pretrain_PX_BERT.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "5S3cySBS0h4z"
      },
      "source": [
        "Load data from google drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8LWjWqx_1Ttt",
        "outputId": "3d403e17-e008-4f55-e0af-1e4d3c232ed8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "# mount Google Drive\n",
        "from google.colab import drive\n",
        "#drive/MyDrive/Colab/thesis_2\n",
        "drive.mount(\"/content/drive/\")\n",
        "path = \"/content/drive/My Drive/\"\n",
        "\n",
        "# ------- ------- -------\n",
        "import sys\n",
        "sys.path.insert(0,\"/content/drive/My Drive/\")\n",
        "#https://www.kaggle.com/code/arnabs007/pretrain-a-bert-language-model-from-scratch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "2Sprelpt0h43",
        "outputId": "2794fca0-e4dc-40de-99cb-9af075bd7203",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (1.12.1+cu113)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch) (4.1.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tokenizers\n",
            "  Downloading tokenizers-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 5.0 MB/s \n",
            "\u001b[?25hInstalling collected packages: tokenizers\n",
            "Successfully installed tokenizers-0.13.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.24.0-py3-none-any.whl (5.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 5.5 MB 5.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.13.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Collecting huggingface-hub<1.0,>=0.10.0\n",
            "  Downloading huggingface_hub-0.10.1-py3-none-any.whl (163 kB)\n",
            "\u001b[K     |████████████████████████████████| 163 kB 83.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.13.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.10.0)\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: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\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: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Installing collected packages: huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.10.1 transformers-4.24.0\n"
          ]
        }
      ],
      "source": [
        "!pip install torch\n",
        "!pip install tokenizers\n",
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1oCvrUIo1j05"
      },
      "outputs": [],
      "source": [
        "\"\"\"import torch\n",
        "import tokenizers\n",
        "import transformers\"\"\""
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "iEavIhpn0h45"
      },
      "source": [
        "# **Tokenization**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VUsC0mRl0h45",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# Train a tokenizer\n",
        "import tokenizers\n",
        "\n",
        "bwpt = tokenizers.BertWordPieceTokenizer()\n",
        "\n",
        "filepath = path + 'myPreprocessing_PX_Comments.txt'\n",
        "\n",
        "bwpt.train(\n",
        "    files=[filepath],\n",
        "    vocab_size=50000,\n",
        "    min_frequency=3,\n",
        "    limit_alphabet=1000\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zzEIPtGN2VdV"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "file_tok = './tokenizer'\n",
        "os.mkdir(file_tok)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7pkZa4bQ24vH",
        "outputId": "00d72a30-c112-4c6d-eec4-b8d1806db425"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['./tokenizer/vocab.txt']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bwpt.save(file_tok + '/config.json')\n",
        "bwpt.save_model(file_tok )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_BmkJkYv0h46",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# Load the tokenizer\n",
        "from transformers import BertTokenizer, LineByLineTextDataset\n",
        "\n",
        "vocab_file_dir = './tokenizer'\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(vocab_file_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTvdVVoG2vfW",
        "outputId": "c0850985-aef7-469c-fc06-7134e1f3c04c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['النظافه', 'ليست', 'بالشكل', 'المامول']\n"
          ]
        }
      ],
      "source": [
        "# display the tokens\n",
        "sentence = \"النظافه ليست بالشكل المأمول\"\n",
        "\n",
        "encoded_input = tokenizer.tokenize(sentence)\n",
        "print(encoded_input)\n",
        "# print(encoded_input['input_ids'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rOmA_nsA0h47",
        "outputId": "90773301-856a-45cf-e170-eb11ac2fb566",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/data/datasets/language_modeling.py:125: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "No. of lines:  943365\n"
          ]
        }
      ],
      "source": [
        "#%%time\n",
        "\n",
        "'''\n",
        "transformers has a predefined class LineByLineTextDataset()\n",
        "which reads your text line by line and converts them to tokens\n",
        "'''\n",
        "\n",
        "dataset= LineByLineTextDataset(\n",
        "    tokenizer = tokenizer,\n",
        "    file_path = path + 'xclean_tnkeeh_myPreprocessing.txt',\n",
        "    block_size = 512  # maximum sequence length\n",
        ")\n",
        "\n",
        "print('No. of lines: ', len(dataset)) # No of lines in your datset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYq2ac68412v",
        "outputId": "80e9728f-6d62-455a-9dab-296a3d65ee27"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "943365"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(dataset.examples)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "K5wXsrP80h48"
      },
      "source": [
        "# **Defining model**\n",
        "\n",
        "\n",
        "MLM model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S7e7WQp20h48",
        "outputId": "14b525eb-b8b9-426d-9506-151def1a6ec5",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "No of parameters:  81965648\n"
          ]
        }
      ],
      "source": [
        "from transformers import BertConfig, BertForMaskedLM, DataCollatorForLanguageModeling\n",
        "\n",
        "config = BertConfig(\n",
        "    vocab_size=50000,\n",
        "    hidden_size=768,\n",
        "    num_hidden_layers=6,\n",
        "    num_attention_heads=12,\n",
        "    max_position_embeddings=512\n",
        ")\n",
        "\n",
        "model = BertForMaskedLM(config)\n",
        "print('No of parameters: ', model.num_parameters())\n",
        "\n",
        "\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer, mlm=True, mlm_probability=0.15\n",
        ")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "BQfjD-ZD0h49"
      },
      "source": [
        "# **training arguments**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4UvYs3Bj0h49",
        "outputId": "f85a90b6-8fc1-4aa3-ca18-134c3260144c",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "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"
          ]
        }
      ],
      "source": [
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./output_dir',\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=10,\n",
        "    per_device_train_batch_size=32,\n",
        "    save_steps=10_000,\n",
        "    save_total_limit=2,\n",
        "    prediction_loss_only=True,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=dataset,\n",
        ")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "0DynuSkF0h49"
      },
      "source": [
        "# **Training**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_Mb2f9oK0h4-",
        "outputId": "e1ed918e-927d-4658-fdf5-3836fb41bb2b",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 943365\n",
            "  Num Epochs = 10\n",
            "  Instantaneous batch size per device = 32\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 294810\n",
            "  Number of trainable parameters = 81965648\n"
          ]
        },
        {
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              "      <progress value='294810' max='294810' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [294810/294810 8:23:58, Epoch 10/10]\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",
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              "      <td>6.274700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22000</td>\n",
              "      <td>6.291200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22500</td>\n",
              "      <td>6.226500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23000</td>\n",
              "      <td>6.192700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23500</td>\n",
              "      <td>6.211800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24000</td>\n",
              "      <td>6.141500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24500</td>\n",
              "      <td>6.138500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25000</td>\n",
              "      <td>6.118400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25500</td>\n",
              "      <td>6.134500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26000</td>\n",
              "      <td>6.095500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26500</td>\n",
              "      <td>6.122700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27000</td>\n",
              "      <td>6.033300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27500</td>\n",
              "      <td>6.040600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28000</td>\n",
              "      <td>6.003100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28500</td>\n",
              "      <td>6.034200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29000</td>\n",
              "      <td>5.973500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29500</td>\n",
              "      <td>6.015600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30000</td>\n",
              "      <td>5.943300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30500</td>\n",
              "      <td>5.934300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31000</td>\n",
              "      <td>5.907900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31500</td>\n",
              "      <td>5.824900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32000</td>\n",
