ConvBert_Matching_entities.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AJamal27891/University-coursees-projects/blob/master/ConvBert_Matching_entities.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mG-rW9lIvoBq"
      },
      "source": [
        "# download dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FNKhj_Tyksx"
      },
      "source": [
        "!rm -r  test_trainer "
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rL6YqrRWb6PW",
        "outputId": "e64ad142-ae58-4a2e-82bd-c93cd10bd163"
      },
      "source": [
        "!pip install transformers\n",
        "!pip install datasets"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.8.2)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.45)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from transformers) (3.13)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: huggingface-hub==0.0.12 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.12)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (4.5.0)\n",
            "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n",
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            "Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (1.8.0)\n",
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            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from datasets) (4.5.0)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets) (0.70.12.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.7/dist-packages (from datasets) (2021.6.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.1.5)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2.23.0)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.7/dist-packages (from datasets) (2.0.2)\n",
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            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->datasets) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->datasets) (3.4.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2018.9)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2021.5.30)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->datasets) (2.4.7)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WF3WDPkDv12A"
      },
      "source": [
        "## load datasets "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JuGlmOEpiqr"
      },
      "source": [
        "dirty_walmart_amazon = r'/content/drive/MyDrive/entity-matching-transformer-master/data_DeepMatching/dirty_walmart_amazon'\n",
        "dirty_dblp_scholar = r'/content/drive/MyDrive/entity-matching-transformer-master/data_DeepMatching/dirty_dblp_scholar'\n",
        "dirty_amazon_ituns = r'/content/drive/MyDrive/entity-matching-transformer-master/data_DeepMatching/dirty_amazon_itunes'\n",
        "dirty_dblp_acm = r'/content/drive/MyDrive/entity-matching-transformer-master/data_DeepMatching/dirty_dblp_acm'\n",
        "abt_buy = r'/content/drive/MyDrive/entity-matching-transformer-master/data_DeepMatching/abt_buy'\n",
        "amazon_google = r'/content/drive/MyDrive/entity-matching-transformer-master/data_DeepMatching/amazon_google'\n",
        "QQP= r'/content/drive/MyDrive/entity-matching-transformer-master/data_DeepMatching/QQP'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WYXWJMcPv8iX"
      },
      "source": [
        "## Test dataset merger functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPs0EaCWWy0y"
      },
      "source": [
        "import pandas as pd \n",
        "data_A = pd.DataFrame({'id':[1,2,3,4,5,6],'price':[.2,.5,.6,.7,1,.8],'describtion':['samsung s5','iphone','xperia','sonny','hstc','xomi']})\n",
        "data_B = pd.DataFrame({'id':[1,2,3,4,5,6],'price':[.2,.5,.6,.7,1,.8],'describtion':['iphone','xperia','sonny','huawi','xomi','samsung s5']})\n",
        "lables = pd.DataFrame({'left_id':[1,2,3,4,5,6],'right_id':[6,1,2,3,4,5],'label':[1,1,1,1,0,1]})"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "S33VggvBYcrU",
        "outputId": "87322fa4-31f1-4fb1-921b-5e3ea2ab186b"
      },
      "source": [
        "total_data = lables.merge(data_A,left_on='left_id',right_on='id')\n",
        "total_data = total_data.merge(data_B,left_on='right_id',right_on='id')\n",
        "total_data"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>left_id</th>\n",
              "      <th>right_id</th>\n",
              "      <th>label</th>\n",
              "      <th>id_x</th>\n",
              "      <th>price_x</th>\n",
              "      <th>describtion_x</th>\n",
              "      <th>id_y</th>\n",
              "      <th>price_y</th>\n",
              "      <th>describtion_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>samsung s5</td>\n",
              "      <td>6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>samsung s5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>iphone</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>iphone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>xperia</td>\n",
              "      <td>2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>xperia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>sonny</td>\n",
              "      <td>3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>sonny</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>hstc</td>\n",
              "      <td>4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>huawi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>xomi</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>xomi</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   left_id  right_id  label  id_x  ...  describtion_x id_y  price_y  describtion_y\n",
              "0        1         6      1     1  ...     samsung s5    6      0.8     samsung s5\n",
              "1        2         1      1     2  ...         iphone    1      0.2         iphone\n",
              "2        3         2      1     3  ...         xperia    2      0.5         xperia\n",
              "3        4         3      1     4  ...          sonny    3      0.6          sonny\n",
              "4        5         4      0     5  ...           hstc    4      0.7          huawi\n",
              "5        6         5      1     6  ...           xomi    5      1.0           xomi\n",
              "\n",
              "[6 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOV_RpcwwJCv"
      },
      "source": [
        "import random\n",
        "def augmentation(df):\n",
        "  first = ['label']\n",
        "  clist = df.columns.tolist()\n",
        "  clist.remove(first[0])\n",
        "  random.shuffle(clist)\n",
        "  df1 = df[first+clist]\n",
        "  random.shuffle(clist)\n",
        "  df2 = df[first+clist]\n",
        "  random.shuffle(clist)\n",
        "  df3 = df[first+clist]\n",
        "  random.shuffle(clist)\n",
        "  df4 = df[first+clist]\n",
        "  return df, df1,df2,df3,df4\n",
        "\n",
        "\n",
        "def remove_id_columns(total_data):\n",
        "  for x in total_data:\n",
        "    if x.endswith('id'):\n",
        "      total_data.drop(x,axis=1,inplace=True)\n",
        "    if x.startswith('id'):\n",
        "      total_data.drop(x,axis=1,inplace=True)\n",
        "  return total_data  \n",
        "\n",
        "def merger(data_A,data_B,train,test,valid,\n",
        "           remove_ids=True,\n",
        "           merge_attributes=True,\n",
        "           drop_original=True,\n",
        "           tuples=True):\n",
        "  train_data = train.merge(data_A,left_on='ltable_id',right_on='id')\n",
