503 | \n", + "575 | \n", + "essay001 | \n", + "
| 2 | \n", + "O | \n", + "Other | \n", + ".\\nFirst of all, | \n", + "575 | \n", + "591 | \n", + "essay001 | \n", + "
| 3 | \n", + "T3 | \n", + "Claim | \n", + "through cooperation, children can learn about ... | \n", + "591 | \n", + "714 | \n", + "essay001 | \n", + "
| 4 | \n", + "O | \n", + "Other | \n", + ". | \n", + "714 | \n", + "716 | \n", + "essay001 | \n", + "
| 5 | \n", + "T4 | \n", + "Premise | \n", + "What we acquired from team work is not only ho... | \n", + "716 | \n", + "851 | \n", + "essay001 | \n", + "
| 6 | \n", + "O | \n", + "Other | \n", + ". | \n", + "851 | \n", + "853 | \n", + "essay001 | \n", + "
| 7 | \n", + "T5 | \n", + "Premise | \n", + "During the process of cooperation, children ca... | \n", + "853 | \n", + "1086 | \n", + "essay001 | \n", + "
| 8 | \n", + "O | \n", + "Other | \n", + ". | \n", + "1086 | \n", + "1088 | \n", + "essay001 | \n", + "
| 9 | \n", + "T6 | \n", + "Premise | \n", + "All of these skills help them to get on well w... | \n", + "1088 | \n", + "1191 | \n", + "essay001 | \n", + "
| 10 | \n", + "O | \n", + "Other | \n", + ".\\nOn the other hand, | \n", + "1191 | \n", + "1212 | \n", + "essay001 | \n", + "
| 11 | \n", + "T8 | \n", + "Premise | \n", + "the significance of competition is that how to... | \n", + "1212 | \n", + "1301 | \n", + "essay001 | \n", + "
| 12 | \n", + "O | \n", + "Other | \n", + ". Hence it is always said that | \n", + "1301 | \n", + "1332 | \n", + "essay001 | \n", + "
| 13 | \n", + "T7 | \n", + "Claim | \n", + "competition makes the society more effective | \n", + "1332 | \n", + "1376 | \n", + "essay001 | \n", + "
| 14 | \n", + "O | \n", + "Other | \n", + ". However, | \n", + "1376 | \n", + "1387 | \n", + "essay001 | \n", + "
| 15 | \n", + "T9 | \n", + "Premise | \n", + "when we consider about the question that how t... | \n", + "1387 | \n", + "1492 | \n", + "essay001 | \n", + "
| 16 | \n", + "O | \n", + "Other | \n", + ". The greater our goal is, the more competitio... | \n", + "1492 | \n", + "1549 | \n", + "essay001 | \n", + "
| 17 | \n", + "T10 | \n", + "Premise | \n", + "Take Olympic games which is a form of competit... | \n", + "1549 | \n", + "1846 | \n", + "essay001 | \n", + "
| 18 | \n", + "O | \n", + "Other | \n", + ". The winner is the athlete but the success be... | \n", + "1846 | \n", + "1927 | \n", + "essay001 | \n", + "
| 19 | \n", + "T11 | \n", + "Claim | \n", + "without the cooperation, there would be no vic... | \n", + "1927 | \n", + "1992 | \n", + "essay001 | \n", + "
| 20 | \n", + "O | \n", + "Other | \n", + ".\\nConsequently, no matter from the view of in... | \n", + "1992 | \n", + "2154 | \n", + "essay001 | \n", + "
| 21 | \n", + "T2 | \n", + "MajorClaim | \n", + "a more cooperative attitudes towards life is m... | \n", + "2154 | \n", + "2231 | \n", + "essay001 | \n", + "
| 22 | \n", + "O | \n", + "Other | \n", + ". | \n", + "2231 | \n", + "2232 | \n", + "essay001 | \n", + "