XLnet_Base_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/XLnet_Base_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": "FhOtNAWXf31r"
      },
      "source": [
        "!rm -r  test_trainer "
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3yqQXwX2iG03",
        "outputId": "06df26e4-d6da-47b7-8516-b2a0e1f26d8e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rL6YqrRWb6PW",
        "outputId": "5524a1cd-2331-4d8b-b210-9d917a57c529"
      },
      "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",
            "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: 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: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\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: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.45)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub==0.0.12->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\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->transformers) (1.24.3)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (1.8.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.7/dist-packages (from datasets) (2021.6.1)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from datasets) (20.9)\n",
            "Requirement already satisfied: tqdm<4.50.0,>=4.27 in /usr/local/lib/python3.7/dist-packages (from datasets) (4.41.1)\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",
            "Requirement already satisfied: huggingface-hub<0.1.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.0.12)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.1.5)\n",
            "Requirement already satisfied: pyarrow<4.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (3.0.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.19.5)\n",
            "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: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->datasets) (2.4.7)\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: 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: 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: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2.10)\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: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<0.1.0->datasets) (3.7.4.3)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2018.9)\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: 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: 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": 237
        },
        "id": "S33VggvBYcrU",
        "outputId": "53de6e9b-1247-403f-b88a-772c37de29d6"
      },
      "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": 6,
      "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",
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              "      <th>label</th>\n",
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              "      <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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "6tK5Wc5QbCoV",
        "outputId": "aa33684f-5d1d-48ff-c818-48fe2e8b4df6"
      },
      "source": [
        "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",
        "remove_id_columns(total_data)"
      ],
      "execution_count": 7,
      "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>price_x</th>\n",
              "      <th>describtion_x</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>0.2</td>\n",
              "      <td>samsung s5</td>\n",
              "      <td>0.8</td>\n",
              "      <td>samsung s5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>iphone</td>\n",
              "      <td>0.2</td>\n",
              "      <td>iphone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>xperia</td>\n",
              "      <td>0.5</td>\n",
              "      <td>xperia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>sonny</td>\n",
              "      <td>0.6</td>\n",
              "      <td>sonny</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>hstc</td>\n",
              "      <td>0.7</td>\n",
              "      <td>huawi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>0.8</td>\n",
              "      <td>xomi</td>\n",
              "      <td>1.0</td>\n",
              "      <td>xomi</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  price_x describtion_x  price_y describtion_y\n",
              "0      1      0.2    samsung s5      0.8    samsung s5\n",
              "1      1      0.5        iphone      0.2        iphone\n",
              "2      1      0.6        xperia      0.5        xperia\n",
              "3      1      0.7         sonny      0.6         sonny\n",
              "4      0      1.0          hstc      0.7         huawi\n",
              "5      1      0.8          xomi      1.0          xomi"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i94LLZFlP-JU"
      },
      "source": [
        "total_data =remove_id_columns(total_data)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "KWnYo7eKQBt4",
        "outputId": "bff7d461-9443-4de4-eb63-e0043036f0b5"
      },
      "source": [
        "total_data.astype(str).apply(lambda x : ' <atr>'+x.name.strip('_y').strip('_x') +'<val>'+x)"
      ],
      "execution_count": 9,
      "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>price_x</th>\n",