        "  train_data = train_data.merge(data_B,left_on='rtable_id',right_on='id')\n",
        "  test_data = test.merge(data_A,left_on='ltable_id',right_on='id')\n",
        "  test_data = test_data.merge(data_B,left_on='rtable_id',right_on='id')\n",
        "  valid_data = valid.merge(data_A,left_on='ltable_id',right_on='id')\n",
        "  valid_data = valid_data.merge(data_B,left_on='rtable_id',right_on='id')\n",
        "  if remove_ids:\n",
        "    train_data = remove_id_columns(train_data)\n",
        "    test_data = remove_id_columns(test_data)\n",
        "    valid_data = remove_id_columns(valid_data)\n",
        "  if merge_attributes:\n",
        "    dfs = augmentation(train_data)\n",
        "    new_dfs = []\n",
        "    for df in dfs:\n",
        "      df.iloc[:,1:]  = train_data.iloc[:,1:].astype(str).apply(lambda x : ' [COL] '+x.name.strip('_y').strip('_x') +' [VAL] '+x)\n",
        "      df['text_left'] =train_data[[c for c in train_data.columns[1:] if c.endswith('_x')]].apply(lambda x :\" [CLS] \" + ' '.join(x.astype(str)) ,axis=1)\n",
        "      df['text_right'] =train_data[[c for c in train_data.columns[1:] if c.endswith('_y')]].apply(lambda x :\" [SEP] \" + ' '.join(x.astype(str)) ,axis=1)\n",
        "      new_dfs.append(df[['label','text_left','text_right']])\n",
        "    train_data = pd.concat(new_dfs)\n",
        "    \n",
        "      \n",
        "    test_data.iloc[:,1:]  = test_data.iloc[:,1:].astype(str).apply(lambda x : ' [COL] '+x.name.strip('_y').strip('_x') +' [VAL] '+x)\n",
        "    test_data['text_left'] =test_data[[c for c in test_data.columns[1:] if c.endswith('_x')]].apply(lambda x :\" [CLS] \" + ' '.join(x.astype(str)) ,axis=1)\n",
        "    test_data['text_right'] =test_data[[c for c in test_data.columns[1:] if c.endswith('_y')]].apply(lambda x :\" [SEP] \" + ' '.join(x.astype(str)) ,axis=1)\n",
        "    valid_data.iloc[:,1:]  = valid_data.iloc[:,1:].astype(str).apply(lambda x : ' [COL]'+x.name.strip('_y').strip('_x') +' [VAL] '+x)\n",
        "    valid_data['text_left'] =valid_data[[c for c in valid_data.columns[1:] if c.endswith('_x')]].apply(lambda x :\" [CLS] \" + ' '.join(x.astype(str)) ,axis=1)\n",
        "    valid_data['text_right'] =valid_data[[c for c in valid_data.columns[1:] if c.endswith('_y')]].apply(lambda x :\" [SEP] \" + ' '.join(x.astype(str)) ,axis=1) \n",
        "\n",
        "  if drop_original:\n",
        "    train_data= train_data[['label','text_left','text_right']]\n",
        "    test_data= test_data[['label','text_left','text_right']]\n",
        "    valid_data= valid_data[['label','text_left','text_right']]\n",
        "  if tuples:\n",
        "    train_data['tuples']= train_data[['text_left','text_right']].values.tolist()\n",
        "    train_data['tuples'] = train_data['tuples'].apply(lambda x: ' '.join(x))\n",
        "    test_data['tuples']= test_data[['text_left','text_right']].values.tolist()\n",
        "    test_data['tuples'] = test_data['tuples'].apply(lambda x: ' '.join(x))\n",
        "    valid_data['tuples']= valid_data[['text_left','text_right']].values.tolist()\n",
        "    valid_data['tuples'] = valid_data['tuples'].apply(lambda x: ' '.join(x))\n",
        "\n",
        "  return train_data, test_data, valid_data"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1KTosSUs9QX"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mgcc16oDs3B6"
      },
      "source": [
        "## Amazon Google data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "PkgGgxR9smGS",
        "outputId": "ed56ec12-2b40-4e3e-fa94-779a42411db7"
      },
      "source": [
        "table_A = pd.read_csv(amazon_google+r'/deep_matcher/tableA.csv')\n",
        "table_B = pd.read_csv(amazon_google+r'/deep_matcher/tableB.csv')\n",
        "AG_train = pd.read_csv(amazon_google+r'/deep_matcher/train.csv')\n",
        "AG_test = pd.read_csv(amazon_google+r'/deep_matcher/test.csv')\n",
        "AG_valid = pd.read_csv(amazon_google+r'/deep_matcher/valid.csv')\n",
        "AG_train, AG_test, AG_valid = merger(table_A,table_B,AG_train,AG_test,AG_valid)\n",
        "AG_train.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1715: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  isetter(loc, v)\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:46: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text_left</th>\n",
              "      <th>text_right</th>\n",
              "      <th>tuples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] microsoft visio stan...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] adobe cs3 design sta...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] microsoft visio stan...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe premiere pro c...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] adobe cs3 design sta...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe premiere pro c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe indesign cs3 u...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] adobe cs3 design sta...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe indesign cs3 u...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe creative suite...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] adobe cs3 design sta...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe creative suite...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe creative suite...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] adobe cs3 design sta...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe creative suite...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  ...                                             tuples\n",
              "0      0  ...   [CLS]  [COL] title [VAL] microsoft visio stan...\n",
              "1      0  ...   [CLS]  [COL] title [VAL] adobe premiere pro c...\n",
              "2      0  ...   [CLS]  [COL] title [VAL] adobe indesign cs3 u...\n",
              "3      0  ...   [CLS]  [COL] title [VAL] adobe creative suite...\n",
              "4      0  ...   [CLS]  [COL] title [VAL] adobe creative suite...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GT3zmTzPII8F",
        "outputId": "48fed2b5-096c-4817-9cbd-ef2f3c785166"
      },
      "source": [
        "AG_train"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text_left</th>\n",
              "      <th>text_right</th>\n",
              "      <th>tuples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] microsoft visio stan...</td>\n",
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              "      <td>[CLS]  [COL] title [VAL] microsoft visio stan...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe premiere pro c...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] adobe cs3 design sta...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe premiere pro c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe indesign cs3 u...</td>\n",
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              "      <td>[CLS]  [COL] title [VAL] adobe indesign cs3 u...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe creative suite...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] adobe cs3 design sta...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe creative suite...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe creative suite...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] adobe cs3 design sta...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adobe creative suite...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6869</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] x-oom internet movie...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] feral interactive th...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] x-oom internet movie...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6870</th>\n",
