Text_classification_with_gpt2.ipynb

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      "source": [
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      },
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      "source": [
        "! pip install transformers datasets accelerate"
      ]
    },
    {
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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "outputId": "39150d6a-8235-4ebd-f77f-3e804f2b90df",
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      "outputs": [
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from transformers import pipeline\n",
        "from transformers.pipelines.pt_utils import KeyDataset\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "import pandas as pd\n",
        "from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
        "from sklearn.utils import resample"
      ],
      "metadata": {
        "id": "TIx_UEtE7QId"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
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      "source": [
        "train_df = pd.read_csv(\"./drive/MyDrive/Colab Notebooks/NLP_exploration/datasets/train.csv\")\n",
        "test_df = pd.read_csv(\"./drive/MyDrive/Colab Notebooks/NLP_exploration/datasets/test.csv\")"
      ],
      "metadata": {
        "id": "hPGDbCv4G8jv"
      },
      "execution_count": 71,
      "outputs": []
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      "source": [
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
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        "outputId": "9d49f8f9-a3a4-4678-ad9e-3ee0c1b311a2"
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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        "train_v2_df = resample(train_df, replace=False, n_samples=10000, random_state=5)\n",
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        "updated_train_df = train_v2_df.replace({'label': {1: \"positive\", 0:\" negative\"}})\n",
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              "              userName                                            content  \\\n",
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              "               userName                                            content  \\\n",
              "9554   TIME PASS CORNER  Excellent app i can go anywhere at anytime whe...   \n",
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        "updated_train_df.drop(['userName', 'score', 'appid'], axis=1, inplace=True)\n",
        "updated_test_df.drop(['userName', 'score', 'appid'], axis=1, inplace=True)"
      ],
      "metadata": {
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      "execution_count": 81,
      "outputs": []
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    {
      "cell_type": "code",
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        "updated_train_df.rename(columns = {'content':'text'}, inplace = True)\n",
        "updated_test_df.rename(columns = {'content':'text'}, inplace = True)"
      ],
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      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "updated_train_df['short_text'] = updated_train_df['text'].str.slice(0,80)\n",
        "updated_test_df['short_text'] = updated_test_df['text'].str.slice(0,80)"
      ],
      "metadata": {
        "id": "LzhUHn7ShtJb"
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      "execution_count": 96,
      "outputs": []
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    {
      "cell_type": "code",
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        "updated_train_df['short_text'].iloc[0]"
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        "colab": {
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      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'I m just giving 3 star because insta gives me so much of problem like i m postin'"
            ],
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        "updated_test_df.drop(['text'], axis=1, inplace=True)"
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      "metadata": {
        "id": "W46BmjC1iueU"
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      "execution_count": 98,
      "outputs": []
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    {
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        "updated_train_df.rename(columns = {'short_text':'text'}, inplace = True)\n",
        "updated_test_df.rename(columns = {'short_text':'text'}, inplace = True)"
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      "metadata": {
        "id": "J_eUWUTgilko"
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      "outputs": []
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              "           label                                               text\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-14ff3568-5fb4-4097-8031-998e1fc8ac6d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-14ff3568-5fb4-4097-8031-998e1fc8ac6d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "updated_train_df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "M1H0dJpzTom3"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "updated_train_df.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gBtaWIkTuzv",
        "outputId": "6995a36a-25e9-4541-f6c4-f7a90e351866"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label    9999\n",
              "text     9999\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "updated_train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3riux2PYT2oR",
        "outputId": "da5cfcdd-481c-4e17-ea5c-f3d5f47d9137"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           label                                               text\n",
              "9552    positive  I m just giving 3 star because insta gives me ...\n",
              "7575    positive  Good but will be better if for video chat able...\n",
              "18182   negative  App interface is absolutely terrible. I also e...\n",
              "9068    positive  Instagram is my favorite social media platform...\n",
              "26581   negative  Please give me the old version back. Ever sinc..."
