Sentence_level_binary_classification.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "predict whether a sentence contains claim, question, experience, experience based on claims"
      ],
      "metadata": {
        "id": "xm2ToePDwfWF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5qGiZegweKd",
        "outputId": "28bb88e4-78c8-4026-d672-4493bfce0f06"
      },
      "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 bert-for-sequence-classification\n",
            "  Downloading bert_for_sequence_classification-0.0.4-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: torch!=1.10,>=1.7.1 in /usr/local/lib/python3.9/dist-packages (from bert-for-sequence-classification) (1.13.1+cu116)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.9/dist-packages (from bert-for-sequence-classification) (1.22.4)\n",
            "Requirement already satisfied: pyyaml>=6.0 in /usr/local/lib/python3.9/dist-packages (from bert-for-sequence-classification) (6.0)\n",
            "Requirement already satisfied: pandas>=1.1.5 in /usr/local/lib/python3.9/dist-packages (from bert-for-sequence-classification) (1.4.4)\n",
            "Collecting transformers>=4.2.0\n",
            "  Downloading transformers-4.27.4-py3-none-any.whl (6.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m90.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=1.1.5->bert-for-sequence-classification) (2022.7.1)\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=1.0->bert-for-sequence-classification) (3.1.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch!=1.10,>=1.7.1->bert-for-sequence-classification) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers>=4.2.0->bert-for-sequence-classification) (23.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m84.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers>=4.2.0->bert-for-sequence-classification) (2.27.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers>=4.2.0->bert-for-sequence-classification) (3.10.7)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.3-py3-none-any.whl (199 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.8/199.8 KB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers>=4.2.0->bert-for-sequence-classification) (4.65.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers>=4.2.0->bert-for-sequence-classification) (2022.10.31)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas>=1.1.5->bert-for-sequence-classification) (1.16.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers>=4.2.0->bert-for-sequence-classification) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers>=4.2.0->bert-for-sequence-classification) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers>=4.2.0->bert-for-sequence-classification) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers>=4.2.0->bert-for-sequence-classification) (3.4)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers, bert-for-sequence-classification\n",
            "Successfully installed bert-for-sequence-classification-0.0.4 huggingface-hub-0.13.3 tokenizers-0.13.2 transformers-4.27.4\n"
          ]
        }
      ],
      "source": [
        "! pip install bert-for-sequence-classification"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "import json\n",
        "\n",
        "from transformers import AutoModel, AutoTokenizer\n",
        "\n",
        "from bert_clf import BertCLF, train_evaluate, predict_metrics, prepare_data_notebook, prepare_dataset\n",
        "from bert_clf.utils import set_global_seed"
      ],
      "metadata": {
        "id": "baxJzMTIwen8"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# just use df = pd.read_csv('st1_data_train_sent.csv') when running in the repo\n",
        "df = pd.read_csv('/content/drive/My Drive/UP_w22/PM/task 8/data/st1_data_train_sent.csv')\n",
        "df"
      ],
      "metadata": {
        "id": "KkijYJUJ0c6Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "af03b09b-1dca-4e3b-ef97-3417e8706f75"
      },
      "execution_count": 4,
      "outputs": [
        {
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              "        Unnamed: 0 post_id subreddit_id  \\\n",
              "0                0  s1jpia     t5_2s23e   \n",
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              "...            ...     ...          ...   \n",
              "127354      127354  ri83g1     t5_2r876   \n",
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              "127358      127358  ri83g1     t5_2r876   \n",
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              "                                                 Sentence     Label  \\\n",
              "0       De-Nial\\nI wrote this a few years ago and just...      none   \n",
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              "127354  Just how effective these drugs are and how do ...  question   \n",
              "127355                                  Edit2: Thank you.      none   \n",
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              "127357                I am glad that Trikafta exists NOW.      none   \n",
              "127358  At least most people with CF don't have to hop...      none   \n",
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              "...                                                   ...  \n",
              "127354  Just how effective these drugs are and how do ...  \n",