              "      <td>5.907300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32500</td>\n",
              "      <td>5.880500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33000</td>\n",
              "      <td>5.813800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33500</td>\n",
              "      <td>5.847500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34000</td>\n",
              "      <td>5.818000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34500</td>\n",
              "      <td>5.784600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35000</td>\n",
              "      <td>5.806400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35500</td>\n",
              "      <td>5.798000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36000</td>\n",
              "      <td>5.785800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36500</td>\n",
              "      <td>5.785000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37000</td>\n",
              "      <td>5.709000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37500</td>\n",
              "      <td>5.777900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38000</td>\n",
              "      <td>5.762400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38500</td>\n",
              "      <td>5.730300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39000</td>\n",
              "      <td>5.685000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39500</td>\n",
              "      <td>5.735100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40000</td>\n",
              "      <td>5.697000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40500</td>\n",
              "      <td>5.719400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41000</td>\n",
              "      <td>5.696400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41500</td>\n",
              "      <td>5.687600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42000</td>\n",
              "      <td>5.645000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42500</td>\n",
              "      <td>5.673200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43000</td>\n",
              "      <td>5.616800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43500</td>\n",
              "      <td>5.623100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44000</td>\n",
              "      <td>5.620300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44500</td>\n",
              "      <td>5.601800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45000</td>\n",
              "      <td>5.618900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45500</td>\n",
              "      <td>5.599100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46000</td>\n",
              "      <td>5.612300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46500</td>\n",
              "      <td>5.577500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47000</td>\n",
              "      <td>5.549400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47500</td>\n",
              "      <td>5.591600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48000</td>\n",
              "      <td>5.543200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48500</td>\n",
              "      <td>5.556500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49000</td>\n",
              "      <td>5.569700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49500</td>\n",
              "      <td>5.513700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50000</td>\n",
              "      <td>5.528500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50500</td>\n",
              "      <td>5.493600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51000</td>\n",
              "      <td>5.452600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51500</td>\n",
              "      <td>5.524600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52000</td>\n",
              "      <td>5.488000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52500</td>\n",
              "      <td>5.495100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53000</td>\n",
              "      <td>5.457000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53500</td>\n",
              "      <td>5.494600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54000</td>\n",
              "      <td>5.511300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54500</td>\n",
              "      <td>5.478100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55000</td>\n",
              "      <td>5.495900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55500</td>\n",
              "      <td>5.451000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56000</td>\n",
              "      <td>5.467700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56500</td>\n",
              "      <td>5.411700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57000</td>\n",
              "      <td>5.480700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57500</td>\n",
              "      <td>5.434600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58000</td>\n",
              "      <td>5.414700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58500</td>\n",
              "      <td>5.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59000</td>\n",
              "      <td>5.360300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59500</td>\n",
              "      <td>5.340700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60000</td>\n",
              "      <td>5.321300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60500</td>\n",
              "      <td>5.379700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61000</td>\n",
              "      <td>5.322400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61500</td>\n",
              "      <td>5.329400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62000</td>\n",
              "      <td>5.346000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62500</td>\n",
              "      <td>5.327700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63000</td>\n",
              "      <td>5.311400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63500</td>\n",
              "      <td>5.316900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64000</td>\n",
              "      <td>5.266600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64500</td>\n",
              "      <td>5.301500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65000</td>\n",
              "      <td>5.328600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65500</td>\n",
              "      <td>5.331400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66000</td>\n",
              "      <td>5.337900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66500</td>\n",
              "      <td>5.287400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67000</td>\n",
              "      <td>5.320800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67500</td>\n",
              "      <td>5.290800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68000</td>\n",
              "      <td>5.303300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68500</td>\n",
              "      <td>5.272000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69000</td>\n",
              "      <td>5.259900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69500</td>\n",
              "      <td>5.282600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70000</td>\n",
              "      <td>5.293900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70500</td>\n",
              "      <td>5.235400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71000</td>\n",
              "      <td>5.267900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71500</td>\n",
              "      <td>5.271800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72000</td>\n",
              "      <td>5.270700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72500</td>\n",
              "      <td>5.230800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73000</td>\n",
              "      <td>5.252300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73500</td>\n",
              "      <td>5.243000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74000</td>\n",
              "      <td>5.228500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74500</td>\n",
              "      <td>5.246900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75000</td>\n",
              "      <td>5.234700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75500</td>\n",
              "      <td>5.268300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76000</td>\n",
              "      <td>5.197500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76500</td>\n",
              "      <td>5.202900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77000</td>\n",
              "      <td>5.232300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77500</td>\n",
              "      <td>5.194400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78000</td>\n",
              "      <td>5.247500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78500</td>\n",
              "      <td>5.268300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79000</td>\n",
              "      <td>5.205900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79500</td>\n",
              "      <td>5.174100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80000</td>\n",
              "      <td>5.221300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80500</td>\n",
              "      <td>5.191200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81000</td>\n",
              "      <td>5.170600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81500</td>\n",
              "      <td>5.166900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82000</td>\n",
              "      <td>5.215600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82500</td>\n",
              "      <td>5.158000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83000</td>\n",
              "      <td>5.196100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83500</td>\n",