              "      <td>1</td>\n",
              "      <td>[CLS]  [COL] title [VAL] pc hoyle jewels : sw...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] encore software 1066...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] pc hoyle jewels : sw...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6871</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] autodesk discreet co...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] autodesk autodesk co...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] autodesk discreet co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6872</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] virtual labs : light...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] virtual labs : light...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] virtual labs : light...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6873</th>\n",
              "      <td>1</td>\n",
              "      <td>[CLS]  [COL] title [VAL] encore the big box o...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] encore software 4600...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] encore the big box o...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34370 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      label  ...                                             tuples\n",
              "0         0  ...   [CLS]  [COL] title [VAL] microsoft visio stan...\n",
              "1         0  ...   [CLS]  [COL] title [VAL] adobe premiere pro c...\n",
              "2         0  ...   [CLS]  [COL] title [VAL] adobe indesign cs3 u...\n",
              "3         0  ...   [CLS]  [COL] title [VAL] adobe creative suite...\n",
              "4         0  ...   [CLS]  [COL] title [VAL] adobe creative suite...\n",
              "...     ...  ...                                                ...\n",
              "6869      0  ...   [CLS]  [COL] title [VAL] x-oom internet movie...\n",
              "6870      1  ...   [CLS]  [COL] title [VAL] pc hoyle jewels : sw...\n",
              "6871      0  ...   [CLS]  [COL] title [VAL] autodesk discreet co...\n",
              "6872      0  ...   [CLS]  [COL] title [VAL] virtual labs : light...\n",
              "6873      1  ...   [CLS]  [COL] title [VAL] encore the big box o...\n",
              "\n",
              "[34370 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XEcwJpwX-9oG"
      },
      "source": [
        "AG_train = AG_train.groupby('label')\n",
        "AG_train = AG_train.apply(lambda x: x.sample(AG_train.size().min()).reset_index(drop=True))\n",
        "AG_train = AG_train.sample(frac=1).reset_index(drop=True)\n",
        "AG_test = AG_test.groupby('label')\n",
        "AG_test = AG_test.apply(lambda x: x.sample(AG_test.size().min()).reset_index(drop=True))\n",
        "AG_test = AG_test.sample(frac=1).reset_index(drop=True)\n",
        "AG_valid = AG_valid.groupby('label')\n",
        "AG_valid = AG_valid.apply(lambda x: x.sample(AG_valid.size().min()).reset_index(drop=True))\n",
        "AG_valid = AG_valid.sample(frac=1).reset_index(drop=True)\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "HGm-x3soIQDF",
        "outputId": "ed4d1fed-2ed8-46aa-9194-cbcabde8bcc9"
      },
      "source": [
        "AG_train"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text_left</th>\n",
              "      <th>text_right</th>\n",
              "      <th>tuples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adventure workshop p...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] encore software 1242...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] adventure workshop p...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] jumpstart 1st grade ...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] brain play 1st-3rd g...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] jumpstart 1st grade ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>[CLS]  [COL] title [VAL] quicken 2007 home &amp; ...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] quicken home and bus...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] quicken 2007 home &amp; ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] land before time : k...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] jumpstart kindergart...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] land before time : k...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>[CLS]  [COL] title [VAL] sibelius 5 professio...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] sibelius sibelius 5 ...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] sibelius 5 professio...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6985</th>\n",
              "      <td>1</td>\n",
              "      <td>[CLS]  [COL] title [VAL] apple remote desktop...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] apple remote desktop...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] apple remote desktop...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6986</th>\n",
              "      <td>1</td>\n",
              "      <td>[CLS]  [COL] title [VAL] boomerang webpage de...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] web page designer go...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] boomerang webpage de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6987</th>\n",
              "      <td>1</td>\n",
              "      <td>[CLS]  [COL] title [VAL] wisdom of the bible ...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] tri synergy inc wisd...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] wisdom of the bible ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6988</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] instant immersion ja...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] instant immersion it...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] instant immersion ja...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6989</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] microsoft windows sm...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] microsoft windows xp...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] microsoft windows sm...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6990 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      label  ...                                             tuples\n",
              "0         0  ...   [CLS]  [COL] title [VAL] adventure workshop p...\n",
              "1         0  ...   [CLS]  [COL] title [VAL] jumpstart 1st grade ...\n",
              "2         1  ...   [CLS]  [COL] title [VAL] quicken 2007 home & ...\n",
              "3         0  ...   [CLS]  [COL] title [VAL] land before time : k...\n",
              "4         1  ...   [CLS]  [COL] title [VAL] sibelius 5 professio...\n",
              "...     ...  ...                                                ...\n",
              "6985      1  ...   [CLS]  [COL] title [VAL] apple remote desktop...\n",
              "6986      1  ...   [CLS]  [COL] title [VAL] boomerang webpage de...\n",