              "127355                                                 {}  \n",
              "127356                                                 {}  \n",
              "127357                                                 {}  \n",
              "127358                                                 {}  \n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# use this for combined binary model\n",
        "labels = ['claim_per_exp', 'claim','per_exp','question']\n",
        "\n",
        "i = 0\n",
        "for idx, row in df.iterrows(): \n",
        "  if df.loc[idx, 'Label'] in labels:\n",
        "    df.loc[idx, 'Label'] = 'Arg'\n",
        "  else:\n",
        "    df.loc[idx, 'Label'] = 'O'\n",
        "\n",
        "# use this for separated binary models \n",
        "# target = 'per_exp' #'claim_per_exp', 'claim','per_exp','question','none'\n",
        "# labels = ['claim_per_exp', 'claim','per_exp','question','none']\n",
        "\n",
        "# for lab in labels:\n",
        "#   if target != lab:\n",
        "#     df['Label'] = df['Label'].str.replace(lab,'O')\n",
        "#   else:\n",
        "#     df['Label'] = df['Label'].str.replace(lab,'Arg')\n",
        "\n",
        "# df"
      ],
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        "id": "13V9kdf1xEWS",
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          "height": 624
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              "        Unnamed: 0 post_id subreddit_id  \\\n",
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              "127354  Just how effective these drugs are and how do ...     O   \n",
              "127355                                  Edit2: Thank you.     O   \n",
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              "127357                I am glad that Trikafta exists NOW.     O   \n",
              "127358  At least most people with CF don't have to hop...     O   \n",
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              "1                                                      {}  \n",
              "2                                                      {}  \n",
              "3                                                      {}  \n",
              "4                                                      {}  \n",
              "...                                                   ...  \n",
              "127354  Just how effective these drugs are and how do ...  \n",
              "127355                                                 {}  \n",
              "127356                                                 {}  \n",
              "127357                                                 {}  \n",
              "127358                                                 {}  \n",
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    },
    {
      "cell_type": "code",
      "source": [
        "df_arg = df[df[\"Label\"]=='Arg']\n",
        "print(len(df_arg))\n",
        "df_o = df[df[\"Label\"]=='O']\n",
        "print(len(df_o))"
      ],
      "metadata": {
        "id": "LHDsMFsyRMTq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": 6,
      "outputs": [
        {
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          "text": [
            "10207\n",
            "117152\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "# can ignore this part if imbalanced data is needed\n",
        "\n",
        "df_arg = df[df[\"Label\"]=='Arg']\n",
        "df_o = df[df[\"Label\"]=='O']\n",
        "\n",
        "df_o_top = df_o[:df_arg.shape[0]]\n",
        "\n",
        "df_balanced = pd.concat([df_arg, df_o_top])\n",
        "df_balanced = df_balanced.reset_index(drop=True)\n",
        "\n",
        "df_balanced = df_balanced.sample(frac=1).reset_index(drop=True)\n",
        "df_balanced"
      ],
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              "20409                                                 {}  \n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fe6922d2-414b-4ff1-8226-60302a1a34d0 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-fe6922d2-414b-4ff1-8226-60302a1a34d0');\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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_balanced[\"Label\"].describe()"
      ],
      "metadata": {
        "id": "0ZV5ze4z0han",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85f7021e-2513-4ec4-a25f-1cb43a53db13"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     20414\n",
              "unique        2\n",
              "top           O\n",
              "freq      10207\n",
              "Name: Label, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "df_train, df_test = train_test_split(df_balanced, test_size=0.2)\n",
        "df_value = df_balanced\n",
        "print(df_train.shape, df_value.shape, df_test.shape)"
      ],
      "metadata": {
        "id": "18zPOit9z6WX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cef7cba0-3413-41e0-88af-80b832068193"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(16331, 6) (20414, 6) (4083, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['Label'].value_counts()"
      ],
      "metadata": {
        "id": "nRfyChJp2FOm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9888dfbd-69e3-4abd-bfba-a2538d41d91a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "O      2053\n",
