Question_Answering_uit_vquad.ipynb

{
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MOsHUjgdIrIW"
      },
      "outputs": [],
      "source": [
        "!pip install datasets transformers\n",
        "!pip install evaluate\n",
        "!apt install git-lfs\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "X00WJ3oo2j4V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3b44d3f-c1e3-4dba-ecd9-1ead81bdb0b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332,
          "referenced_widgets": [
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            "0c49f225df2c40f095be5c2b44f2a9b2",
            "3298cb2427274425be1dc454ebdf3e8f"
          ]
        },
        "id": "fSHETU42GNMT",
        "outputId": "66782f13-3ae8-4c34-8ae1-37f2f162984c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "ENbIYyjNGNMU"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "import json\n",
        "from collections import namedtuple\n",
        "from random import shuffle\n",
        "import datasets\n",
        "import pandas as pd\n",
        "import evaluate\n",
        "from tqdm import tqdm \n",
        "from datasets import load_dataset, load_metric"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rEJBSTyZIrIb"
      },
      "source": [
        "# Fine-tuning a model on a question-answering task"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "zVvslsfMIrIh"
      },
      "outputs": [],
      "source": [
        "squad_v2 = False\n",
        "model_checkpoint = \"xlm-roberta-base\"\n",
        "batch_size = 16"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "whPRbBNbIrIl"
      },
      "source": [
        "## Loading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_question_agument():\n",
        "    qs = []\n",
        "    with open(f\"/content/drive/MyDrive/Colab/UIT_vQuaD/augument.txt\",'r') as f:\n",
        "        for line in f:\n",
        "          qs.append(line[:-1])\n",
        "    return qs \n",
        "qs = load_question_agument()\n",
        "qs[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQCAxz0dpS4r",
        "outputId": "30ce01fe-970d-4b10-e279-8c2804e773c6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Phạm Văn Đồng đã sử dụng tên gì khi còn là phó giám đốc Sở Tư pháp ở Quế Lâm?',\n",
              " 'Phạm Văn Đồng giữ chức vụ gì trong bộ máy nhà nước CHXHCNVN?',\n",
              " 'Từ 1955 đến 1976, Phạm Văn Đồng giữ chức vụ gì?']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sacrebleu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0M-sYrFRwKlB",
        "outputId": "5b751812-0da5-421f-e282-9268629421b2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sacrebleu\n",
            "  Downloading sacrebleu-2.3.1-py3-none-any.whl (118 kB)\n",
            "\u001b[K     |████████████████████████████████| 118 kB 36.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (1.21.6)\n",
            "Collecting portalocker\n",
            "  Downloading portalocker-2.6.0-py2.py3-none-any.whl (15 kB)\n",
            "Collecting colorama\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (0.8.10)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (4.9.1)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (2022.6.2)\n",
            "Installing collected packages: portalocker, colorama, sacrebleu\n",
            "Successfully installed colorama-0.4.6 portalocker-2.6.0 sacrebleu-2.3.1\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "bleu = evaluate.load(\"sacrebleu\")\n",
        "def bleu_2_sent(s,t):\n",
        "  results = bleu.compute(predictions=[s], references=[[t]])\n",
        "  # print(results)\n",
        "  return results['score']\n",
        "\n",
        "bleu_2_sent('toi la chieu ddd','toi la chieu a b c')"
      ],
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        "id": "Lfs7F-6nr1KL",
        "outputId": "6ad6dd37-1685-4a8a-b278-c61dcf52c196"
      },
      "execution_count": 10,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/8.15k [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "OxSCMIMx8-Ei",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5107b49c-eb60-4eb5-f996-7fdb36c1e504"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        }
      ],
      "source": [
        "\n",
        "def load_train_data(name):\n",
        "\n",
        "  data = []\n",
        "\n",
        "  dirs = [\"/content/drive/MyDrive/Colab/UIT_vQuaD/dev-context-en-question-vi.json\", \n",
        "          \"/content/drive/MyDrive/Colab/UIT_vQuaD/dev-context-vi-question-en.json\",\n",
        "          \"/content/drive/MyDrive/Colab/UIT_vQuaD/test-context-en-question-vi.json\",\n",
        "          \"/content/drive/MyDrive/Colab/UIT_vQuaD/test-context-vi-question-en.json\"\n",
        "          ]\n",
        "  for dir in dirs :\n",
        "      with open(dir,'r') as f:\n",
        "           tmp  = json.load(f)\n",
        "           for i in tqdm(range(len(tmp['data']))):\n",
        "                o = tmp['data'][i]\n",
        "                title = tmp['data'][i]['title']\n",
        "                n = len(tmp['data'][i]['paragraphs'])\n",
        "                for j in range(n):\n",
        "                    context = tmp['data'][i]['paragraphs'][j]['context']\n",
        "                    qas = tmp['data'][i]['paragraphs'][j]['qas']\n",
        "                    for qa in qas:\n",
        "                        question = qa['question']\n",
        "                        id = qa['id'] \n",
        "                        # print(qa['answers'])\n",