              "6987      1  ...   [CLS]  [COL] title [VAL] wisdom of the bible ...\n",
              "6988      0  ...   [CLS]  [COL] title [VAL] instant immersion ja...\n",
              "6989      0  ...   [CLS]  [COL] title [VAL] microsoft windows sm...\n",
              "\n",
              "[6990 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGTSs9-r9bjq"
      },
      "source": [
        "import os \n",
        "try:\n",
        "  os.mkdir('merged_data')\n",
        "  os.mkdir('merged_data/amazon_google')\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "AG_train.to_csv(r'merged_data/amazon_google/train.csv')\n",
        "AG_test.to_csv(r'merged_data/amazon_google/test.csv')\n",
        "AG_valid.to_csv(r'merged_data/amazon_google/valid.csv')\n",
        "\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89,
          "referenced_widgets": [
            "43f0d31781504280b96fb0c96ab134a2",
            "028ae02a2c1d4df0b073f7c87bc02436",
            "5569e8827fbf494fa9775cdd1987125b",
            "221ceaa1d10844fe9bc75a9a9922883b",
            "451803ed534b4f6989509d6855b37ce8",
            "d12e1af9a28043359dde4b90b66b08d5",
            "d2b383374f0b4bd7ad046c1ecf69890d",
            "85d242baabd04e96a456d2c0130cec07",
            "328bbc3cd580478c88a7d03fd98312c9",
            "4ab860b4cc3540058ba3935d89ac1cbc",
            "6b46daf5c2784cc3a08888252c8c7e36",
            "f91c562ca7bc413fa06785a4390e1249",
            "a84e70c2c7be4e959cec29f10a6287e8",
            "e528e407438f4f65a23b785d7f7f8ac8",
            "249ac84914ec43708b0c82df231e5a82",
            "c62a0e8b0dfc4536bcd0598aa0b72b93"
          ]
        },
        "id": "ZsbzHx0GrAQY",
        "outputId": "198ed7d3-1bf4-4da5-d2a7-99dae6e7daf8"
      },
      "source": [
        "from datasets import load_dataset\n",
        "raw_datasets = load_dataset('csv',data_files={'train':['merged_data/amazon_google/train.csv','merged_data/amazon_google/valid.csv'],\n",
        "                                              'test':'merged_data/amazon_google/test.csv',\n",
        "                                              })"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-3b43034c075bb555\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/csv/default-3b43034c075bb555/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43f0d31781504280b96fb0c96ab134a2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "328bbc3cd580478c88a7d03fd98312c9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-3b43034c075bb555/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLTeCIuLbs-x"
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"YituTech/conv-bert-base\")"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "af92a8ec4c7344e48c99681f4e42f4bc",
            "c6f2c9fb2d1641329ca4a961a08315fd",
            "a490d60e7eb441aca2575874d62457ce",
            "e377d6450db845faa191482bbf1d6597",
            "d08d03441eb54852b7582f5fda55c9e9",
            "41c040060ba94935a570af186b52d069",
            "683e29e2cca54c72bd17b1fe28cdd97d",
            "05901a3e1ff841cc9150e3bf5c99a00b",
            "2afc5bd2f00f46f9934aa820d88d1cc8",
            "aa077791867e481a9227819142f363c6",
            "5f53ba0e21e642aabb2f741e6625b731",
            "dd1d0617d5b240d4862428a4e8f4ac57",
            "c34ef2b1efd3445caf1bfb8dee30e7cb",
            "19b88c85435746fbb68723395cf83931",
            "52a4f68412454322890314db11239177",
            "49929cea48bc47c5ac1232b66149abed"
          ]
        },
        "id": "ArLWe4hP-9Vb",
        "outputId": "e23a1f18-6e3e-4c35-ddfd-e7d8274497da"
      },
      "source": [
        "def tokenize_function(examples):\n",
        "    # examples['text_left'].head()\n",
        "    # examples['tuples'] = examples[[\"text_left\",\"text_right\"]].apply(lambda x: (x[0],x[1]),axis=1)\n",
        "    return tokenizer(examples['tuples'], padding=\"max_length\", truncation=True,max_length=128)\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Parameter 'function'=<function tokenize_function at 0x7fac3065a170> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "af92a8ec4c7344e48c99681f4e42f4bc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=31.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2afc5bd2f00f46f9934aa820d88d1cc8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "vv1FWbZjRmdY",
        "outputId": "a9d3b975-be05-464d-cdd5-ad19dd94fe4e"
      },
      "source": [
        "tokenized_datasets = tokenized_datasets.remove_columns('Unnamed: 0')\n",
        "tokenizer.decode(tokenized_datasets['train']['input_ids'][0])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'[CLS] [CLS] [ col ] title [ val ] adventure workshop preschool - 1st grade 8th edition [ col ] manufacturer [ val ] encore [ col ] price [ val ] 19. 99 [SEP] [ col ] title [ val ] encore software 12421 adventure workshop 8 1st - 3rd [ col ] manufacturer [ val ] nan [ col ] price [ val ] 17. 97 [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBmPWvjBb3S4"
      },
      "source": [
        "import torch\n",
        "torch.cuda.empty_cache()"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgJ7bXw_hNLL",
        "outputId": "40c7d437-3b5f-4cf7-bdff-b13dac0c9a9b"
      },
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"YituTech/conv-bert-base\", num_labels=2)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of ConvBertForSequenceClassification were not initialized from the model checkpoint at YituTech/conv-bert-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JD0p3172QtOv"
      },
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\"test_trainer\",num_train_epochs=14,per_device_train_batch_size=16)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCHBcRcbQz1b"
      },
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model, args=training_args, train_dataset=tokenized_datasets['train'], eval_dataset=tokenized_datasets['test']\n",
        ")"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QVUNIuEFRLZ3",
        "outputId": "16eaeb4f-d891-4121-8f42-ef42c993eae9"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `ConvBertForSequenceClassification.forward` and have been ignored: text_right, text_left, tuples.\n",
            "***** Running training *****\n",
            "  Num examples = 7458\n",
            "  Num Epochs = 14\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 6538\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6538' max='6538' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6538/6538 31:58, Epoch 14/14]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.252600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.123500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.087700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.068000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.056400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.042300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.026300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.027400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.013500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.010300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.006100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.003100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.004800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to test_trainer/checkpoint-500\n",
            "Configuration saved in test_trainer/checkpoint-500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-1000\n",