              "      <th>describtion_x</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>&lt;atr&gt;label&lt;val&gt;1</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;0.2</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;samsung s5</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;0.8</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;samsung s5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;atr&gt;label&lt;val&gt;1</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;0.5</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;iphone</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;0.2</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;iphone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>&lt;atr&gt;label&lt;val&gt;1</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;0.6</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;xperia</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;0.5</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;xperia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>&lt;atr&gt;label&lt;val&gt;1</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;0.7</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;sonny</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;0.6</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;sonny</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>&lt;atr&gt;label&lt;val&gt;0</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;1.0</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;hstc</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;0.7</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;huawi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>&lt;atr&gt;label&lt;val&gt;1</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;0.8</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;xomi</td>\n",
              "      <td>&lt;atr&gt;price&lt;val&gt;1.0</td>\n",
              "      <td>&lt;atr&gt;describtion&lt;val&gt;xomi</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               label  ...                     describtion_y\n",
              "0   <atr>label<val>1  ...   <atr>describtion<val>samsung s5\n",
              "1   <atr>label<val>1  ...       <atr>describtion<val>iphone\n",
              "2   <atr>label<val>1  ...       <atr>describtion<val>xperia\n",
              "3   <atr>label<val>1  ...        <atr>describtion<val>sonny\n",
              "4   <atr>label<val>0  ...        <atr>describtion<val>huawi\n",
              "5   <atr>label<val>1  ...         <atr>describtion<val>xomi\n",
              "\n",
              "[6 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vvfRbO2shpk"
      },
      "source": [
        "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",
        "remove_id_columns(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",
        "    train_data.iloc[:,1:]  = train_data.iloc[:,1:].astype(str).apply(lambda x : ' <atr>'+x.name.strip('_y').strip('_x') +'<val>'+x)\n",
        "    train_data['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",
        "    train_data['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",
        "    test_data.iloc[:,1:]  = test_data.iloc[:,1:].astype(str).apply(lambda x : ' <atr>'+x.name.strip('_y').strip('_x') +'<val>'+x)\n",
        "    test_data['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",
        "    test_data['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",
        "    valid_data.iloc[:,1:]  = valid_data.iloc[:,1:].astype(str).apply(lambda x : ' <atr>'+x.name.strip('_y').strip('_x') +'<val>'+x)\n",
        "    valid_data['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",
        "    valid_data['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",
        "\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": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6hjyb55OqOP"
      },
      "source": [
        ""
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1KTosSUs9QX"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mgcc16oDs3B6"
      },
      "source": [
        "## Amazon Google data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPcQGI4lD2uP"
      },
      "source": [
        "AG_train = pd.read_csv(amazon_google+r'/deep_matcher/train.csv')\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PkgGgxR9smGS",
        "outputId": "f6d59a71-f647-4ac1-f8f3-6d513ed3cc09"
      },
      "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": 12,
      "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>&lt;cls&gt; &lt;atr&gt;title&lt;val&gt;microsoft visio standard ...</td>\n",
              "      <td>&lt;sep&gt; &lt;atr&gt;title&lt;val&gt;adobe cs3 design standard...</td>\n",
              "      <td>&lt;cls&gt; &lt;atr&gt;title&lt;val&gt;microsoft visio standard ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>&lt;cls&gt; &lt;atr&gt;title&lt;val&gt;adobe premiere pro cs3 up...</td>\n",
              "      <td>&lt;sep&gt; &lt;atr&gt;title&lt;val&gt;adobe cs3 design standard...</td>\n",
              "      <td>&lt;cls&gt; &lt;atr&gt;title&lt;val&gt;adobe premiere pro cs3 up...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>&lt;cls&gt; &lt;atr&gt;title&lt;val&gt;adobe indesign cs3 upgrad...</td>\n",
              "      <td>&lt;sep&gt; &lt;atr&gt;title&lt;val&gt;adobe cs3 design standard...</td>\n",
              "      <td>&lt;cls&gt; &lt;atr&gt;title&lt;val&gt;adobe indesign cs3 upgrad...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>&lt;cls&gt; &lt;atr&gt;title&lt;val&gt;adobe creative suite cs3 ...</td>\n",