              "      <td>5.171600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84000</td>\n",
              "      <td>5.180600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84500</td>\n",
              "      <td>5.134000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85000</td>\n",
              "      <td>5.169300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85500</td>\n",
              "      <td>5.173500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86000</td>\n",
              "      <td>5.135000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86500</td>\n",
              "      <td>5.128200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87000</td>\n",
              "      <td>5.173800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87500</td>\n",
              "      <td>5.137600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88000</td>\n",
              "      <td>5.114200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88500</td>\n",
              "      <td>5.143400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89000</td>\n",
              "      <td>5.066500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89500</td>\n",
              "      <td>5.086700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90000</td>\n",
              "      <td>5.056800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90500</td>\n",
              "      <td>5.054300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91000</td>\n",
              "      <td>5.098400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91500</td>\n",
              "      <td>5.059700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92000</td>\n",
              "      <td>5.039400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92500</td>\n",
              "      <td>5.060600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93000</td>\n",
              "      <td>5.049500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93500</td>\n",
              "      <td>5.041300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94000</td>\n",
              "      <td>5.061700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94500</td>\n",
              "      <td>5.079500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95000</td>\n",
              "      <td>5.067600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95500</td>\n",
              "      <td>5.055500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96000</td>\n",
              "      <td>5.041100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96500</td>\n",
              "      <td>5.047300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97000</td>\n",
              "      <td>5.079700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97500</td>\n",
              "      <td>5.027700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98000</td>\n",
              "      <td>5.014500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98500</td>\n",
              "      <td>5.034000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99000</td>\n",
              "      <td>5.051000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99500</td>\n",
              "      <td>5.047600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100000</td>\n",
              "      <td>4.994500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100500</td>\n",
              "      <td>5.030600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101000</td>\n",
              "      <td>5.068500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101500</td>\n",
              "      <td>5.076900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102000</td>\n",
              "      <td>5.016100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102500</td>\n",
              "      <td>5.017100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103000</td>\n",
              "      <td>5.016500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103500</td>\n",
              "      <td>5.030100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104000</td>\n",
              "      <td>5.003600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104500</td>\n",
              "      <td>5.039400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105000</td>\n",
              "      <td>5.014500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105500</td>\n",
              "      <td>5.007500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106000</td>\n",
              "      <td>4.991500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106500</td>\n",
              "      <td>4.986700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107000</td>\n",
              "      <td>5.036400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107500</td>\n",
              "      <td>4.996500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108000</td>\n",
              "      <td>5.010500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108500</td>\n",
              "      <td>5.017500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109000</td>\n",
              "      <td>4.992100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109500</td>\n",
              "      <td>5.004200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110000</td>\n",
              "      <td>4.999700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110500</td>\n",
              "      <td>4.997500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>111000</td>\n",
              "      <td>5.008800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>111500</td>\n",
              "      <td>4.922600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>112000</td>\n",
              "      <td>4.942200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>112500</td>\n",
              "      <td>5.015500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>113000</td>\n",
              "      <td>4.954900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>113500</td>\n",
              "      <td>4.946800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>114000</td>\n",
              "      <td>4.955400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>114500</td>\n",
              "      <td>5.003600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>115000</td>\n",
              "      <td>4.974400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>115500</td>\n",
              "      <td>4.971200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>116000</td>\n",
              "      <td>4.976300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>116500</td>\n",
              "      <td>4.977200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>117000</td>\n",
              "      <td>4.968100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>117500</td>\n",
              "      <td>4.981900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>118000</td>\n",
              "      <td>4.956300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>118500</td>\n",
              "      <td>4.872500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>119000</td>\n",
              "      <td>4.877800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>119500</td>\n",
              "      <td>4.881800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120000</td>\n",
              "      <td>4.911600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120500</td>\n",
              "      <td>4.881500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>121000</td>\n",
              "      <td>4.930800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>121500</td>\n",
              "      <td>4.898700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>122000</td>\n",
              "      <td>4.923700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>122500</td>\n",
              "      <td>4.907800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123000</td>\n",
              "      <td>4.877300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123500</td>\n",
              "      <td>4.882000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>124000</td>\n",
              "      <td>4.914600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>124500</td>\n",
              "      <td>4.890300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125000</td>\n",
              "      <td>4.890500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125500</td>\n",
              "      <td>4.889000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>126000</td>\n",
              "      <td>4.876000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>126500</td>\n",
              "      <td>4.880700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>127000</td>\n",
              "      <td>4.879800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>127500</td>\n",
              "      <td>4.861700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>128000</td>\n",
              "      <td>4.862800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>128500</td>\n",
              "      <td>4.895200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>129000</td>\n",
              "      <td>4.869100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>129500</td>\n",
              "      <td>4.875200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130000</td>\n",
              "      <td>4.867400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130500</td>\n",
              "      <td>4.841300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>131000</td>\n",
              "      <td>4.914400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>131500</td>\n",
              "      <td>4.882500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>132000</td>\n",
              "      <td>4.884800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>132500</td>\n",
              "      <td>4.849200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>133000</td>\n",
              "      <td>4.876300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>133500</td>\n",
              "      <td>4.857100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>134000</td>\n",
              "      <td>4.857300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>134500</td>\n",
              "      <td>4.864600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>135000</td>\n",