            "Configuration saved in test_trainer/checkpoint-1000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-1000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-1500\n",
            "Configuration saved in test_trainer/checkpoint-1500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-1500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-2000\n",
            "Configuration saved in test_trainer/checkpoint-2000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-2000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-2500\n",
            "Configuration saved in test_trainer/checkpoint-2500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-2500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-3000\n",
            "Configuration saved in test_trainer/checkpoint-3000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-3000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-3500\n",
            "Configuration saved in test_trainer/checkpoint-3500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-3500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-4000\n",
            "Configuration saved in test_trainer/checkpoint-4000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-4000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-4500\n",
            "Configuration saved in test_trainer/checkpoint-4500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-4500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-5000\n",
            "Configuration saved in test_trainer/checkpoint-5000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-5000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-5500\n",
            "Configuration saved in test_trainer/checkpoint-5500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-5500/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-6000\n",
            "Configuration saved in test_trainer/checkpoint-6000/config.json\n",
            "Model weights saved in test_trainer/checkpoint-6000/pytorch_model.bin\n",
            "Saving model checkpoint to test_trainer/checkpoint-6500\n",
            "Configuration saved in test_trainer/checkpoint-6500/config.json\n",
            "Model weights saved in test_trainer/checkpoint-6500/pytorch_model.bin\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=6538, training_loss=0.05522880535963074, metrics={'train_runtime': 1918.8989, 'train_samples_per_second': 54.412, 'train_steps_per_second': 3.407, 'total_flos': 8521835467622400.0, 'train_loss': 0.05522880535963074, 'epoch': 14.0})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJpXMTl7YG45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "37cce2dafa0c4aa38534e22134e2a7da",
            "3b35d60a3a0448dfb9ad8bfef63d6bac",
            "5bd01456efd94da380a0c8785d7623b4",
            "46be9c002aed43708208275aeff45372",
            "9c69d0d6022a4721b7359749f4f2e63f",
            "c836ad65d8634dadb8a883fc7fcb5e49",
            "981120129a7343db928b77e336d8e9ec",
            "268954fa33f94e9d9770be5baa62fd1f"
          ]
        },
        "outputId": "ca80cdeb-0707-4063-b2e4-94014ec3f839"
      },
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"f1\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37cce2dafa0c4aa38534e22134e2a7da",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1905.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTNUm3fRYcsD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "d33082ba-f5b6-4a4d-ccf9-324c0abc4cc6"
      },
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_datasets['train'],\n",
        "    eval_dataset=tokenized_datasets['test'],\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "trainer.evaluate()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `ConvBertForSequenceClassification.forward` and have been ignored: text_right, text_left, tuples.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 468\n",
            "  Batch size = 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='59' max='59' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [59/59 00:02]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_f1': 0.8954545454545455,\n",
              " 'eval_loss': 0.9385485649108887,\n",
              " 'eval_runtime': 3.0199,\n",
              " 'eval_samples_per_second': 154.973,\n",
              " 'eval_steps_per_second': 19.537}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMOXEhC2iOyX"
      },
      "source": [
        "## abt-BUy dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3llffB9NTUVA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "outputId": "365f264b-e348-494e-cdaf-ad3e13ebe034"
      },
      "source": [
        "table_A = pd.read_csv(abt_buy+r'/deep_matcher/tableA.csv')\n",
        "table_B = pd.read_csv(abt_buy+r'/deep_matcher/tableB.csv')\n",
        "AG_train = pd.read_csv(abt_buy+r'/deep_matcher/train.csv')\n",
        "AG_test = pd.read_csv(abt_buy+r'/deep_matcher/test.csv')\n",
        "AG_valid = pd.read_csv(abt_buy+r'/deep_matcher/valid.csv')\n",
        "AG_train, AG_test, AG_valid = merger(table_A,table_B,AG_train,AG_test,AG_valid)\n",
        "AG_train.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1715: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  isetter(loc, v)\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:45: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text_left</th>\n",
              "      <th>text_right</th>\n",
              "      <th>tuples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] name [VAL] lg 24 ' lds4821ww sem...</td>\n",
              "      <td>[SEP]  [COL] name [VAL] lg ldf6920bb fully in...</td>\n",
              "      <td>[CLS]  [COL] name [VAL] lg 24 ' lds4821ww sem...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] name [VAL] lg 24 ' lds4821bb sem...</td>\n",
              "      <td>[SEP]  [COL] name [VAL] lg ldf6920bb fully in...</td>\n",
              "      <td>[CLS]  [COL] name [VAL] lg 24 ' lds4821bb sem...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>[CLS]  [COL] name [VAL] lg 24 ' ldf6920bb ful...</td>\n",
              "      <td>[SEP]  [COL] name [VAL] lg ldf6920bb fully in...</td>\n",
              "      <td>[CLS]  [COL] name [VAL] lg 24 ' ldf6920bb ful...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] name [VAL] lg 24 ' ldf6920ww ful...</td>\n",
              "      <td>[SEP]  [COL] name [VAL] lg ldf6920bb fully in...</td>\n",
              "      <td>[CLS]  [COL] name [VAL] lg 24 ' ldf6920ww ful...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] name [VAL] lg 24 ' ldf6920st ful...</td>\n",
              "      <td>[SEP]  [COL] name [VAL] lg ldf6920bb fully in...</td>\n",
              "      <td>[CLS]  [COL] name [VAL] lg 24 ' ldf6920st ful...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  ...                                             tuples\n",
              "0      0  ...   [CLS]  [COL] name [VAL] lg 24 ' lds4821ww sem...\n",
              "1      0  ...   [CLS]  [COL] name [VAL] lg 24 ' lds4821bb sem...\n",
              "2      1  ...   [CLS]  [COL] name [VAL] lg 24 ' ldf6920bb ful...\n",
              "3      0  ...   [CLS]  [COL] name [VAL] lg 24 ' ldf6920ww ful...\n",
              "4      0  ...   [CLS]  [COL] name [VAL] lg 24 ' ldf6920st ful...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0avwe15ixVf"
      },
      "source": [
        "AG_train = AG_train.groupby('label')\n",