        "                        if len(qa['answers']) == 0:\n",
        "                            continue\n",
        "                        answer_start = qa['answers'][0]['answer_start']\n",
        "                        answer_text = qa['answers'][0]['text']\n",
        "                        \n",
        "                        data.append({\n",
        "                                'id':id,\n",
        "                                'title':title,\n",
        "                                'context': context,\n",
        "                                'question': question ,\n",
        "                                'answers':{\n",
        "                                    'text':[answer_text],\n",
        "                                    'answer_start':[answer_start]\n",
        "                                }\n",
        "                            })\n",
        "\n",
        "  tmp = []\n",
        "  with open(f\"/content/drive/MyDrive/Colab/UIT_vQuaD/{name}.json\",'r') as f:\n",
        "       tmp  = json.load(f)\n",
        "\n",
        "  \n",
        "  cnt = 0 \n",
        "  for i in tqdm(range(len(tmp['data']))):\n",
        "      o = tmp['data'][i]\n",
        "      title = tmp['data'][i]['title']\n",
        "      n = len(tmp['data'][i]['paragraphs'])\n",
        "\n",
        "      for j in range(n):\n",
        "          context = tmp['data'][i]['paragraphs'][j]['context']\n",
        "          qas = tmp['data'][i]['paragraphs'][j]['qas']\n",
        "          for qa in qas:\n",
        "              question = qa['question']\n",
        "              id = qa['id'] \n",
        "              # print(qa['answers'])\n",
        "              if len(qa['answers']) == 0:\n",
        "                  continue\n",
        "              answer_start = qa['answers'][0]['answer_start']\n",
        "              answer_text = qa['answers'][0]['text']\n",
        "              \n",
        "              data.append({\n",
        "                      'id':id,\n",
        "                      'title':title,\n",
        "                      'context': context,\n",
        "                      'question': question ,\n",
        "                      'answers':{\n",
        "                          'text':[answer_text],\n",
        "                          'answer_start':[answer_start]\n",
        "                      }\n",
        "                  })\n",
        "              \n",
        "              # agument data \n",
        "              bleu_score = bleu_2_sent(qs[cnt],question)\n",
        "              if 15 <= bleu_score and bleu_score <= 95:\n",
        "                data.append({\n",
        "                        'id':id,\n",
        "                        'title':title,\n",
        "                        'context': context,\n",
        "                        'question': qs[cnt] ,\n",
        "                        'answers':{\n",
        "                            'text':[answer_text],\n",
        "                            'answer_start':[answer_start]\n",
        "                        }\n",
        "                    })\n",
        "              cnt += 1\n",
        "  tdata = pd.DataFrame(data)\n",
        "  tdata = datasets.Dataset.from_pandas(tdata)\n",
        "  return tdata\n",
        "\n",
        "data_train = load_train_data('train')\n",
        "# data_test = load_train_data('test')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTxpO-5Ipreq",
        "outputId": "df5660b4-fbfa-4d3f-afbd-7eac09087a8c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "    num_rows: 44326\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_test_data(name):\n",
        "  with open(f\"/content/drive/MyDrive/Colab/UIT_vQuaD/{name}.json\",'r') as f:\n",
        "       tmp  = json.load(f)\n",
        "  data = []\n",
        "  for i in range(len(tmp['data'])):\n",
        "      o = tmp['data'][i]\n",
        "      title = tmp['data'][i]['title']\n",
        "      n = len(tmp['data'][i]['paragraphs'])\n",
        "\n",
        "      for j in range(n):\n",
        "          context = tmp['data'][i]['paragraphs'][j]['context']\n",
        "          qas = tmp['data'][i]['paragraphs'][j]['qas']\n",
        "          for qa in qas:\n",
        "              question = qa['question']\n",
        "              id = qa['id'] \n",
        "              # print(qa['answers'])\n",
        "              if len(qa['answers']) == 0:\n",
        "                  continue\n",
        "              answer_start = []\n",
        "              answer_text = []\n",
        "              for t in range(len(qa['answers'])):\n",
        "                  answer_start.append(qa['answers'][t]['answer_start'])\n",
        "                  answer_text.append(qa['answers'][t]['text'])\n",
        "              data.append({\n",
        "                  'id':id,\n",
        "                  'title':title,\n",
        "                  'context': context,\n",
        "                  'question': question ,\n",
        "                  'answers':{\n",
        "                          'text':answer_text,\n",
        "                          'answer_start':answer_start\n",
        "                      }\n",
        "                  \n",
        "              })\n",
        "  tdata = pd.DataFrame(data)\n",
        "  tdata = datasets.Dataset.from_pandas(tdata)\n",
        "  return tdata\n",
        "\n",
        "data_test = load_test_data('test')\n"
      ],
      "metadata": {