              "      <td>4.848700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>135500</td>\n",
              "      <td>4.867900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>136000</td>\n",
              "      <td>4.851600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>136500</td>\n",
              "      <td>4.804000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>137000</td>\n",
              "      <td>4.876500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>137500</td>\n",
              "      <td>4.867100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>138000</td>\n",
              "      <td>4.825400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>138500</td>\n",
              "      <td>4.844200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>139000</td>\n",
              "      <td>4.828900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>139500</td>\n",
              "      <td>4.843200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140000</td>\n",
              "      <td>4.843100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140500</td>\n",
              "      <td>4.829000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>141000</td>\n",
              "      <td>4.868900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>141500</td>\n",
              "      <td>4.836400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>142000</td>\n",
              "      <td>4.846000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>142500</td>\n",
              "      <td>4.834700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>143000</td>\n",
              "      <td>4.874800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>143500</td>\n",
              "      <td>4.823500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>144000</td>\n",
              "      <td>4.847900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>144500</td>\n",
              "      <td>4.832500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>145000</td>\n",
              "      <td>4.810700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>145500</td>\n",
              "      <td>4.810900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>146000</td>\n",
              "      <td>4.843700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>146500</td>\n",
              "      <td>4.818500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>147000</td>\n",
              "      <td>4.808300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>147500</td>\n",
              "      <td>4.816700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>148000</td>\n",
              "      <td>4.745700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>148500</td>\n",
              "      <td>4.793300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>149000</td>\n",
              "      <td>4.763800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>149500</td>\n",
              "      <td>4.773100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150000</td>\n",
              "      <td>4.737500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150500</td>\n",
              "      <td>4.752800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>151000</td>\n",
              "      <td>4.757300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>151500</td>\n",
              "      <td>4.744600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>152000</td>\n",
              "      <td>4.727600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>152500</td>\n",
              "      <td>4.754300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>153000</td>\n",
              "      <td>4.738100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>153500</td>\n",
              "      <td>4.774000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>154000</td>\n",
              "      <td>4.809700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>154500</td>\n",
              "      <td>4.798200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>155000</td>\n",
              "      <td>4.752700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>155500</td>\n",
              "      <td>4.742100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>156000</td>\n",
              "      <td>4.711700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>156500</td>\n",
              "      <td>4.797100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>157000</td>\n",
              "      <td>4.771900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>157500</td>\n",
              "      <td>4.741600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>158000</td>\n",
              "      <td>4.718200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>158500</td>\n",
              "      <td>4.757400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>159000</td>\n",
              "      <td>4.755700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>159500</td>\n",
              "      <td>4.763800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160000</td>\n",
              "      <td>4.789600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160500</td>\n",
              "      <td>4.721700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>161000</td>\n",
              "      <td>4.787300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>161500</td>\n",
              "      <td>4.783400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>162000</td>\n",
              "      <td>4.740100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>162500</td>\n",
              "      <td>4.692500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>163000</td>\n",
              "      <td>4.713300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>163500</td>\n",
              "      <td>4.755600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164000</td>\n",
              "      <td>4.737200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164500</td>\n",
              "      <td>4.731000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>165000</td>\n",
              "      <td>4.731800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>165500</td>\n",
              "      <td>4.773500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>166000</td>\n",
              "      <td>4.727900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>166500</td>\n",
              "      <td>4.698700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>167000</td>\n",
              "      <td>4.735200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>167500</td>\n",
              "      <td>4.753500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>168000</td>\n",
              "      <td>4.738900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>168500</td>\n",
              "      <td>4.688100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>169000</td>\n",
              "      <td>4.721700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>169500</td>\n",
              "      <td>4.725100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170000</td>\n",
              "      <td>4.753900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170500</td>\n",
              "      <td>4.690600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>171000</td>\n",
              "      <td>4.694200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>171500</td>\n",
              "      <td>4.752300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>172000</td>\n",
              "      <td>4.711600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>172500</td>\n",
              "      <td>4.736700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>173000</td>\n",
              "      <td>4.723700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>173500</td>\n",
              "      <td>4.724900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>174000</td>\n",
              "      <td>4.763300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>174500</td>\n",
              "      <td>4.693100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175000</td>\n",
              "      <td>4.714400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175500</td>\n",
              "      <td>4.725600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>176000</td>\n",
              "      <td>4.686000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>176500</td>\n",
              "      <td>4.736900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>177000</td>\n",
              "      <td>4.744400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>177500</td>\n",
              "      <td>4.667500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>178000</td>\n",
              "      <td>4.660300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>178500</td>\n",
              "      <td>4.646200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>179000</td>\n",
              "      <td>4.669500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>179500</td>\n",
              "      <td>4.693900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180000</td>\n",
              "      <td>4.724900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180500</td>\n",
              "      <td>4.675600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>181000</td>\n",
              "      <td>4.697200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>181500</td>\n",
              "      <td>4.681000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>182000</td>\n",
              "      <td>4.651400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>182500</td>\n",
              "      <td>4.662000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>183000</td>\n",
              "      <td>4.634700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>183500</td>\n",
              "      <td>4.672500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>184000</td>\n",
              "      <td>4.660200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>184500</td>\n",
              "      <td>4.678800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>185000</td>\n",
              "      <td>4.667100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>185500</td>\n",
              "      <td>4.628200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>186000</td>\n",