        "AG_train = AG_train.apply(lambda x: x.sample(AG_train.size().min()).reset_index(drop=True))\n",
        "AG_train = AG_train.sample(frac=1).reset_index(drop=True)\n",
        "AG_test = AG_test.groupby('label')\n",
        "AG_test = AG_test.apply(lambda x: x.sample(AG_test.size().min()).reset_index(drop=True))\n",
        "AG_test = AG_test.sample(frac=1).reset_index(drop=True)\n",
        "AG_valid = AG_valid.groupby('label')\n",
        "AG_valid = AG_valid.apply(lambda x: x.sample(AG_valid.size().min()).reset_index(drop=True))\n",
        "AG_valid = AG_valid.sample(frac=1).reset_index(drop=True)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nD1JewJYh0sI"
      },
      "source": [
        "\n",
        "import os \n",
        "try:\n",
        "  os.mkdir('merged_data/abt_buy/')\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "AG_train.to_csv(r'merged_data/abt_buy/train.csv')\n",
        "AG_test.to_csv(r'merged_data/abt_buy/test.csv')\n",
        "AG_valid.to_csv(r'merged_data/abt_buy/valid.csv')\n"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89,
          "referenced_widgets": [
            "90de6c384ce1479d9ecadd0d23383565",
            "8e61b308bc5a4e4cae344ebb8eda5984",
            "9a341f99af324a94b4f40fb3c8cf9831",
            "6925dcc766064397abc91fdda5f0b992",
            "727eeac0a98245d3974fe9e56b1443f4",
            "cd54b3700cbb4956a446d9713b1ad146",
            "89533613b64a4ea79d9e0cf3e0eafb4d",
            "44dc382f2855431c9e806b24c484862f",
            "499087777306432fbaa9457203418483",
            "fc664877bdc348268c9f1c8a5c738d53",
            "cd91620779eb4a5ab9ea78267faa7353",
            "eabe1b30d32f4b72a4ba9f2e898d431f",
            "d083bb2bb4f54082a76f542b51d53db6",
            "65c2d4eef02c488ba8a2cf257409e4c8",
            "5af5658d5aa44aadbbed45f6211d5690",
            "267c00defcf745cf93c7d8de6e8e8a57"
          ]
        },
        "id": "2_51cpB6h3XN",
        "outputId": "677eee32-db41-49dc-b10e-bacf82167844"
      },
      "source": [
        "from datasets import load_dataset\n",
        "raw_datasets = load_dataset('csv',data_files={'train':['merged_data/abt_buy/train.csv','merged_data/abt_buy/valid.csv'],\n",
        "                                              'test':'merged_data/abt_buy/test.csv',\n",
        "                                              })"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-d734a5cdf9f9c19f\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/csv/default-d734a5cdf9f9c19f/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "90de6c384ce1479d9ecadd0d23383565",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "499087777306432fbaa9457203418483",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-d734a5cdf9f9c19f/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289,
          "referenced_widgets": [
            "b36dea99a89a4c74a7def5781141c518",
            "5f05dc6fb60545bca2c01557d835f48a",
            "99f7a85184bb41ea85974afecc15df71",
            "925f046d195f458eac09328af2b12942",
            "4ab5cbdade344a01a2d875c6360ede7c",
            "41a6ce57857345e5832c17d361897dc9",
            "b0fc752557c04ad8bc66a7aeaf0b94c6",
            "bd4bbd44672a4d179a240d601d610708",
            "cadaf804b3824ff481f22ef9935bac21",
            "cee1c513a2a44dcaa77a970fb8cc1c5f",
            "02b87c0500464ea6a2fe5fbe20c56fe7",
            "ea6cd030084945dfaea5da3a0ac7e966",
            "3948481d72aa473fb4c89c16a6c42646",
            "88bc03058f4446c6bd6b623a5aaf0fa4",
            "66fb8efc851343c6a682e870ac624f08",
            "53ed500e4bfe4666a1a8eda17acc31c3"
          ]
        },
        "id": "ANQRHIpZh_Ro",
        "outputId": "cdb885c9-e50f-43d7-a6da-bafd2b132331"
      },
      "source": [
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "tokenized_datasets = tokenized_datasets.remove_columns('Unnamed: 0')\n",
        "tokenized_datasets"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b36dea99a89a4c74a7def5781141c518",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=7.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cadaf804b3824ff481f22ef9935bac21",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'label', 'text_left', 'text_right', 'token_type_ids', 'tuples'],\n",
              "        num_rows: 6572\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'label', 'text_left', 'text_right', 'token_type_ids', 'tuples'],\n",
              "        num_rows: 412\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtnwcuhhiDJs",
        "outputId": "8193735c-abc4-4320-d235-b513703fd064"
      },
      "source": [
        "torch.cuda.empty_cache()\n",
        "training_args = TrainingArguments(\"/content/drive/MyDrive/XLNet/abt-Buy\",num_train_epochs=14,per_device_train_batch_size=16)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KY4PN2qEiGET",
        "outputId": "905cd89d-555b-4b52-807d-e45b63edd8c0"
      },
      "source": [
        "tokenizer.decode(tokenized_datasets['train']['input_ids'][1])\n",
        "trainer = Trainer(\n",
        "    model=model, args=training_args, train_dataset=tokenized_datasets['train'], eval_dataset=tokenized_datasets['test']\n",
        ")\n",
        "trainer.train()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `ConvBertForSequenceClassification.forward` and have been ignored: text_left, tuples, text_right.\n",
            "***** Running training *****\n",
            "  Num examples = 6572\n",
            "  Num Epochs = 14\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 5754\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5754' max='5754' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5754/5754 29:26, Epoch 14/14]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.187300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.177900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.162400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.158100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.155100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.151500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.157300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.145300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.136200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.136900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.130900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-500/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-1000\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-1000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-1000/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-1500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-1500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-1500/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-2000\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-2000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-2000/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-2500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-2500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-2500/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-3000\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-3000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-3000/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-3500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-3500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-3500/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-4000\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-4000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-4000/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-4500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-4500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-4500/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-5000\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-5000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-5000/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-5500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-5500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/abt-Buy/checkpoint-5500/pytorch_model.bin\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=5754, training_loss=0.15352084001071095, metrics={'train_runtime': 1766.7037, 'train_samples_per_second': 52.079, 'train_steps_per_second': 3.257, 'total_flos': 7509453297561600.0, 'train_loss': 0.15352084001071095, 'epoch': 14.0})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "s1er3VBeiIj8",