        "id": "1pRlg0QNhmM1"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_train = data_train.shuffle(seed=42)"
      ],
      "metadata": {
        "id": "cgE56tdmiLO8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "dqD0EvznTONq"
      },
      "outputs": [],
      "source": [
        "DATA = namedtuple('data', ['train', 'test',])\n",
        "data = DATA(data_train,data_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9qywopnIrJH"
      },
      "source": [
        "## Preprocessing the training data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
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              "model_id": "1838f24bb8aa4b00bafa2be27448daec"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "pgPS82OBGNMa"
      },
      "outputs": [],
      "source": [
        "max_length = 384 # The maximum length of a feature (question and context)\n",
        "doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.\n",
        "pad_on_right = tokenizer.padding_side == \"right\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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          ]
        },
        "id": "daEZhxSYGNMf",
        "outputId": "67d5b184-4514-49ea-c0bc-bf144feffae3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/45 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70b71c744367412a963c1b1a6b13495c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "bb7f7717c1724d95ae0184a27fd2d696"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "def prepare_train_features(examples):\n",
        "    # Some of the questions have lots of whitespace on the left, which is not useful and will make the\n",
        "    # truncation of the context fail (the tokenized question will take a lots of space). So we remove that\n",
        "    # left whitespace\n",
        "    examples[\"question\"] = [q.lstrip() for q in examples[\"question\"]]\n",
        "\n",
        "    # Tokenize our examples with truncation and padding, but keep the overflows using a stride. This results\n",
        "    # in one example possible giving several features when a context is long, each of those features having a\n",
        "    # context that overlaps a bit the context of the previous feature.\n",
        "    tokenized_examples = tokenizer(\n",
        "        examples[\"question\" if pad_on_right else \"context\"],\n",
        "        examples[\"context\" if pad_on_right else \"question\"],\n",
        "        truncation=\"only_second\" if pad_on_right else \"only_first\",\n",
        "        max_length=max_length,\n",
        "        stride=doc_stride,\n",
        "        return_overflowing_tokens=True,\n",
        "        return_offsets_mapping=True,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "\n",
        "    # Since one example might give us several features if it has a long context, we need a map from a feature to\n",
        "    # its corresponding example. This key gives us just that.\n",
        "    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n",
        "    # The offset mappings will give us a map from token to character position in the original context. This will\n",
        "    # help us compute the start_positions and end_positions.\n",
        "    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n",
        "\n",
        "    # Let's label those examples!\n",
        "    tokenized_examples[\"start_positions\"] = []\n",
        "    tokenized_examples[\"end_positions\"] = []\n",
        "\n",
        "    for i, offsets in enumerate(offset_mapping):\n",
        "        # We will label impossible answers with the index of the CLS token.\n",
        "        input_ids = tokenized_examples[\"input_ids\"][i]\n",
        "        cls_index = input_ids.index(tokenizer.cls_token_id)\n",
        "\n",
        "        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\n",
        "        sequence_ids = tokenized_examples.sequence_ids(i)\n",
        "\n",
        "        # One example can give several spans, this is the index of the example containing this span of text.\n",
        "        sample_index = sample_mapping[i]\n",
        "        answers = examples[\"answers\"][sample_index]\n",
        "        # If no answers are given, set the cls_index as answer.\n",
        "        if len(answers[\"answer_start\"]) == 0:\n",
        "            tokenized_examples[\"start_positions\"].append(cls_index)\n",
        "            tokenized_examples[\"end_positions\"].append(cls_index)\n",
        "        else:\n",
        "            # Start/end character index of the answer in the text.\n",
        "            start_char = answers[\"answer_start\"][0]\n",
        "            end_char = start_char + len(answers[\"text\"][0])\n",
        "\n",
        "            # Start token index of the current span in the text.\n",
        "            token_start_index = 0\n",
        "            while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\n",
        "                token_start_index += 1\n",
        "\n",
        "            # End token index of the current span in the text.\n",
        "            token_end_index = len(input_ids) - 1\n",
        "            while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\n",
        "                token_end_index -= 1\n",
        "\n",
        "            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\n",
        "            if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\n",
        "                tokenized_examples[\"start_positions\"].append(cls_index)\n",