              "      <td>4.653200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>186500</td>\n",
              "      <td>4.645200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>187000</td>\n",
              "      <td>4.660400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>187500</td>\n",
              "      <td>4.569700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>188000</td>\n",
              "      <td>4.659700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>188500</td>\n",
              "      <td>4.677500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>189000</td>\n",
              "      <td>4.639600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>189500</td>\n",
              "      <td>4.679800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190000</td>\n",
              "      <td>4.656900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190500</td>\n",
              "      <td>4.681300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>191000</td>\n",
              "      <td>4.633100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>191500</td>\n",
              "      <td>4.639200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>192000</td>\n",
              "      <td>4.649300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>192500</td>\n",
              "      <td>4.659500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>193000</td>\n",
              "      <td>4.650600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>193500</td>\n",
              "      <td>4.610400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>194000</td>\n",
              "      <td>4.601700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>194500</td>\n",
              "      <td>4.612700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>195000</td>\n",
              "      <td>4.613800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>195500</td>\n",
              "      <td>4.656700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>196000</td>\n",
              "      <td>4.669800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>196500</td>\n",
              "      <td>4.634600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>197000</td>\n",
              "      <td>4.647400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>197500</td>\n",
              "      <td>4.624000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>198000</td>\n",
              "      <td>4.626900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>198500</td>\n",
              "      <td>4.656600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>199000</td>\n",
              "      <td>4.638900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>199500</td>\n",
              "      <td>4.616900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200000</td>\n",
              "      <td>4.616400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200500</td>\n",
              "      <td>4.660800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>201000</td>\n",
              "      <td>4.599400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>201500</td>\n",
              "      <td>4.646400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>202000</td>\n",
              "      <td>4.599800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>202500</td>\n",
              "      <td>4.623700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>203000</td>\n",
              "      <td>4.642300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>203500</td>\n",
              "      <td>4.630400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>204000</td>\n",
              "      <td>4.652500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>204500</td>\n",
              "      <td>4.582400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>205000</td>\n",
              "      <td>4.634000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>205500</td>\n",
              "      <td>4.619700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>206000</td>\n",
              "      <td>4.637800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>206500</td>\n",
              "      <td>4.603200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>207000</td>\n",
              "      <td>4.585100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>207500</td>\n",
              "      <td>4.595900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>208000</td>\n",
              "      <td>4.572700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>208500</td>\n",
              "      <td>4.638800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>209000</td>\n",
              "      <td>4.565900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>209500</td>\n",
              "      <td>4.587400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210000</td>\n",
              "      <td>4.598900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210500</td>\n",
              "      <td>4.588500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>211000</td>\n",
              "      <td>4.584700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>211500</td>\n",
              "      <td>4.580800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>212000</td>\n",
              "      <td>4.553700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>212500</td>\n",
              "      <td>4.570800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>213000</td>\n",
              "      <td>4.560600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>213500</td>\n",
              "      <td>4.568500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>214000</td>\n",
              "      <td>4.568800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>214500</td>\n",
              "      <td>4.561100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>215000</td>\n",
              "      <td>4.569400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>215500</td>\n",
              "      <td>4.593400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>216000</td>\n",
              "      <td>4.545000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>216500</td>\n",
              "      <td>4.563400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>217000</td>\n",
              "      <td>4.581500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>217500</td>\n",
              "      <td>4.547600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>218000</td>\n",
              "      <td>4.570800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>218500</td>\n",
              "      <td>4.551600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>219000</td>\n",
              "      <td>4.578100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>219500</td>\n",
              "      <td>4.582500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220000</td>\n",
              "      <td>4.576800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220500</td>\n",
              "      <td>4.551100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>221000</td>\n",
              "      <td>4.545500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>221500</td>\n",
              "      <td>4.553900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>222000</td>\n",
              "      <td>4.567400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>222500</td>\n",
              "      <td>4.541200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>223000</td>\n",
              "      <td>4.549800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>223500</td>\n",
              "      <td>4.562400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>224000</td>\n",
              "      <td>4.588800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>224500</td>\n",
              "      <td>4.526700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225000</td>\n",
              "      <td>4.580700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225500</td>\n",
              "      <td>4.560900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>226000</td>\n",
              "      <td>4.568600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>226500</td>\n",
              "      <td>4.565900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>227000</td>\n",
              "      <td>4.559100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>227500</td>\n",
              "      <td>4.580400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>228000</td>\n",
              "      <td>4.564300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>228500</td>\n",
              "      <td>4.575700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>229000</td>\n",
              "      <td>4.533000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>229500</td>\n",
              "      <td>4.535800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230000</td>\n",
              "      <td>4.554300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230500</td>\n",
              "      <td>4.569800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>231000</td>\n",
              "      <td>4.532400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>231500</td>\n",
              "      <td>4.549600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>232000</td>\n",
              "      <td>4.546800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>232500</td>\n",
              "      <td>4.561900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>233000</td>\n",
              "      <td>4.548500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>233500</td>\n",
              "      <td>4.554300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>234000</td>\n",
              "      <td>4.536300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>234500</td>\n",
              "      <td>4.542600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>235000</td>\n",
              "      <td>4.546000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>235500</td>\n",
              "      <td>4.539600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>236000</td>\n",
              "      <td>4.526700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>236500</td>\n",