              "      <td>&lt;sep&gt; &lt;atr&gt;title&lt;val&gt;adobe cs3 design standard...</td>\n",
              "      <td>&lt;cls&gt; &lt;atr&gt;title&lt;val&gt;adobe creative suite cs3 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>&lt;cls&gt; &lt;atr&gt;title&lt;val&gt;adobe creative suite cs3 ...</td>\n",
              "      <td>&lt;sep&gt; &lt;atr&gt;title&lt;val&gt;adobe cs3 design standard...</td>\n",
              "      <td>&lt;cls&gt; &lt;atr&gt;title&lt;val&gt;adobe creative suite cs3 ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  ...                                             tuples\n",
              "0      0  ...  <cls> <atr>title<val>microsoft visio standard ...\n",
              "1      0  ...  <cls> <atr>title<val>adobe premiere pro cs3 up...\n",
              "2      0  ...  <cls> <atr>title<val>adobe indesign cs3 upgrad...\n",
              "3      0  ...  <cls> <atr>title<val>adobe creative suite cs3 ...\n",
              "4      0  ...  <cls> <atr>title<val>adobe creative suite cs3 ...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "1eiIc3WjuVlt",
        "outputId": "f2a75b8b-4c72-4259-98ce-78b1c66e7495"
      },
      "source": [
        "AG_train['tuples'][0]"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'<cls> <atr>title<val>microsoft visio standard 2007 version upgrade  <atr>manufacturer<val>microsoft  <atr>price<val>129.95<sep><sep> <atr>title<val>adobe cs3 design standard upgrade  <atr>manufacturer<val>nan  <atr>price<val>413.99'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8snDJ4v-kDEY",
        "outputId": "c7cf4bc7-923e-47cb-9f23-278375d7daca"
      },
      "source": [
        "AG_train['label'].describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    6874.000000\n",
              "mean        0.101688\n",
              "std         0.302259\n",
              "min         0.000000\n",
              "25%         0.000000\n",
              "50%         0.000000\n",
              "75%         0.000000\n",
              "max         1.000000\n",
              "Name: label, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mMMDfrXCUbu"
      },
      "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": 15,
      "outputs": []
    },
    {
      "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": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92,
          "referenced_widgets": [
            "67b8195a1db24490871e22c9d6ee9ed8",
            "d752d19fe55d4e36878e35410810bfbe",
            "21c407f628d94a1d9105d79fe98cb7f8",
            "56cde73afef44fa18b784340297a27d8",
            "e45c4a0d618e465fb9853e6b8958a1af",
            "aff643bbca5c46168d7190b74f7bfff4",
            "047b144223704a0b8b67eab2b4a38b1e",
            "af1da035d36b4c22bea1c0ecb30fefc5",
            "421041b83ca542faa0d95ade546dd7a9",
            "38931aca2bac464295279966c0111b3e",
            "09acad6073174d2cb4e69258e140a3c0",
            "436f45e76ab04b06864339b1a10ba49b",
            "230371d3498f4dd480c38e4cfbde756f",
            "8668d6a3b4734e809275d890a822cfe9",
            "eedae0a692ef47938d2e7721918977d5",
            "bbefb5148fbc4cfa8234546cdecfa1ce"
          ]
        },
        "id": "ZsbzHx0GrAQY",
        "outputId": "ea400db7-9f37-4af4-f8e9-97c7de69b3a8"
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default-a6af5151775a091a\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-a6af5151775a091a/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "67b8195a1db24490871e22c9d6ee9ed8",
              "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": "421041b83ca542faa0d95ade546dd7a9",
              "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-a6af5151775a091a/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(\"xlnet-base-cased\")"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "aa6d53940c0241438f784bd0d8be88b4",
            "fb20e6dd237e4371adfb8bfc53ffc422",
            "24ad2a678a594a8d84f4217610c77f07",
            "01997b42b387417a97db1d2cb28b5b9d",
            "916db7ecffb741d7b75099ed651844c9",
            "8f61772c484a43b5a3cdba1328349a75",
            "6979cec81bff4507a58c6ae962178e9c",
            "21661a28c6624bd480b7016299d04c15",
            "d5a95a63abd94446b024fc120e226628",
            "9886644638b348be93da96705b56a674",
            "50f9ce23ab164aba8418d575ef3612db",
            "4d272a70381f4d0aa511080dd6f99ba9",
            "70e898e59c3843c9ad46056088e44800",
            "97b5791303204667809efb5e21784099",
            "864b1877dd4643cfaae0ed42eea1e238",
            "93414729693b44aeb8be2c422cb0b535"
          ]
        },
        "id": "ArLWe4hP-9Vb",
        "outputId": "781d1838-f118-4c95-fed0-26612bc719cc"
      },
      "source": [
        "maxlen = 512\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples['tuples'], padding=\"max_length\", truncation=True,max_length=maxlen)\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aa6d53940c0241438f784bd0d8be88b4",
              "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"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d5a95a63abd94446b024fc120e226628",
              "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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vv1FWbZjRmdY",
        "outputId": "feac17ab-4453-45ea-ca65-e3a3042aeea7"
      },
      "source": [
        "tokenized_datasets = tokenized_datasets.remove_columns('Unnamed: 0')\n",