        "                tokenized_examples[\"end_positions\"].append(cls_index)\n",
        "            else:\n",
        "                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\n",
        "                # Note: we could go after the last offset if the answer is the last word (edge case).\n",
        "                while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\n",
        "                    token_start_index += 1\n",
        "                tokenized_examples[\"start_positions\"].append(token_start_index - 1)\n",
        "                while offsets[token_end_index][1] >= end_char:\n",
        "                    token_end_index -= 1\n",
        "                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\n",
        "\n",
        "    return tokenized_examples\n",
        "\n",
        "tokenized_train = data.train.map(prepare_train_features, batched=True, remove_columns=data.train.column_names)\n",
        "tokenized_test = data.test.map(prepare_train_features, batched=True, remove_columns=data.test.column_names)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9TnjmWfmN6g",
        "outputId": "106f7418-b58b-4bdf-852d-f0c2abc8507d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "data(train=Dataset({\n",
              "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "    num_rows: 44326\n",
              "}), test=Dataset({\n",
              "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "    num_rows: 2210\n",
              "}))"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "a90d7e04f8b74a33a8585673bbaafedd",
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            "77b4237ed7d2473fba16882cccb48df8",
            "8c6323f45b2f4d0c8aaf5de061391426"
          ]
        },
        "id": "-TohBOlvf4Sx",
        "outputId": "bd3eaf6d-2837-4e1d-c759-331b2ac2ce06"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "a90d7e04f8b74a33a8585673bbaafedd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def prepare_validation_features(examples):\n",
        "    # Some of the questions have lots of whitespace on the left, which is not useful and will make the\n",
        "    # truncation of the context fail (the tokenized question will take a lots of space). So we remove that\n",
        "    # left whitespace\n",
        "    examples[\"question\"] = [q.lstrip() for q in examples[\"question\"]]\n",
        "\n",
        "    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\n",
        "    # in one example possible giving several features when a context is long, each of those features having a\n",
        "    # context that overlaps a bit the context of the previous feature.\n",
        "    tokenized_examples = tokenizer(\n",
        "        examples[\"question\" if pad_on_right else \"context\"],\n",
        "        examples[\"context\" if pad_on_right else \"question\"],\n",
        "        truncation=\"only_second\" if pad_on_right else \"only_first\",\n",
        "        max_length=max_length,\n",
        "        stride=doc_stride,\n",
        "        return_overflowing_tokens=True,\n",
        "        return_offsets_mapping=True,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "\n",
        "    # Since one example might give us several features if it has a long context, we need a map from a feature to\n",
        "    # its corresponding example. This key gives us just that.\n",
        "    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n",
        "\n",
        "    # We keep the example_id that gave us this feature and we will store the offset mappings.\n",
        "    tokenized_examples[\"example_id\"] = []\n",
        "\n",
        "    for i in range(len(tokenized_examples[\"input_ids\"])):\n",
        "        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\n",
        "        sequence_ids = tokenized_examples.sequence_ids(i)\n",
        "        context_index = 1 if pad_on_right else 0\n",
        "\n",
        "        # One example can give several spans, this is the index of the example containing this span of text.\n",
        "        sample_index = sample_mapping[i]\n",
        "        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\n",
        "\n",
        "        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token\n",
        "        # position is part of the context or not.\n",
        "        tokenized_examples[\"offset_mapping\"][i] = [\n",
        "            (o if sequence_ids[k] == context_index else None)\n",
        "            for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\n",
        "        ]\n",
        "\n",
        "    return tokenized_examples\n",
        "tokenized_test_prepared = data.test.map(prepare_validation_features, batched=True, remove_columns=data.test.column_names)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLlQKmnbm4Kl",
        "outputId": "0179420f-d5cb-4543-ff1f-e227466bda05"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'attention_mask', 'start_positions', 'end_positions'],\n",
              "    num_rows: 50611\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "tokenized_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "545PP3o8IrJV"
      },
      "source": [
        "## Fine-tuning the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159,
          "referenced_widgets": [
            "0f74f34a708a430399714c6ce5da0b69",
            "fc8276d04e4244c2bd15931160b824d8",
            "9c7de3cf61fe4979bf266074b71253ab",
            "7fe5ee7505b54d92b05d52a1d94979bc",
            "169c248a21564132af273ecfa031b3c8",