              "Arg    2030\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['Label'].value_counts()\n",
        "df_train['Label'].value_counts()"
      ],
      "metadata": {
        "id": "VCZPHQZJ2ZFN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7621ec18-4b3f-44c4-819f-b113eb201c87"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Arg    8177\n",
              "O      8154\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['Label'].value_counts()\n",
        "df_train['Label'].value_counts()\n",
        "df_value['Label'].value_counts()"
      ],
      "metadata": {
        "id": "DZw7_ibs2hT-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cddff6e4-8075-495e-e232-fca74a4f5453"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "O      10207\n",
              "Arg    10207\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transformer Language Model"
      ],
      "metadata": {
        "id": "dOvqYAKD2nDG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "config = dict(\n",
        "    transformer_model = dict(\n",
        "        model = \"chkla/roberta-argument\",\n",
        "        path_to_state_dict = False,\n",
        "        device = 'cuda',\n",
        "        dropout = 0.2,\n",
        "        learning_rate = 2e-5,\n",
        "        batch_size = 16,\n",
        "        shuffle = True,\n",
        "        maxlen = 128,\n",
        "    ),\n",
        "    data = dict(\n",
        "        train_data_path = df_train,\n",
        "        test_data_path = df_value,\n",
        "        text_column = \"Sentence\",\n",
        "        target_column = \"Label\",\n",
        "        random_state = 52,\n",
        "        test_size = 0.3,\n",
        "        stratify=True\n",
        "    ),\n",
        "    training = dict (\n",
        "    save_state_dict = False,\n",
        "    early_stopping = True,\n",
        "    delta = 0.001,\n",
        "    patience = 7,\n",
        "    num_epochs = 2,\n",
        "    average_f1 = 'macro',\n",
        "    other_metrics = ['micro', 'weighted'],\n",
        "    output_dir = \"../results/\",\n",
        "    class_weight = True\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "id": "gRi1AxYI2i2L"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "set_global_seed(seed=config['data']['random_state'])\n",
        "os.makedirs(config['training']['output_dir'], exist_ok=True)"
      ],
      "metadata": {
        "id": "lDKnfWsq2p-B"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(config['transformer_model']['device'])\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "        pretrained_model_name_or_path=config['transformer_model'][\"model\"]\n",
        "    )\n",
        "model_bert = AutoModel.from_pretrained(\n",
        "    pretrained_model_name_or_path=config['transformer_model'][\"model\"]\n",
        ").to(device)"
      ],
      "metadata": {
        "id": "_sMYg9_O23-7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318,
          "referenced_widgets": [
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        },
        "outputId": "fee48409-3444-49e7-bec3-e6bfab0d4c3d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/255 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00ec4ff41cc74a7d8d93072b72c9277e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/790 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e74d19800c4480db420e7f65142cab4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0bcb17fac0243b28a84e319f80f97ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "66d0369e75ac466e886389c3d68da097"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d7aac6d06314d17bfba9e7b80cd5330"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/499M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "949cd09e80534165a95d37cb2145eb11"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at chkla/roberta-argument were not used when initializing RobertaModel: ['classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "- This IS expected if you are initializing RobertaModel 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 RobertaModel 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 RobertaModel were not initialized from the model checkpoint at chkla/roberta-argument and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2label, train_texts, valid_texts, train_targets, valid_targets = prepare_data_notebook(\n",
        "    config=config, train_df = df_train, test_df = df_value\n",
        ")"
      ],
      "metadata": {
        "id": "WiDiJ9G729jN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "764feaf5-e6c3-48d4-c0e8-da4ca6d30c3d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/bert_clf/src/pandas_dataset/SimpleDataFrame.py:21: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.train.dropna(inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2label"
      ],
      "metadata": {
        "id": "Srqq2nif3AJi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cf02d77-ef12-4609-97b5-3a7e8fc31af8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'Arg', 1: 'O'}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = BertCLF(\n",
        "    pretrained_model=model_bert,\n",
        "    tokenizer=tokenizer,\n",
        "    id2label=id2label,\n",
        "    dropout=config['transformer_model']['dropout'],\n",
        "    device=device     \n",
        "    )"
      ],