        "tokenized_datasets"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "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: 1866\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['attention_mask', 'input_ids', 'label', 'text_left', 'text_right', 'token_type_ids', 'tuples'],\n",
              "        num_rows: 468\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBmPWvjBb3S4"
      },
      "source": [
        "import torch\n",
        "torch.cuda.empty_cache()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DgJ7bXw_hNLL",
        "outputId": "e12b63dc-8eb8-4cd4-c50f-26f061c421ec"
      },
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"xlnet-base-cased\", num_labels=2)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at xlnet-base-cased were not used when initializing XLNetForSequenceClassification: ['lm_loss.bias', 'lm_loss.weight']\n",
            "- This IS expected if you are initializing XLNetForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing XLNetForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of XLNetForSequenceClassification were not initialized from the model checkpoint at xlnet-base-cased and are newly initialized: ['sequence_summary.summary.bias', 'sequence_summary.summary.weight', 'logits_proj.bias', 'logits_proj.weight']\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": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "dOmR6A2JtAoI",
        "outputId": "711abf97-ee09-44f0-a404-6f6a226a3f0f"
      },
      "source": [
        "tokenizer.decode(tokenized_datasets['train']['input_ids'][1])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'<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><cls> <atr>title<val>logic express 7 <atr>manufacturer<val>apple computer <atr>price<val>299.0<sep><sep> <atr>title<val>apple logic express 7.2 <atr>manufacturer<val>nan <atr>price<val>299.99<sep><cls>'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "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": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "QVUNIuEFRLZ3",
        "outputId": "495db0e7-3213-47f4-e50a-cc182f5eeafa"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `XLNetForSequenceClassification.forward` and have been ignored: text_left, tuples, text_right.\n",
            "***** Running training *****\n",
            "  Num examples = 1866\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 = 1638\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1638' max='1638' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1638/1638 15:02, 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>0.708400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.706400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.699900</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",
            "\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=1638, training_loss=0.7044061700357477, metrics={'train_runtime': 903.025, 'train_samples_per_second': 28.929, 'train_steps_per_second': 1.814, 'total_flos': 2353627095386112.0, 'train_loss': 0.7044061700357477, 'epoch': 14.0})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJpXMTl7YG45"
      },
      "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": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXeywnCCvm5q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "bb118aec-fc90-401e-bca9-98d73b4a2e76"
      },
      "source": [
        "tokenizer.decode(tokenized_datasets['train']['input_ids'][0])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'<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><cls> <atr>title<val>microsoft data analyzer 2002 <atr>manufacturer<val>microsoft <atr>price<val>179.0<sep><sep> <atr>title<val>microsoft ( r ) money 2007 deluxe <atr>manufacturer<val>nan <atr>price<val>49.99<sep><cls>'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTNUm3fRYcsD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "3d20052a-f26e-4ea5-d62f-72fce5d9c3a8"
      },
      "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": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `XLNetForSequenceClassification.forward` and have been ignored: text_left, tuples, text_right.\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 00:05]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_f1': 0.6342412451361868,\n",
              " 'eval_loss': 0.6930510401725769,\n",
              " 'eval_runtime': 5.3129,\n",
              " 'eval_samples_per_second': 88.087,\n",
              " 'eval_steps_per_second': 11.105}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1kpyqSYh9HN"
      },
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"accuracy\")\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": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFb5XJ0bh-8s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "b6a2c70c-18f9-4ca2-b275-fe5c8ba98a1e"
      },
      "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": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `XLNetForSequenceClassification.forward` and have been ignored: text_left, tuples, text_right.\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 00:05]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_accuracy': 0.5982905982905983,\n",
              " 'eval_loss': 0.6930510401725769,\n",
              " 'eval_runtime': 5.3172,\n",
              " 'eval_samples_per_second': 88.017,\n",
              " 'eval_steps_per_second': 11.096}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    }
  ]
}