            ],
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              "\n",
              "  <div id=\"df-d3ea6864-907a-4094-9938-91e7e3a60803\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
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              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9552</th>\n",
              "      <td>positive</td>\n",
              "      <td>I m just giving 3 star because insta gives me ...</td>\n",
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              "      <th>7575</th>\n",
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              "      <td>negative</td>\n",
              "      <td>App interface is absolutely terrible. I also e...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9068</th>\n",
              "      <td>positive</td>\n",
              "      <td>Instagram is my favorite social media platform...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26581</th>\n",
              "      <td>negative</td>\n",
              "      <td>Please give me the old version back. Ever sinc...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-d3ea6864-907a-4094-9938-91e7e3a60803 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d3ea6864-907a-4094-9938-91e7e3a60803');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "updated_train_df.to_csv(\"./drive/MyDrive/Colab Notebooks/NLP_exploration/datasets/train_v2.csv\", index=False)\n",
        "updated_test_df.to_csv(\"./drive/MyDrive/Colab Notebooks/NLP_exploration/datasets/test_v2.csv\", index=False)"
      ],
      "metadata": {
        "id": "9rrheC3YM-FL"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_save_path = \"./drive/MyDrive/Colab Notebooks/NLP_exploration/model_dir/distilgpt2-1\"\n",
        "\n",
        "train_dataset_path = \"./drive/MyDrive/Colab Notebooks/NLP_exploration/datasets/train_v2.csv\"\n",
        "test_dataset_path = \"./drive/MyDrive/Colab Notebooks/NLP_exploration/datasets/test_v2.csv\""
      ],
      "metadata": {
        "id": "qRbRvbGR7pDX"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python drive/MyDrive/Colab\\ Notebooks/NLP_exploration/finetune_gpt2_clm_pytorch.py \\\n",
        "--dataset-path \"./drive/MyDrive/Colab Notebooks/NLP_exploration/datasets/train_v2.csv\" \\\n",
        "--batch-size 16 \\\n",
        "--epoch 4 \\\n",
        "--model-name distilgpt2 \\\n",
        "--model-version 1 \\\n",
        "--model-dir \"./drive/MyDrive/Colab Notebooks/NLP_exploration/model_dir\""
      ],
      "metadata": {
        "id": "x7yT-yxf8AC7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8cd9b86-b260-4e2f-e18a-2ccfedc33b1d"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-06-04 13:15:36.679617: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-4d3ae6cc2479f823/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1...\n",
            "Downloading data files: 100% 1/1 [00:00<00:00, 2375.03it/s]\n",
            "Extracting data files: 100% 1/1 [00:00<00:00, 124.59it/s]\n",
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-4d3ae6cc2479f823/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1. Subsequent calls will reuse this data.\n",
            "100% 1/1 [00:00<00:00, 771.44it/s]\n",
            "dataset loaded: DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['label', 'text'],\n",
            "        num_rows: 9999\n",
            "    })\n",
            "}) from path:./drive/MyDrive/Colab Notebooks/NLP_exploration/datasets/train_v2.csv\n",
            "preprocessed dataset: ['###Text: I m just giving 3 star because insta gives me so much of problem like i m postin ###Label:positive', '###Text: Good but will be better if for video chat able to add more than 4 people ###Label:positive', '###Text: App interface is absolutely terrible. I also encounter a lot of bugs when return ###Label:negative', '###Text: Instagram is my favorite social media platform. I enjoy creating posts and suppo ###Label:positive', '###Text: Please give me the old version back. Ever since the update I cant write proper m ###Label:negative']\n",
            "Model loaded from checkpoint: distilgpt2\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:407: 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",
            "  warnings.warn(\n",
            "  0% 0/2500 [00:00<?, ?it/s]You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "{'loss': 4.926, 'learning_rate': 1.6000000000000003e-05, 'epoch': 0.8}\n",
            "{'loss': 4.5893, 'learning_rate': 1.2e-05, 'epoch': 1.6}\n",
            "{'loss': 4.4607, 'learning_rate': 8.000000000000001e-06, 'epoch': 2.4}\n",
            "{'loss': 4.3952, 'learning_rate': 4.000000000000001e-06, 'epoch': 3.2}\n",
            "{'loss': 4.3406, 'learning_rate': 0.0, 'epoch': 4.0}\n",