      "metadata": {
        "id": "IqY9LR1H3BQQ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = model.to(device)"
      ],
      "metadata": {
        "id": "oId6Zxts3Ctc"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "optimizer = optim.Adam(model.parameters(), lr=float(config['transformer_model']['learning_rate']))\n",
        "criterion = nn.NLLLoss()\n",
        "\n",
        "training_generator, valid_generator = prepare_dataset(\n",
        "    tokenizer=tokenizer,\n",
        "    train_texts=train_texts,\n",
        "    train_targets=train_targets,\n",
        "    valid_texts=valid_texts,\n",
        "    valid_targets=valid_targets,\n",
        "    config=config\n",
        ")"
      ],
      "metadata": {
        "id": "gQxNh2VZ3kpm"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = train_evaluate(\n",
        "    model=model,\n",
        "    training_generator=training_generator,\n",
        "    valid_generator=valid_generator,\n",
        "    criterion=criterion,\n",
        "    optimizer=optimizer,\n",
        "    num_epochs=config['training']['num_epochs'],\n",
        "    average=config['training']['average_f1'],\n",
        "    config=config\n",
        ")"
      ],
      "metadata": {
        "id": "YYHXbgRB3lpq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6865c733-b4f1-4ddd-e220-719631127f31"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==== Epoch 1 out of 2 ====\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training loop: 100%|██████████| 1021/1021 [01:39<00:00, 10.26it/s]\n",
            "Evaluating loop: 100%|██████████| 1276/1276 [00:39<00:00, 32.18it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train F1: 0.6861194598987732\n",
            "Eval F1: 0.7770953280728632\n",
            "\n",
            "Train F1 micro: 0.703604977294987\n",
            "Eval F1 micro: 0.7888840685176892\n",
            "\n",
            "Train F1 weighted: 0.6989423772245509\n",
            "Eval F1 weighted: 0.7886624214997021\n",
            "\n",
            "==== Epoch 2 out of 2 ====\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training loop: 100%|██████████| 1021/1021 [01:36<00:00, 10.59it/s]\n",
            "Evaluating loop: 100%|██████████| 1276/1276 [00:39<00:00, 32.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train F1: 0.7793255965047192\n",
            "Eval F1: 0.8609923939663957\n",
            "\n",
            "Train F1 micro: 0.7920265337013622\n",
            "Eval F1 micro: 0.8691082624272279\n",
            "\n",
            "Train F1 weighted: 0.7906802129156768\n",
            "Eval F1 weighted: 0.8688967660114555\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Computing final metrics...: 100%|██████████| 1276/1276 [00:34<00:00, 36.47it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         Arg       0.88      0.85      0.87     10207\n",
            "           O       0.86      0.88      0.87     10207\n",
            "\n",
            "    accuracy                           0.87     20414\n",
            "   macro avg       0.87      0.87      0.87     20414\n",
            "weighted avg       0.87      0.87      0.87     20414\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.to('cpu')"
      ],
      "metadata": {
        "id": "X8vsLta73m01",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f768f057-66b9-4b70-8d07-3514b6d65efe"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertCLF(\n",
              "  (pretrained_model): RobertaModel(\n",
              "    (embeddings): RobertaEmbeddings(\n",
              "      (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
              "      (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
              "      (token_type_embeddings): Embedding(1, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): RobertaEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): RobertaPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (drop): Dropout(p=0.2, inplace=False)\n",
              "  (act): LogSoftmax(dim=1)\n",
              "  (fc): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = []\n",
        "for i,j in zip(df_test['Sentence'], df_test['Label']):\n",
        "    preds.append([model.predict(i), j, i])"
      ],
      "metadata": {
        "id": "USxUasW24ISN"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "pred = []\n",
        "for i in preds:\n",
        "    pred.append(i[0])\n",
        "\n",
        "true = []\n",
        "for m in preds:\n",
        "    true.append(m[1])"
      ],
      "metadata": {
        "id": "Ii4v6ikC4MCN"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "target_names = ['class 0', 'class 1']\n",
        "print(classification_report(true, pred, target_names=target_names, digits=3))"
      ],
      "metadata": {
        "id": "366Jf7IUT8rR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b60cd85-8bad-4c72-e549-7691b62824a0"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     class 0      0.823     0.769     0.795      2030\n",
            "     class 1      0.785     0.836     0.810      2053\n",
            "\n",
            "    accuracy                          0.803      4083\n",
            "   macro avg      0.804     0.803     0.803      4083\n",
            "weighted avg      0.804     0.803     0.803      4083\n",
            "\n"
          ]
        }
      ]
    }
  ]
}