              "      <td>4.498000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>237000</td>\n",
              "      <td>4.540200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>237500</td>\n",
              "      <td>4.482900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>238000</td>\n",
              "      <td>4.493800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>238500</td>\n",
              "      <td>4.512500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>239000</td>\n",
              "      <td>4.512800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>239500</td>\n",
              "      <td>4.497100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240000</td>\n",
              "      <td>4.564400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240500</td>\n",
              "      <td>4.537900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>241000</td>\n",
              "      <td>4.471100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>241500</td>\n",
              "      <td>4.476600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>242000</td>\n",
              "      <td>4.528300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>242500</td>\n",
              "      <td>4.539200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>243000</td>\n",
              "      <td>4.470700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>243500</td>\n",
              "      <td>4.511200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>244000</td>\n",
              "      <td>4.488800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>244500</td>\n",
              "      <td>4.472700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>245000</td>\n",
              "      <td>4.491600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>245500</td>\n",
              "      <td>4.501200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>246000</td>\n",
              "      <td>4.505300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>246500</td>\n",
              "      <td>4.460600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>247000</td>\n",
              "      <td>4.457500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>247500</td>\n",
              "      <td>4.540800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>248000</td>\n",
              "      <td>4.492500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>248500</td>\n",
              "      <td>4.503100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>249000</td>\n",
              "      <td>4.499200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>249500</td>\n",
              "      <td>4.468500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250000</td>\n",
              "      <td>4.481100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250500</td>\n",
              "      <td>4.464700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>251000</td>\n",
              "      <td>4.524000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>251500</td>\n",
              "      <td>4.516100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>252000</td>\n",
              "      <td>4.555200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>252500</td>\n",
              "      <td>4.490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>253000</td>\n",
              "      <td>4.489500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>253500</td>\n",
              "      <td>4.469400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>254000</td>\n",
              "      <td>4.480600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>254500</td>\n",
              "      <td>4.486300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>255000</td>\n",
              "      <td>4.484400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>255500</td>\n",
              "      <td>4.484500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>256000</td>\n",
              "      <td>4.499200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>256500</td>\n",
              "      <td>4.503900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>257000</td>\n",
              "      <td>4.520500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>257500</td>\n",
              "      <td>4.529000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>258000</td>\n",
              "      <td>4.496900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>258500</td>\n",
              "      <td>4.485700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>259000</td>\n",
              "      <td>4.509100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>259500</td>\n",
              "      <td>4.485100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260000</td>\n",
              "      <td>4.511200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260500</td>\n",
              "      <td>4.490400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>261000</td>\n",
              "      <td>4.494600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>261500</td>\n",
              "      <td>4.482600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>262000</td>\n",
              "      <td>4.523800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>262500</td>\n",
              "      <td>4.495300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>263000</td>\n",
              "      <td>4.477000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>263500</td>\n",
              "      <td>4.446700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>264000</td>\n",
              "      <td>4.441300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>264500</td>\n",
              "      <td>4.496600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>265000</td>\n",
              "      <td>4.479300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>265500</td>\n",
              "      <td>4.481400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>266000</td>\n",
              "      <td>4.489200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>266500</td>\n",
              "      <td>4.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>267000</td>\n",
              "      <td>4.446900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>267500</td>\n",
              "      <td>4.435900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>268000</td>\n",
              "      <td>4.436800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>268500</td>\n",
              "      <td>4.488800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>269000</td>\n",
              "      <td>4.469400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>269500</td>\n",
              "      <td>4.457000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270000</td>\n",
              "      <td>4.464100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270500</td>\n",
              "      <td>4.454600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>271000</td>\n",
              "      <td>4.433700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>271500</td>\n",
              "      <td>4.451000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>272000</td>\n",
              "      <td>4.458000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>272500</td>\n",
              "      <td>4.473800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>273000</td>\n",
              "      <td>4.439200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>273500</td>\n",
              "      <td>4.492700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>274000</td>\n",
              "      <td>4.460400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>274500</td>\n",
              "      <td>4.445300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275000</td>\n",
              "      <td>4.473600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275500</td>\n",
              "      <td>4.447300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>276000</td>\n",
              "      <td>4.441700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>276500</td>\n",
              "      <td>4.443600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>277000</td>\n",
              "      <td>4.418400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>277500</td>\n",
              "      <td>4.455600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>278000</td>\n",
              "      <td>4.449000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>278500</td>\n",
              "      <td>4.480800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>279000</td>\n",
              "      <td>4.445800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>279500</td>\n",
              "      <td>4.448400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280000</td>\n",
              "      <td>4.477800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280500</td>\n",
              "      <td>4.436000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>281000</td>\n",
              "      <td>4.487800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>281500</td>\n",
              "      <td>4.468900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>282000</td>\n",
              "      <td>4.453000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>282500</td>\n",
              "      <td>4.467500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>283000</td>\n",
              "      <td>4.502500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>283500</td>\n",
              "      <td>4.455900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>284000</td>\n",
              "      <td>4.429700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>284500</td>\n",
              "      <td>4.444600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>285000</td>\n",
              "      <td>4.452100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>285500</td>\n",
              "      <td>4.385500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>286000</td>\n",
              "      <td>4.446400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>286500</td>\n",
              "      <td>4.467100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>287000</td>\n",
              "      <td>4.480800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>287500</td>\n",