            "{'train_runtime': 261.6395, 'train_samples_per_second': 152.867, 'train_steps_per_second': 9.555, 'train_loss': 4.54235986328125, 'epoch': 4.0}\n",
            "100% 2500/2500 [04:21<00:00,  9.56it/s]\n",
            "Training complete!\n",
            "saving model to ./drive/MyDrive/Colab Notebooks/NLP_exploration/model_dir/distilgpt2-1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def parse_label_preds(text):\n",
        "  \"\"\" Parses generated text by the model to extracts the label.\n",
        "      Regex pattern is defined based on the prompt patterns used in data preparation\n",
        "      for model finetuning.\n",
        "  \"\"\"\n",
        "  try:\n",
        "    span = re.search(\"(###Text:)[a-zA-Z]+\", text).span()\n",
        "    label_text = text[span[0]: span[1]]\n",
        "    label_text = re.sub(\"###Text:\", \"\", label_text)\n",
        "\n",
        "  except Exception as e:\n",
        "    print(f\"Can't parse: {text}\")\n",
        "    label_text = \"None\"\n",
        "\n",
        "  return label_text"
      ],
      "metadata": {
        "id": "Ru4wAoik8icZ"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prep_test_data(row):\n",
        "  \"\"\" Preprocessing test data example the same way during inference as is done \n",
        "  during data prep for finetuning.\n",
        "  \"\"\"\n",
        "  row['text'] = \"###Text: \" + row['text'] + \" ###Label:\"\n",
        "  return row"
      ],
      "metadata": {
        "id": "HyLmKsNB9jWB"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model with text generation NLP task for inference on GPU\n",
        "ft_gpt2_classifier = pipeline(\"text-generation\", model=model_save_path, device=0)\n",
        "\n",
        "# load test datasets\n",
        "test_data = load_dataset(\"csv\", data_files={\"test\": [test_dataset_path]}, \n",
        "                         download_mode='force_redownload')\n",
        "\n",
        "test_df = pd.read_csv(test_dataset_path)"
      ],
      "metadata": {
        "id": "ZBpVfGUy-BnY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "referenced_widgets": [
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        "outputId": "6fe29c31-4e01-47df-f33a-0ec63ddec1cf"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-392fcd31aaedc039/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5244b7d9169b4aaaae6d88e1efaf3dae"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-392fcd31aaedc039/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1. Subsequent calls will reuse this data.\n"
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        {
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          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
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    {
      "cell_type": "code",
      "source": [
        "updated_test_dataset = test_data.map(prep_test_data)"
      ],
      "metadata": {
        "id": "tt2l9TNo-to0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "77369b7cc4134aae907b0d35019d6568",
            "2bcede94d67b40aeb537d15ad31d3efc",
            "dc5890e0553b4b41b1f04f5228abc425",
            "b09343488fbd442e9f9fd010983d1803",
            "8326cfeff61e4986acb62fde6a0751e4",
            "b29df99525a54d9fb559638adb597810",
            "e0a946e1427d40bfa200a91639c38edb",
            "370e1dbc9c32456fbfdd54d64b55edb4",
            "903d9e32dc114446ad3fa38ff29d7b6c",
            "ef154e47094a4a279b3f17b36f4e50c9",
            "843949132d8c404ba16c45da93f2a517"
          ]
        },
        "outputId": "2e87eca6-d04b-42ea-aed8-df183668f6ca"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/200 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77369b7cc4134aae907b0d35019d6568"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "updated_test_dataset['test']['text'][:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPX_3TwjerN7",
        "outputId": "20d5791c-0b7f-44d5-e454-a12f1b1bc369"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['###Text: Excellent app i can go anywhere at anytime where i want to go i am very pride of ###Label:',\n",
              " '###Text: its nice and easy for hassle free travel ###Label:',\n",
              " '###Text: My whatsapp account not taking backup. And not showing my email account also. ###Label:',\n",
              " \"###Text: It's a great app but lately I've been facing some glitches on my posts like i ca ###Label:\",\n",
              " \"###Text: Call quality is ok but video call is so bad , can't hear and see clearly ###Label:\",\n",
              " \"###Text: Mine never updates like my husband's does and daughters does it stays the same a ###Label:\",\n",