            "3cc34977c5c144f492e425452ddad581",
            "5a81020104d24ca6bdc6fa1dd16a0649",
            "35384ae06dbf47258aedafa49bdf4021",
            "81bb7d67eef94a30814447ad9c1fdf70",
            "85e79fe1e286403bb5ee48af43471ca0",
            "e0fe418be4bf46b89e149e63e57d8a71"
          ]
        },
        "id": "TlqNaB8jIrJW",
        "outputId": "1ae4805b-9c7f-4c15-9a8f-758c1cc9b10f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f74f34a708a430399714c6ce5da0b69"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForQuestionAnswering: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias']\n",
            "- This IS expected if you are initializing XLMRobertaForQuestionAnswering 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 XLMRobertaForQuestionAnswering 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 XLMRobertaForQuestionAnswering were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['qa_outputs.weight', 'qa_outputs.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": [
        "from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer\n",
        "model = AutoModelForQuestionAnswering.from_pretrained(\"xlm-roberta-base\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "9TqCPaDqdpQ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "2ec28932b52d448e93cd4cc8849ca25c",
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            "fd1fc735a9cf457d82a99992d4c618ba",
            "0720e897e1724aba81ac799c7ea94619"
          ]
        },
        "outputId": "0acfcbea-c50c-49e1-e908-461fe7397aa8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.53k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ec28932b52d448e93cd4cc8849ca25c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/3.32k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ea338b836f74b6aa35ff9d159be8d59"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import evaluate\n",
        "metric = evaluate.load(\"squad\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "Yz1vCUjncLKq"
      },
      "outputs": [],
      "source": [
        "import collections \n",
        "import numpy as np\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "n_best=20 \n",
        "max_answer_length = 30\n",
        "examples = data.test\n",
        "features = tokenized_test_prepared\n",
        "\n",
        "def compute_metrics(evals):\n",
        "    (start_logits, end_logits) , _ = evals\n",
        "    # print(start_logits)\n",
        "    # print('-'*100)\n",
        "    example_to_features = collections.defaultdict(list)\n",
        "    for idx, feature in enumerate(features):\n",
        "        example_to_features[feature[\"example_id\"]].append(idx)\n",
        "\n",
        "    predicted_answers = []\n",
        "    for example in tqdm(examples):\n",
        "        example_id = example[\"id\"]\n",
        "        context = example[\"context\"]\n",
        "        answers = []\n",
        "\n",
        "        # Loop through all features associated with that example\n",
        "        for feature_index in example_to_features[example_id]:\n",
        "            start_logit = start_logits[feature_index]\n",
        "            end_logit = end_logits[feature_index]\n",
        "            offsets = features[feature_index][\"offset_mapping\"]\n",
        "\n",
        "            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
        "            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
        "            for start_index in start_indexes:\n",
        "                for end_index in end_indexes:\n",
        "                    # Skip answers that are not fully in the context\n",
        "                    if offsets[start_index] is None or offsets[end_index] is None:\n",
        "                        continue\n",
        "                    # Skip answers with a length that is either < 0 or > max_answer_length\n",
        "                    if (\n",
        "                        end_index < start_index\n",
        "                        or end_index - start_index + 1 > max_answer_length\n",
        "                    ):\n",
        "                        continue\n",
        "\n",
        "                    answer = {\n",
        "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
        "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
        "                    }\n",
        "                    answers.append(answer)\n",
        "\n",
        "        # Select the answer with the best score\n",
        "        if len(answers) > 0:\n",
        "            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
        "            predicted_answers.append(\n",
        "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
        "            )\n",
        "        else:\n",
        "            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
        "\n",
        "    theoretical_answers = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n",
        "    return metric.compute(predictions=predicted_answers, references=theoretical_answers)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJbmjTg4GNMh"
      },
      "source": [
        "Then we will need a data collator that will batch our processed examples together, here the default one will work:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "_zwcYs9Jew4y"
      },
      "outputs": [],
      "source": [
        "total = len(data.train)\n",
        "batchsz = 16 \n",
        "st = int(total / 64)\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "args = TrainingArguments(\n",