        "outputId": "fbadfd64-6ea6-4b92-d01e-fee91c047b59"
      },
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"f1\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_datasets['train'],\n",
        "    eval_dataset=tokenized_datasets['test'],\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "trainer.evaluate()\n"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `ConvBertForSequenceClassification.forward` and have been ignored: text_left, tuples, text_right.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 412\n",
            "  Batch size = 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='52' max='52' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [52/52 00:02]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_f1': 0.7533875338753389,\n",
              " 'eval_loss': 1.3691786527633667,\n",
              " 'eval_runtime': 2.6576,\n",
              " 'eval_samples_per_second': 155.028,\n",
              " 'eval_steps_per_second': 19.567}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQSZdyP4i2hQ"
      },
      "source": [
        "## dirty_walmart_amazon"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "TF9SvBZxi7GE",
        "outputId": "8b17bd93-ef60-4acd-cd4a-71dc35a5a21e"
      },
      "source": [
        "table_A = pd.read_csv(dirty_walmart_amazon+r'/deep_matcher/tableA.csv')\n",
        "table_B = pd.read_csv(dirty_walmart_amazon+r'/deep_matcher/tableB.csv')\n",
        "AG_train = pd.read_csv(dirty_walmart_amazon+r'/deep_matcher/train.csv')\n",
        "AG_test = pd.read_csv(dirty_walmart_amazon+r'/deep_matcher/test.csv')\n",
        "AG_valid = pd.read_csv(dirty_walmart_amazon+r'/deep_matcher/valid.csv')\n",
        "AG_train, AG_test, AG_valid = merger(table_A,table_B,AG_train,AG_test,AG_valid)\n",
        "AG_train.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1715: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  isetter(loc, v)\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:45: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text_left</th>\n",
              "      <th>text_right</th>\n",
              "      <th>tuples</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] elite screens cinewh...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] cinegray ezframe ser...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] elite screens cinewh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] elite screens cinewh...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] powergain prime visi...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] elite screens cinewh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] elite screens cinegr...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] powergain prime visi...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] elite screens cinegr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] elite screens cinewh...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] cinewhite prime visi...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] elite screens cinewh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>[CLS]  [COL] title [VAL] elite screens cinegr...</td>\n",
              "      <td>[SEP]  [COL] title [VAL] cinewhite prime visi...</td>\n",
              "      <td>[CLS]  [COL] title [VAL] elite screens cinegr...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  ...                                             tuples\n",
              "0      0  ...   [CLS]  [COL] title [VAL] elite screens cinewh...\n",
              "1      0  ...   [CLS]  [COL] title [VAL] elite screens cinewh...\n",
              "2      0  ...   [CLS]  [COL] title [VAL] elite screens cinegr...\n",
              "3      0  ...   [CLS]  [COL] title [VAL] elite screens cinewh...\n",
              "4      0  ...   [CLS]  [COL] title [VAL] elite screens cinegr...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CL7Pg7ltjGgL"
      },
      "source": [
        "AG_train = AG_train.groupby('label')\n",
        "AG_train = AG_train.apply(lambda x: x.sample(AG_train.size().min()).reset_index(drop=True))\n",
        "AG_train = AG_train.sample(frac=1).reset_index(drop=True)\n",
        "AG_test = AG_test.groupby('label')\n",
        "AG_test = AG_test.apply(lambda x: x.sample(AG_test.size().min()).reset_index(drop=True))\n",
        "AG_test = AG_test.sample(frac=1).reset_index(drop=True)\n",
        "AG_valid = AG_valid.groupby('label')\n",
        "AG_valid = AG_valid.apply(lambda x: x.sample(AG_valid.size().min()).reset_index(drop=True))\n",
        "AG_valid = AG_valid.sample(frac=1).reset_index(drop=True)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S68xm48bjATj"
      },
      "source": [
        "import os \n",
        "try:\n",
        "  os.mkdir('merged_data/dirty_walmart_amazon/')\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "AG_train.to_csv(r'merged_data/dirty_walmart_amazon/train.csv')\n",
        "AG_test.to_csv(r'merged_data/dirty_walmart_amazon/test.csv')\n",
        "AG_valid.to_csv(r'merged_data/dirty_walmart_amazon/valid.csv')\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89,
          "referenced_widgets": [
            "9a774df74b5442d99ff17ae3b0f684c9",
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            "3b4e017d05c64ef99d770af3f5ec72ee",
            "a487df19bb7b4afb879a8d5e352b869b",
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            "d11b8c245c4744e7a939cab11fe92d1f",
            "fafb1529091a42c1a2dc9323d8bd719d",
            "bcb89abd8d354acfab800d337a54c723",
            "fc62d22e72d84504bbdff614537af84f",
            "5db41c563cf54f538173a26f7330a515",
            "97bf767591474ae49a83f0fc42b11131",
            "5c20cca8d31d4d62b8366fab4b5cb3c6",
            "8e96b33d1ef849dab1b43b6a11001a22",
            "4dc8ae2d30fd4a9aa3199d8691e21fff",
            "cea1a6f275b34480a2393dacf8c4af25"
          ]
        },
        "id": "Ew86tFQbjO0g",
        "outputId": "80758289-23a3-42d5-cc1c-f1a80c213554"
      },
      "source": [
        "from datasets import load_dataset\n",
        "raw_datasets = load_dataset('csv',data_files={'train':['merged_data/dirty_walmart_amazon/train.csv','merged_data/dirty_walmart_amazon/valid.csv'],\n",
        "                                              'test':'merged_data/dirty_walmart_amazon/test.csv',\n",
