Test-Binary.ipynb

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      "source": [
        "## Test models"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "<ipython-input-2-6a0c439328bb>:43: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item = {key: torch.tensor(val[idx]).to(device) for key, val in self.encodings.items()}\n",
            "<ipython-input-2-6a0c439328bb>:44: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item['labels'] = torch.tensor(self.labels[idx]).to(device)\n",
            "<ipython-input-2-6a0c439328bb>:43: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item = {key: torch.tensor(val[idx]).to(device) for key, val in self.encodings.items()}\n",
            "<ipython-input-2-6a0c439328bb>:44: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item['labels'] = torch.tensor(self.labels[idx]).to(device)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test Metrics:\n",
            "Accuracy: 0.9009617586443783\n",
            "Precision: 0.8969552558495383\n",
            "Recall: 0.9009617586443783\n",
            "F1 Score: 0.8987399701620706\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "import torch\n",
        "from transformers import BertTokenizer, BertForSequenceClassification\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd\n",
        "\n",
        "# Enable GPU\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "model_name = \"tupi-bert-base-portuguese-cased\"\n",
        "model_tokenizer_path = f\"FpOliveira/{model_name}\"\n",
        "\n",
        "# Define features and target\n",
        "x_name = \"text\"\n",
        "y_name_split = \"hate\"\n",
        "y_name_target = \"hate\"\n",
        "\n",
        "# Load and split the dataset into training and validation sets (stratified)\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/Silly-Machine/TuPi-Portuguese-Hate-Speech-Dataset/main/data/raw/binary/tupi_binary.csv\")\n",
        "train_texts, val_texts, train_labels, val_labels = train_test_split(df[x_name], df[y_name_target], test_size=0.2, random_state=42, stratify=df[y_name_split])\n",
        "test_dataset = pd.DataFrame({x_name: val_texts, y_name_target : val_labels})\n",
        "##Note: it is necessary to use this particular form of partition to reproduce the original training and testing codes at the time of training\n",
        "\n",
        "# Define the tokenizer (replace 'bert-base-multilingual-cased' with your desired tokenizer)\n",
        "tokenizer = BertTokenizer.from_pretrained(model_tokenizer_path)\n",
        "\n",
        "# Load the pre-trained model and move it to the device\n",
        "loaded_model = BertForSequenceClassification.from_pretrained(model_tokenizer_path).to(device)\n",
        "loaded_model.eval()\n",
        "\n",
        "# Tokenize the test dataset\n",
        "test_encodings = tokenizer(list(test_dataset[x_name]), truncation=True, padding=True, return_tensors='pt').to(device)\n",
        "test_labels = torch.tensor(test_dataset[y_name_target].values).long()\n",
        "\n",
        "# Create PyTorch dataset\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]).to(device) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx]).to(device)\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "test_dataset = CustomDataset(test_encodings, test_labels)\n",
        "\n",
        "# Test the model on the test dataset\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
        "test_preds = []\n",
        "\n",
        "with torch.no_grad():\n",
        "    for batch_idx, batch in enumerate(test_loader, 1):\n",
        "        input_ids = batch['input_ids']\n",
        "        attention_mask = batch['attention_mask']\n",
        "\n",
        "        outputs = loaded_model(input_ids, attention_mask=attention_mask)\n",
        "        logits = outputs.logits\n",
        "        preds = torch.argmax(logits, dim=1)\n",
        "        test_preds.extend(preds.cpu().numpy())\n",
        "\n",
        "# Evaluate the model on the test dataset\n",
        "array_test_labels = test_labels.cpu().numpy()\n",
        "test_accuracy = accuracy_score(array_test_labels, test_preds)\n",
        "test_precision = precision_score(array_test_labels, test_preds, average='weighted')\n",
        "test_recall = recall_score(array_test_labels, test_preds, average='weighted')\n",
        "test_f1 = f1_score(array_test_labels, test_preds, average='weighted')\n",
        "\n",
        "# Create a pandas DataFrame with the results\n",
        "results_df = pd.DataFrame({\n",
        "    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1 Score'],\n",
        "    'Value': [test_accuracy, test_precision, test_recall, test_f1]\n",
        "})\n",
        "results_df.to_csv(f'{model_name}_{y_name_target}.csv', index=False)\n",
        "#\n",
        "print('\\nTest Metrics:')\n",
        "print(f'Accuracy: {test_accuracy}')\n",
        "print(f'Precision: {test_precision}')\n",
        "print(f'Recall: {test_recall}')\n",
        "print(f'F1 Score: {test_f1}')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test dataset\n",
        "array_test_labels = test_labels.cpu().numpy()\n",
        "test_accuracy = accuracy_score(array_test_labels, test_preds)\n",
        "test_precision = precision_score(array_test_labels, test_preds)\n",
        "test_recall = recall_score(array_test_labels, test_preds)\n",
        "test_f1 = f1_score(array_test_labels, test_preds)\n",
        "\n",
        "# Create a pandas DataFrame with the results\n",
        "results_df = pd.DataFrame({\n",
        "    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1 Score'],\n",
        "    'Value': [test_accuracy, test_precision, test_recall, test_f1]\n",
        "})\n",
        "results_df.to_csv(f'{model_name}_{y_name_target}.csv', index=False)\n",
        "#\n",
        "print('\\nTest Metrics:')\n",
        "print(f'Accuracy: {test_accuracy}')\n",
        "print(f'Precision: {test_precision}')\n",
        "print(f'Recall: {test_recall}')\n",
        "print(f'F1 Score: {test_f1}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sd3OVVuWRwyF",
        "outputId": "d48ba54a-070d-45a7-8d0c-13601497d9fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test Metrics:\n",
            "Accuracy: 0.9009617586443783\n",
            "Precision: 0.5981012658227848\n",
            "Recall: 0.5394862036156042\n",
            "F1 Score: 0.5672836418209104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9c9GX9Coz4IJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}