        "    f\"./{model_name}-finetuned-squad-4\",\n",
        "    evaluation_strategy = \"steps\",\n",
        "    logging_strategy = \"steps\",\n",
        "    eval_steps=st,\n",
        "    logging_steps=st,\n",
        "    save_steps=st,\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=batchsz,\n",
        "    per_device_eval_batch_size=batchsz,\n",
        "    gradient_accumulation_steps=1,\n",
        "    num_train_epochs=6,\n",
        "    weight_decay=0.01,\n",
        "    # logging_steps=2,\n",
        "    metric_for_best_model='f1',\n",
        "    save_total_limit=10,\n",
        "    push_to_hub=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "args"
      ],
      "metadata": {
        "id": "oFoFqboob8RG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "PNW3dqXsGNMh"
      },
      "outputs": [],
      "source": [
        "from transformers import default_data_collator\n",
        "data_collator = default_data_collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nqGq6V6qK85e"
      },
      "outputs": [],
      "source": [
        "import gc\n",
        "gc.collect()\n",
        "trainer = None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rWBNqHeXL0fA"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "s2A4vhIbUvWp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 908,
          "referenced_widgets": [
            "304d23dbd4bf4b74b841f2b5589809ad",
            "e1eb9e000fd740bfb2185cc4ff8ce821",
            "ef67495985b34d56bc6746eae53ecdaf",
            "4636efdadbae4fdc963f2eeb3186b6de",
            "5f9901aeb1d8432cae9ccd60e5ae03bb",
            "7f99dff88caa482985633f92a2a90bc4",
            "65558162e8a54da59a052c4f49de263f",
            "aaee453bde2246ee806d2f076b6141ff",
            "2f74200b93f94caca01aba4408bf88be",
            "d026f9b191664a7bb66f442e9209135b",
            "9aa2d50e720a48b0a51312231f860b3e",
            "1caaaecbabba43edac97ee30f0f97057",
            "f0e5cbf228c849e3a2274b00285cb224",
            "2daa5c88c3ad4a73ac7d9b5941e5fcb7",
            "9f7ee61d01d44cccb82755c89fe53696",
            "d1ff63fe99a74afaafdfaf7ed24986e3",
            "0b5cad556a07436d903835c6aac40894",
            "a74bc0d955a2444a81a7e836435ec58f",
            "fc662d2d6840430dad6d6b2586a803a2",
            "951acd2f91134ab2b609388d5aa813ee",
            "1e3c28911f4f4680ae389ab8fba04ffd",
            "f6f2057afd9f447186edeb63abe22dcf"
          ]
        },
        "id": "imY1oC3SIrJf",
        "outputId": "e7a40bf6-28b3-4f05-f01a-8cf3166afdda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/repository.py:729: FutureWarning: Creating a repository through 'clone_from' is deprecated and will be removed in v0.12. Please create the repository first using `create_repo(..., exists_ok=True)`.\n",
            "  FutureWarning,\n",
            "Cloning https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad-4 into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad-4 into local empty directory.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 50611\n",
            "  Num Epochs = 6\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 18984\n",
            "  Number of trainable parameters = 277454594\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2077' max='18984' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 2077/18984 47:38 < 6:28:12, 0.73 it/s, Epoch 0.66/6]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Exact Match</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>692</td>\n",
              "      <td>2.830200</td>\n",
              "      <td>1.767404</td>\n",
              "      <td>48.371041</td>\n",
              "      <td>69.385328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1384</td>\n",
              "      <td>1.750100</td>\n",
              "      <td>1.443659</td>\n",
              "      <td>56.968326</td>\n",
              "      <td>75.988236</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "    <div>\n",
              "      \n",
              "      <progress value='47' max='148' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 47/148 00:19 < 00:42, 2.40 it/s]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2359\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2210 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "304d23dbd4bf4b74b841f2b5589809ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to ./xlm-roberta-base-finetuned-squad-4/checkpoint-692\n",
            "Configuration saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-692/config.json\n",
            "Model weights saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-692/pytorch_model.bin\n",
            "tokenizer config file saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-692/tokenizer_config.json\n",
            "Special tokens file saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-692/special_tokens_map.json\n",
            "tokenizer config file saved in ./xlm-roberta-base-finetuned-squad-4/tokenizer_config.json\n",
            "Special tokens file saved in ./xlm-roberta-base-finetuned-squad-4/special_tokens_map.json\n",
            "Adding files tracked by Git LFS: ['tokenizer.json']. This may take a bit of time if the files are large.\n",
            "WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['tokenizer.json']. This may take a bit of time if the files are large.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2359\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2210 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1caaaecbabba43edac97ee30f0f97057"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to ./xlm-roberta-base-finetuned-squad-4/checkpoint-1384\n",