              " '###Text: Too invasive. The constant notifications and the lack of a clear way to turn the ###Label:',\n",
              " '###Text: I had this app for ages to talk to my family and all that but out of nowhere, ev ###Label:',\n",
              " '###Text: 25 years experience in India & Middle East in Project Management & Coordination; ###Label:',\n",
              " '###Text: Order history is empty now! Where are my orders Flipkart? ###Label:']"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"###Text: The app is good. I like it ###Label:\"\n",
        "output = ft_gpt2_classifier(prompt, return_full_text=True, no_repeat_ngram_size=2, \n",
        "                            num_beams=2)\n",
        "print(output[0]['generated_text'])"
      ],
      "metadata": {
        "id": "p_X64Pq7-tbH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "263764c2-b084-4d33-fccf-99fa77191d2a"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1346: UserWarning: Using `max_length`'s default (50) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "###Text: The app is good. I like it ###Label: __________________________________________________________________________________________________________________ __________________________________________ I'm using ____________ ____________________ I've ____________________________ i'm a _____  _______ ______ ___ \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = []\n",
        "# Run inference on entire test dataset. Run on each example separately rather \n",
        "# than in batches for GPT2 with text generation task while using KeyDataset\n",
        "\n",
        "for output in ft_gpt2_classifier(KeyDataset(updated_test_dataset['test'], 'text'), \n",
        "                                 return_full_text=True, \n",
        "                                 no_repeat_ngram_size=2, \n",
        "                                 num_beams=2):\n",
        "  pred_label = parse_label_preds(output[0]['generated_text'])\n",
        "  preds.append(pred_label)"
      ],
      "metadata": {
        "id": "dgeLJ8Fc-tWc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df['preds'] = preds"
      ],
      "metadata": {
        "id": "OQNCJp6S-tK3"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_df[[\"label\", \"preds\"]].head(20))"
      ],
      "metadata": {
        "id": "KqLAKWZVAvUs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2871030f-07a9-400f-d30a-31c849b14cc7"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        label preds\n",
            "0    positive  None\n",
            "1    positive  None\n",
            "2    negative  None\n",
            "3    positive  None\n",
            "4    negative  None\n",
            "5    negative  None\n",
            "6    negative  None\n",
            "7    positive  None\n",
            "8    positive  None\n",
            "9    negative  None\n",
            "10   positive  None\n",
            "11   negative  None\n",
            "12   negative  None\n",
            "13   positive  None\n",
            "14   negative  None\n",
            "15   positive  None\n",
            "16   negative  None\n",
            "17   negative  None\n",
            "18   negative  None\n",
            "19   positive  None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(test_df['label'], test_df['preds']))"
      ],
      "metadata": {
        "id": "cvFAgI4uAvRF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac1b69f5-f4f1-4d66-87e9-b5830282a3dc"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.00      0.00      0.00      97.0\n",
            "        None       0.00      0.00      0.00       0.0\n",
            "    positive       0.00      0.00      0.00     103.0\n",
            "\n",
            "    accuracy                           0.00     200.0\n",
            "   macro avg       0.00      0.00      0.00     200.0\n",
            "weighted avg       0.00      0.00      0.00     200.0\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check misclassified examples\n",
        "test_df[test_df['preds'] != test_df['label']]"
      ],
      "metadata": {
        "id": "Hj08bYuqAvO1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df['preds'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kAIaT34lFc5",
        "outputId": "8de51397-f1d9-498b-e722-a3fad218b95b"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "None    200\n",
              "Name: preds, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save misclassified cases into excel for further RCA\n",
        "test_df[test_df['preds'] != test_df['label']].to_excel(\"./misclassified.xlsx\", index=False)"
      ],
      "metadata": {
        "id": "UG6LUMxkAvMG"
      },
      "execution_count": 69,
      "outputs": []
    }
  ]
}