              "      <td>4.431000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>288000</td>\n",
              "      <td>4.477000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>288500</td>\n",
              "      <td>4.434700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>289000</td>\n",
              "      <td>4.444200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>289500</td>\n",
              "      <td>4.438100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>290000</td>\n",
              "      <td>4.442000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>290500</td>\n",
              "      <td>4.487400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>291000</td>\n",
              "      <td>4.426100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>291500</td>\n",
              "      <td>4.439500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>292000</td>\n",
              "      <td>4.458100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>292500</td>\n",
              "      <td>4.436800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>293000</td>\n",
              "      <td>4.460900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>293500</td>\n",
              "      <td>4.440800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>294000</td>\n",
              "      <td>4.441400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>294500</td>\n",
              "      <td>4.473100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to ./output_dir/checkpoint-10000\n",
            "Configuration saved in ./output_dir/checkpoint-10000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-10000/pytorch_model.bin\n",
            "Saving model checkpoint to ./output_dir/checkpoint-20000\n",
            "Configuration saved in ./output_dir/checkpoint-20000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-20000/pytorch_model.bin\n",
            "Saving model checkpoint to ./output_dir/checkpoint-30000\n",
            "Configuration saved in ./output_dir/checkpoint-30000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-30000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-10000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-40000\n",
            "Configuration saved in ./output_dir/checkpoint-40000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-40000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-20000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-50000\n",
            "Configuration saved in ./output_dir/checkpoint-50000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-50000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-30000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-60000\n",
            "Configuration saved in ./output_dir/checkpoint-60000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-60000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-40000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-70000\n",
            "Configuration saved in ./output_dir/checkpoint-70000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-70000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-50000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-80000\n",
            "Configuration saved in ./output_dir/checkpoint-80000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-80000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-60000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-90000\n",
            "Configuration saved in ./output_dir/checkpoint-90000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-90000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-70000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-100000\n",
            "Configuration saved in ./output_dir/checkpoint-100000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-100000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-80000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-110000\n",
            "Configuration saved in ./output_dir/checkpoint-110000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-110000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-90000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-120000\n",
            "Configuration saved in ./output_dir/checkpoint-120000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-120000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-100000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-130000\n",
            "Configuration saved in ./output_dir/checkpoint-130000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-130000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-110000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-140000\n",
            "Configuration saved in ./output_dir/checkpoint-140000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-140000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-120000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-150000\n",
            "Configuration saved in ./output_dir/checkpoint-150000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-150000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-130000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-160000\n",
            "Configuration saved in ./output_dir/checkpoint-160000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-160000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-140000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-170000\n",
            "Configuration saved in ./output_dir/checkpoint-170000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-170000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-150000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-180000\n",
            "Configuration saved in ./output_dir/checkpoint-180000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-180000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-160000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-190000\n",
            "Configuration saved in ./output_dir/checkpoint-190000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-190000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-170000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-200000\n",
            "Configuration saved in ./output_dir/checkpoint-200000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-200000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-180000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-210000\n",
            "Configuration saved in ./output_dir/checkpoint-210000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-210000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-190000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-220000\n",
            "Configuration saved in ./output_dir/checkpoint-220000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-220000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-200000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-230000\n",
            "Configuration saved in ./output_dir/checkpoint-230000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-230000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-210000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-240000\n",
            "Configuration saved in ./output_dir/checkpoint-240000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-240000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-220000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-250000\n",
            "Configuration saved in ./output_dir/checkpoint-250000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-250000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-230000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-260000\n",
            "Configuration saved in ./output_dir/checkpoint-260000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-260000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-240000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-270000\n",
            "Configuration saved in ./output_dir/checkpoint-270000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-270000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-250000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-280000\n",
            "Configuration saved in ./output_dir/checkpoint-280000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-280000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-260000] due to args.save_total_limit\n",
            "Saving model checkpoint to ./output_dir/checkpoint-290000\n",
            "Configuration saved in ./output_dir/checkpoint-290000/config.json\n",
            "Model weights saved in ./output_dir/checkpoint-290000/pytorch_model.bin\n",
            "Deleting older checkpoint [output_dir/checkpoint-270000] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Saving model checkpoint to ./model/\n",
            "Configuration saved in ./model/config.json\n",
            "Model weights saved in ./model/pytorch_model.bin\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 8h 24min 31s, sys: 2min 55s, total: 8h 27min 26s\n",
            "Wall time: 8h 24min 2s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "trainer.train()\n",
        "trainer.save_model('./model/')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "fekfXQluLuIZ"
      },
      "source": [
        "--------------------------------------\n",
        "--------------------------------------\n",
        "--------------------------------------\n",
        "--------------------------------------"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "1WgvzAfkz-tc"
      },
      "source": [
        "# **Finetuning**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LaVl6HzuL7wv"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "EZ4Ms2Soi4WE"
      },
      "source": [
        "## Load dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xMmUMh8fi7c8",
        "outputId": "44d4bc0d-f5e2-4a40-a4ba-a8f5730302d2"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Journey</th>\n",
              "      <th>Journey#refnumber</th>\n",
              "      <th>comment</th>\n",
              "      <th>xclean</th>\n",
              "      <th>rating</th>\n",
              "      <th>classes</th>\n",
              "      <th>Examination</th>\n",
              "      <th>Patient_Journey</th>\n",
              "      <th>Quality_Care</th>\n",
              "      <th>...</th>\n",
              "      <th>Access</th>\n",