            "Configuration saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-1384/config.json\n",
            "Model weights saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-1384/pytorch_model.bin\n",
            "tokenizer config file saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-1384/tokenizer_config.json\n",
            "Special tokens file saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-1384/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2359\n",
            "  Batch size = 16\n"
          ]
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=tokenized_train,\n",
        "    eval_dataset=tokenized_test,\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForQuestionAnswering.from_pretrained(\"xlm-roberta-base-finetuned-squad_au/checkpoint-4000\")\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "args = TrainingArguments(\n",
        "    f\"{model_name}-finetuned-squad_au\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    logging_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=5,\n",
        "    weight_decay=0.01,\n",
        "    logging_steps=2,\n",
        "    metric_for_best_model='f1',\n",
        "    save_total_limit=4,\n",
        "    push_to_hub=True,\n",
        ")\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_train,\n",
        "    eval_dataset=tokenized_test,\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")\n"
      ],
      "metadata": {
        "id": "p5vU4R7LKHDL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "849e0e85-827c-48dd-bb73-2e84465cb208"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file xlm-roberta-base-finetuned-squad_au/checkpoint-4000/config.json\n",
            "Model config XLMRobertaConfig {\n",
            "  \"_name_or_path\": \"xlm-roberta-base-finetuned-squad_au/checkpoint-4000\",\n",
            "  \"architectures\": [\n",
            "    \"XLMRobertaForQuestionAnswering\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"xlm-roberta\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.24.0\",\n",
            "  \"type_vocab_size\": 1,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 250002\n",
            "}\n",
            "\n",
            "loading weights file xlm-roberta-base-finetuned-squad_au/checkpoint-4000/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing XLMRobertaForQuestionAnswering.\n",
            "\n",
            "All the weights of XLMRobertaForQuestionAnswering were initialized from the model checkpoint at xlm-roberta-base-finetuned-squad_au/checkpoint-4000.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use XLMRobertaForQuestionAnswering for predictions without further training.\n",
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
            "/content/xlm-roberta-base-finetuned-squad_au is already a clone of https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "WARNING:huggingface_hub.repository:/content/xlm-roberta-base-finetuned-squad_au is already a clone of https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au. Make sure you pull the latest changes with `repo.git_pull()`.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(commit_message=\"Training complete baseline\")"
      ],
      "metadata": {
        "id": "aNYuF0wwKecb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683,
          "referenced_widgets": [
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            "c15f41c1b2964c158d48222e49215591",
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            "6763d78bb520461e92ad278b7b7a0ab7",
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            "29007ef6348541d180c9b31ee0c44f80",
            "d7bbefd0a81b446fb0aa8a7d9c70a6e5",
            "22aa5fd8ffd945b89e5bb342dcc8dca3",
            "df7dceda2f9843a9a977cba8a2afb367",
            "f8717eccaff94a7681b9fb084b27c913",
            "002cd9d24f2d4c39801146a0bae7210a",
            "96916d3a68ab4ab88b6af00199b104e7",
            "d40db84d2d2048138315502e5f104b2b",
            "0536df16611a46e291639cdb95747244"
          ]
        },
        "outputId": "ff366267-f9f1-4a2c-eee4-3f0b872f0640"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to xlm-roberta-base-finetuned-squad_au\n",
            "Configuration saved in xlm-roberta-base-finetuned-squad_au/config.json\n",
            "Model weights saved in xlm-roberta-base-finetuned-squad_au/pytorch_model.bin\n",
            "tokenizer config file saved in xlm-roberta-base-finetuned-squad_au/tokenizer_config.json\n",
            "Special tokens file saved in xlm-roberta-base-finetuned-squad_au/special_tokens_map.json\n",
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.33k/1.03G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e4a4b1a3b92c431caa561303449ab59f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file training_args.bin:  99%|#########9| 3.34k/3.36k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a131c1a367e3484c81409e30633fc97a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/Nov21_15-19-13_391905903de9/events.out.tfevents.1669043963.391905903de9.79.0:  64%|######3   …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6763d78bb520461e92ad278b7b7a0ab7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "remote: Scanning LFS files for validity, may be slow...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au\n",
            "   3aad80f..c46c2b4  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:remote: Scanning LFS files for validity, may be slow...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au\n",