        "                                              })"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-bc972e8e60874d5d\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/csv/default-bc972e8e60874d5d/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a774df74b5442d99ff17ae3b0f684c9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bcb89abd8d354acfab800d337a54c723",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-bc972e8e60874d5d/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289,
          "referenced_widgets": [
            "982e1ea08c7c4d71807688451773eadc",
            "bd9e9f0902fa49a787bf3bed78e5f95b",
            "c82d8ccf13734aca9a24bc0dbb0e43e3",
            "47a4f6de268a4061b59ac8da9c8a8d94",
            "22ce3865474e48789ff9e9e8097b7546",
            "ad3190767e8e43fcb1b00662fe52e69c",
            "a21ac7e961364fe8989b0bbea4ff373b",
            "689ebe61c30b41b8bbfe0586a2a811f7",
            "b305dfd5de1c494caa4f32b93226cb88",
            "7dde357f4cc34d83981c59fdfcfde48c",
            "ba1efae5bccd4f6ebce5f26babd80897",
            "33acd221b8154051aa8e12f331f15e63",
            "e39f8a349611438d918b0e7d47861509",
            "4e0f2c5adb9e439cb6e91902b98ace82",
            "386d0d5cb767432996ff88ca54f4b86c",
            "23f2797758e742c49e0379066035db0d"
          ]
        },
        "id": "To1Yuwm4jRsW",
        "outputId": "a9626c0b-d105-4937-8041-34ec373655cf"
      },
      "source": [
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "tokenized_datasets = tokenized_datasets.remove_columns('Unnamed: 0')\n",
        "tokenized_datasets"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "982e1ea08c7c4d71807688451773eadc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=7.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b305dfd5de1c494caa4f32b93226cb88",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'label', 'text_left', 'text_right', 'token_type_ids', 'tuples'],\n",
              "        num_rows: 6146\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'label', 'text_left', 'text_right', 'token_type_ids', 'tuples'],\n",
              "        num_rows: 386\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruEXOZqFjULE",
        "outputId": "17c0534d-da6e-4516-9ff9-8bde4c93777b"
      },
      "source": [
        "torch.cuda.empty_cache()\n",
        "training_args = TrainingArguments(\"/content/drive/MyDrive/XLNet/dirty_walmart_amazon\",num_train_epochs=14,per_device_train_batch_size=16)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rSY180LHjXIr",
        "outputId": "736fcc75-7d3d-4ec4-e0c6-d4673bc5cc4a"
      },
      "source": [
        "tokenizer.decode(tokenized_datasets['train']['input_ids'][1])\n",
        "trainer = Trainer(\n",
        "    model=model, args=training_args, train_dataset=tokenized_datasets['train'], eval_dataset=tokenized_datasets['test']\n",
        ")\n",
        "trainer.train()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `ConvBertForSequenceClassification.forward` and have been ignored: text_left, tuples, text_right.\n",
            "***** Running training *****\n",
            "  Num examples = 6146\n",
            "  Num Epochs = 14\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 5390\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5390' max='5390' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5390/5390 27:21, Epoch 14/14]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.218800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.069600</td>\n",
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              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.034800</td>\n",
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              "      <td>2000</td>\n",
              "      <td>0.024700</td>\n",
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              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.016700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.006600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.005600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.005000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.002300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.001700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-500/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-1000\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-1000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-1000/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-1500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-1500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-1500/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-2000\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-2000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-2000/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-2500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-2500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-2500/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-3000\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-3000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-3000/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-3500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-3500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-3500/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-4000\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-4000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-4000/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-4500\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-4500/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-4500/pytorch_model.bin\n",
            "Saving model checkpoint to /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-5000\n",
            "Configuration saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-5000/config.json\n",
            "Model weights saved in /content/drive/MyDrive/XLNet/dirty_walmart_amazon/checkpoint-5000/pytorch_model.bin\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=5390, training_loss=0.03601146066122462, metrics={'train_runtime': 1641.8266, 'train_samples_per_second': 52.407, 'train_steps_per_second': 3.283, 'total_flos': 7022687152588800.0, 'train_loss': 0.03601146066122462, 'epoch': 14.0})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "nvhUCzfjjbNt",
        "outputId": "435ae4d5-82ea-45c6-d8c8-099c9f596ad7"
      },
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"f1\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_datasets['train'],\n",
        "    eval_dataset=tokenized_datasets['test'],\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "trainer.evaluate()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `ConvBertForSequenceClassification.forward` and have been ignored: text_left, tuples, text_right.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 386\n",
            "  Batch size = 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='49' max='49' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [49/49 00:02]\n",
              "    </div>\n",
              "    "
            ],
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            ]
          },
          "metadata": {
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_f1': 0.9116022099447514,\n",
              " 'eval_loss': 0.8740784525871277,\n",
              " 'eval_runtime': 3.1101,\n",
              " 'eval_samples_per_second': 124.113,\n",
              " 'eval_steps_per_second': 15.755}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    }
  ]
}