Multilabel_Classification.ipynb

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        "<a href=\"https://colab.research.google.com/github/biranchi2018/simpletransformer-examples/blob/master/Multilabel_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "from simpletransformers.classification import MultiLabelClassificationModel\n",
        "\n",
        "# Train and Evaluation data needs to be in a Pandas Dataframe containing at least two columns, a 'text' and a 'labels' column. The `labels` column should contain multi-hot encoded lists.\n",
        "train_data = [[\"Example sentence 1 for multilabel classification.\", [1, 1, 1, 1, 0, 1]]] + [\n",
        "    [\"This is another example sentence. \", [0, 1, 1, 0, 0, 0]]\n",
        "]\n",
        "train_df = pd.DataFrame(train_data, columns=[\"text\", \"labels\"])\n",
        "\n",
        "eval_data = [\n",
        "    [\"Example eval sentence for multilabel classification.\", [1, 1, 1, 1, 0, 1]],\n",
        "    [\"Example eval senntence belonging to class 2\", [0, 1, 1, 0, 0, 0]],\n",
        "]\n",
        "eval_df = pd.DataFrame(eval_data)\n",
        "\n",
        "# Create a MultiLabelClassificationModel\n",
        "model = MultiLabelClassificationModel(\n",
        "    \"roberta\",\n",
        "    \"roberta-base\",\n",
        "    use_cuda=True,\n",
        "    num_labels=6,\n",
        "    args={'fp16':False, \"reprocess_input_data\": True, \"overwrite_output_dir\": True, \"num_train_epochs\": 5},\n",
        ")\n",
        "# You can set class weights by using the optional weight argument\n",
        "print(train_df.head())\n",
        "\n",
        "# Train the model\n",
        "model.train_model(train_df)\n",
        "\n",
        "# Evaluate the model\n",
        "result, model_outputs, wrong_predictions = model.eval_model(eval_df)\n",
        "print(result)\n",
        "print(model_outputs)\n",
        "\n",
        "predictions, raw_outputs = model.predict([\"This thing is entirely different from the other thing. \"])\n",
        "print(predictions)\n",
        "print(raw_outputs)\n"
      ],
      "execution_count": 3,
      "outputs": [
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          "output_type": "stream",
          "text": [
            "                                                text              labels\n",
            "0  Example sentence 1 for multilabel classification.  [1, 1, 1, 1, 0, 1]\n",
            "1                 This is another example sentence.   [0, 1, 1, 0, 0, 0]\n"
          ],
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        },
        {
          "output_type": "stream",
          "text": [
            "\rRunning loss: 0.751533\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "/pytorch/torch/csrc/utils/python_arg_parser.cpp:756: UserWarning: This overload of add_ is deprecated:\n",
            "\tadd_(Number alpha, Tensor other)\n",
            "Consider using one of the following signatures instead:\n",
            "\tadd_(Tensor other, *, Number alpha)\n",
            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:200: UserWarning: Please also save or load the state of the optimzer when saving or loading the scheduler.\n",
            "  warnings.warn(SAVE_STATE_WARNING, UserWarning)\n"
          ],
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        {
          "output_type": "stream",
          "text": [
            "\rRunning loss: 0.715369\n"
          ],
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          "text": [
            "\rRunning loss: 0.711920\n"
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          "name": "stdout"
        },
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        {
          "output_type": "stream",
          "text": [
            "\rRunning loss: 0.698078\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "\rRunning loss: 0.697137\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/simpletransformers/classification/classification_model.py:664: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n"
          ],
          "name": "stderr"
        },
        {
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          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e7c3a74592bc4abfaab71a0ce37d1137",
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "{'LRAP': 0.6916666666666667, 'eval_loss': 0.6917680501937866}\n",
            "[[0.49289387 0.52271587 0.48739725 0.54709655 0.48671624 0.48618382]\n",
            " [0.49372256 0.50634307 0.48073784 0.547069   0.4990852  0.48219553]]\n"
          ],
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        },
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          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bb7ab403605540e0a79703c8e135c887",
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              "version_major": 2
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "[[0, 1, 0, 1, 0, 0]]\n",
            "[[0.48949927 0.5156814  0.48203737 0.54251534 0.4846027  0.47872642]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYonjMXRELS1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5Z5qdSoGoSa",
        "colab_type": "text"
      },
      "source": [
        "## Toxic Comments Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PqVBjwhlGysG",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/\n",
        "\n",
        "https://towardsdatascience.com/multi-label-classification-using-bert-roberta-xlnet-xlm-and-distilbert-with-simple-transformers-b3e0cda12ce5\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxvXqmIZGzPa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import os, shutil\n",
        "from sklearn.model_selection import train_test_split\n",
        "from simpletransformers.classification import MultiLabelClassificationModel\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wq1gyJ5jI4_H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# if os.path.isdir(\"outputs\")==True:\n",
        "#   shutil.rmtree(\"outputs\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pU8hb2lAHMEz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "d163e8fb-99cf-4558-aa28-2f9dd122bb80"
      },
      "source": [
        "df = pd.read_csv('data/train.csv', engine='python', error_bad_lines=False)\n",
        "df.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<style scoped>\n",
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        "df['text'] = df['comment_text'].apply(lambda x: x.replace('\\n', ' '))\n",
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            "Running loss: 0.652043"
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        },
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            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:231: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.\n",
            "  warnings.warn(\"To get the last learning rate computed by the scheduler, \"\n"
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        "colab_type": "code",
        "colab": {}
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      "metadata": {
        "id": "wtcDjnlCLik1",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "test_df = pd.read_csv('data/test.csv', engine='python', error_bad_lines=False)\n",
        "test_df.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k97eLYVqLmbU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "to_predict = test_df.comment_text.apply(lambda x: x.replace('\\n', ' ')).tolist()\n",
        "preds, outputs = model.predict(to_predict)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkUqN1eNLpU3",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "preds"
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      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aXNaBKXjLqN7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "outputs"
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      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBKOwHMDLrQF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub_df = pd.DataFrame(outputs, columns=['toxic','severe_toxic','obscene','threat','insult','identity_hate'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nlRLHTDLtTE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub_df['id'] = test_df['id']\n",
        "sub_df = sub_df[['id', 'toxic','severe_toxic','obscene','threat','insult','identity_hate']]\n",
        "\n",
        "sub_df.to_csv('outputs/submission.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmmZfvcOLvtb",
        "colab_type": "code",
        "colab": {}
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
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}