            "   3aad80f..c46c2b4  main -> main\n",
            "\n",
            "Dropping the following result as it does not have all the necessary fields:\n",
            "{'task': {'name': 'Question Answering', 'type': 'question-answering'}}\n",
            "To https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au\n",
            "   c46c2b4..58475d8  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au\n",
            "   c46c2b4..58475d8  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au/commit/c46c2b4af346673e68b62f1533d6e9ebb6a84f05'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdzABDVcIrJg"
      },
      "source": [
        "We can now finetune our model by just calling the `train` method:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xz0HMpBoGNMi"
      },
      "source": [
        "Since this training is particularly long, let's save the model just in case we need to restart."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SWK3JrSBGNMi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1661e51-a1d9-4eba-b301-dc55ee3941bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/drive/MyDrive/Colab/UIT_vQuaD/model\n",
            "Configuration saved in /content/drive/MyDrive/Colab/UIT_vQuaD/model/config.json\n",
            "Model weights saved in /content/drive/MyDrive/Colab/UIT_vQuaD/model/pytorch_model.bin\n",
            "tokenizer config file saved in /content/drive/MyDrive/Colab/UIT_vQuaD/model/tokenizer_config.json\n",
            "Special tokens file saved in /content/drive/MyDrive/Colab/UIT_vQuaD/model/special_tokens_map.json\n",
            "Saving model checkpoint to xlm-roberta-base-finetuned-squad_1\n",
            "Configuration saved in xlm-roberta-base-finetuned-squad_1/config.json\n",
            "Model weights saved in xlm-roberta-base-finetuned-squad_1/pytorch_model.bin\n",
            "tokenizer config file saved in xlm-roberta-base-finetuned-squad_1/tokenizer_config.json\n",
            "Special tokens file saved in xlm-roberta-base-finetuned-squad_1/special_tokens_map.json\n",
            "Dropping the following result as it does not have all the necessary fields:\n",
            "{'task': {'name': 'Question Answering', 'type': 'question-answering'}}\n"
          ]
        }
      ],
      "source": [
        "trainer.save_model(\"/content/drive/MyDrive/Colab/UIT_vQuaD/model\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# Replace this with your own checkpoint\n",
        "model_checkpoint = \"chieunq/xlm-roberta-base-finetuned-squad-2\"\n",
        "question_answerer = pipeline(\"question-answering\", model=model_checkpoint)\n",
        "\n",
        "context = \"\"\"\n",
        "Nhóm của chúng tôi là sinh viên năm 4 trường ĐH Công Nghệ - ĐHQG Hà Nội. Nhóm gồm 3 thành viên : Nguyễn Quang Chiều, Nguyễn Quang Huy và Nguyễn Trần Anh Đức.\\\n",
        " Đây là pha Reader trong dự án cuồi kì môn Các vấn đề hiện đại trong CNTT của nhóm . \n",
        "\"\"\"\n",
        "question = \"3 thành viên trong nhóm gồm những ai ?\"\n",
        "question_answerer(question=question, context=context)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWv5EyQCXPuW",
        "outputId": "aeed3bc1-d96b-42df-c339-512db4b71533"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/config.json\n",
            "Model config XLMRobertaConfig {\n",
            "  \"_name_or_path\": \"chieunq/xlm-roberta-base-finetuned-squad-2\",\n",
            "  \"architectures\": [\n",
            "    \"XLMRobertaForQuestionAnswering\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"xlm-roberta\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.24.0\",\n",
            "  \"type_vocab_size\": 1,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 250002\n",
            "}\n",
            "\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/config.json\n",
            "Model config XLMRobertaConfig {\n",
            "  \"_name_or_path\": \"chieunq/xlm-roberta-base-finetuned-squad-2\",\n",
            "  \"architectures\": [\n",
            "    \"XLMRobertaForQuestionAnswering\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"xlm-roberta\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.24.0\",\n",
            "  \"type_vocab_size\": 1,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 250002\n",
            "}\n",
            "\n",
            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing XLMRobertaForQuestionAnswering.\n",
            "\n",
            "All the weights of XLMRobertaForQuestionAnswering were initialized from the model checkpoint at chieunq/xlm-roberta-base-finetuned-squad-2.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use XLMRobertaForQuestionAnswering for predictions without further training.\n",
            "loading file sentencepiece.bpe.model from cache at None\n",
            "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/tokenizer.json\n",
            "loading file added_tokens.json from cache at None\n",
            "loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/special_tokens_map.json\n",
            "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/tokenizer_config.json\n"
          ]
        },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'score': 0.9928902387619019,\n",
              " 'start': 98,\n",
              " 'end': 158,\n",
              " 'answer': 'Nguyễn Quang Chiều, Nguyễn Quang Huy và Nguyễn Trần Anh Đức.'}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
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