              "      <th>Patient_Disposition</th>\n",
              "      <th>Delays</th>\n",
              "      <th>Referrals</th>\n",
              "      <th>Patient-Staff_Communication</th>\n",
              "      <th>Incorrect_Information</th>\n",
              "      <th>Emotional_Support</th>\n",
              "      <th>Assault_Harassment</th>\n",
              "      <th>Confidentiality</th>\n",
              "      <th>Consent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>PCU</td>\n",
              "      <td>PCU000001</td>\n",
              "      <td>الدكتور لا يتكلم مع المريض ولا يلبس القفازات و...</td>\n",
              "      <td>الدكتور يتكلم المريض يلبس القفازات يدلي باي مع...</td>\n",
              "      <td>Negative</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>PCU</td>\n",
              "      <td>PCU000002</td>\n",
              "      <td>الشي الوحيد الذي تضيقت منه في زياره سابقه وليس...</td>\n",
              "      <td>الشي الوحيد تضيقت زياره سابقه وليست اخر زياره ...</td>\n",
              "      <td>Negative</td>\n",
              "      <td>غير مهتم باحتياجات المريض- انتهاك خصوصية المري...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 32 columns</p>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fb52a9a7-9017-4919-8928-c300f013d2ff button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fb52a9a7-9017-4919-8928-c300f013d2ff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Unnamed: 0 Journey Journey#refnumber  \\\n",
              "0           0     PCU         PCU000001   \n",
              "1           1     PCU         PCU000002   \n",
              "\n",
              "                                             comment  \\\n",
              "0  الدكتور لا يتكلم مع المريض ولا يلبس القفازات و...   \n",
              "1  الشي الوحيد الذي تضيقت منه في زياره سابقه وليس...   \n",
              "\n",
              "                                              xclean    rating  \\\n",
              "0  الدكتور يتكلم المريض يلبس القفازات يدلي باي مع...  Negative   \n",
              "1  الشي الوحيد تضيقت زياره سابقه وليست اخر زياره ...  Negative   \n",
              "\n",
              "                                             classes  Examination  \\\n",
              "0    غير مهتم باحتياجات المريض -رعاية طبية دون ال...            0   \n",
              "1  غير مهتم باحتياجات المريض- انتهاك خصوصية المري...            0   \n",
              "\n",
              "   Patient_Journey  Quality_Care  ...  Access  Patient_Disposition  Delays  \\\n",
              "0                0             1  ...       1                    0       0   \n",
              "1                0             1  ...       0                    0       0   \n",
              "\n",
              "   Referrals  Patient-Staff_Communication  Incorrect_Information  \\\n",
              "0          0                            1                      0   \n",
              "1          0                            0                      0   \n",
              "\n",
              "   Emotional_Support  Assault_Harassment  Confidentiality  Consent  \n",
              "0                  0                   0                0        0  \n",
              "1                  0                   0                1        0  \n",
              "\n",
              "[2 rows x 32 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "px_df1 = pd.read_csv(path+'all_subcategories_xcleaned.csv')\n",
        "\n",
        "px_df1.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dF8me6brymoP",
        "outputId": "f937fa3e-4de6-4b19-813c-69625ee2c6e0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "shape before dropping possible NA rows:  (14426, 32)\n",
            "shape after dropping possible NA rows:  (14423, 26)\n"
          ]
        }
      ],
      "source": [
        "print('shape before dropping possible NA rows: ' ,px_df1.shape)\n",
        "px_df1.drop(inplace=True,columns=['Journey','Journey#refnumber','comment','rating','classes','Unnamed: 0'] ,)\n",
        "px_df1.dropna(inplace=True, how='any')\n",
        "print('shape after dropping possible NA rows: ' ,px_df1.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dIZIvjUChlH",
        "outputId": "bcb28c20-73fd-4019-f8f5-0aa8eee4e531"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>xclean</th>\n",
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              "      <th>Quality_Care</th>\n",
              "      <th>Treatment</th>\n",
              "      <th>Diagnosis</th>\n",
              "      <th>Medication_Vaccination</th>\n",
              "      <th>Safety_Inceidents</th>\n",
              "      <th>Skills_conducts</th>\n",
              "      <th>Administrative_Policies_procedures</th>\n",
              "      <th>...</th>\n",
              "      <th>Access</th>\n",
              "      <th>Patient_Disposition</th>\n",
              "      <th>Delays</th>\n",
              "      <th>Referrals</th>\n",
              "      <th>Patient-Staff_Communication</th>\n",
              "      <th>Incorrect_Information</th>\n",
              "      <th>Emotional_Support</th>\n",
              "      <th>Assault_Harassment</th>\n",
              "      <th>Confidentiality</th>\n",
              "      <th>Consent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>الدكتور يتكلم المريض يلبس القفازات يدلي باي مع...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 26 columns</p>\n",
              "</div>\n",
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              "  </svg>\n",
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              "      \n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-d61154b2-d1d5-44f2-aa1e-2336bc0f4071 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d61154b2-d1d5-44f2-aa1e-2336bc0f4071');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                              xclean  Examination  \\\n",
              "0  الدكتور يتكلم المريض يلبس القفازات يدلي باي مع...            0   \n",
              "\n",
              "   Patient_Journey  Quality_Care  Treatment  Diagnosis  \\\n",
              "0                0             1          0          0   \n",
              "\n",
              "   Medication_Vaccination  Safety_Inceidents  Skills_conducts  \\\n",
              "0                       0                  0                0   \n",
              "\n",
              "   Administrative_Policies_procedures  ...  Access  Patient_Disposition  \\\n",
              "0                                   0  ...       1                    0   \n",
              "\n",
              "   Delays  Referrals  Patient-Staff_Communication  Incorrect_Information  \\\n",
              "0       0          0                            1                      0   \n",
              "\n",
              "   Emotional_Support  Assault_Harassment  Confidentiality  Consent  \n",
              "0                  0                   0                0        0  \n",
              "\n",
              "[1 rows x 26 columns]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "px_df1.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hFCBt2SRymto"
      },
      "outputs": [],
      "source": [
        "fifth = int(px_df1.shape[0]/5) * 4 # first 4 fifths for training, the last for testing\n",
        "px_df1 = px_df1[px_df1['xclean'].notna()]\n",
        "#px_df1['xclean'].replace('', np.nan, inplace=True)\n",
        "#px_df2 = px_df1.dropna(subset=['xclean'], inplace=True)\n",
        "#px_df2.shape\n",
        "\n",
        "px_df1[:fifth].to_csv('training.csv')\n",
        "px_df1[fifth:].to_csv('testing.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EaDv_3HpymwK",
        "outputId": "71ef9f24-c662-48e9-b63f-b12ff2ccee6a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['Examination', 'Patient_Journey', 'Quality_Care', 'Treatment',\n",
            "       'Diagnosis', 'Medication_Vaccination', 'Safety_Inceidents',\n",
            "       'Skills_conducts', 'Administrative_Policies_procedures', 'Environment',\n",
            "       'Safety_Security', 'Finance_Billing', 'Staffing', 'Resources',\n",
            "       'Medical_Records', 'Access', 'Patient_Disposition', 'Delays',\n",
            "       'Referrals', 'Patient-Staff_Communication', 'Incorrect_Information',\n",
            "       'Emotional_Support', 'Assault_Harassment', 'Confidentiality',\n",
            "       'Consent'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "cols = px_df1.columns[1:]\n",
        "print(cols)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Jb-HLIHM8voe"
      },
      "source": [
        "## set up the finetuning process"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "SbmCY6xcncpc"
      },
      "source": [
        "### prepare the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KqXnwqiTu_0I"
      },
      "outputs": [],
      "source": [
        "! pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n6VmIuSp6Pez"
      },
      "outputs": [],
      "source": [
        "#model_name = '/content/drive/My Drive/Colab/thesis_2/language_models/PXBert_test_unannotated_33K'\n",
        "model_name = path + 'arnab_10e/model'\n",
        "tokenizer_name = path + 'arnab_10e/tokenizer'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187,
          "referenced_widgets": [
            "9f8f000ab3e74ba1913ed759c1ef5c27",
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            "5919ab15b00444a08a7273d74fe5e56b",
            "d2bf5971ea1a4293b926d5a42a491941",
            "4319139e360b4701b49ed21ee6f9f367",
            "d4a0d6298a4a4d9c91f5df028de8e94d",
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            "200d7fabe1aa459381e3ee15f3e12eeb",
            "58af56baf9654631a3fcb5d38278ccb8",
            "4904408caaa24b3089c360d15ae053fd",
            "90918c6dea4f45889e90087323d2cdd0",
            "136c3bf7660b42deb9d75160b64b86c1",
            "e281d837d2834daa9e910dcb80f9a302",
            "1383fbbeb8b44f29adba8a54742f48e4",
            "e497555a2b3d4a08a022c1ab8163d580",
            "85657186eed546b2a652fac8995a1e85",
            "2d4bbf0f7453461281a52aa6be757d73",
            "97cd5041e7d6481ab67614cbbb3264c1"
          ]
        },
        "id": "1OEyfKBk6PhX",
        "outputId": "0c5e6d0c-8058-4b48-d079-ff512dfe769d"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset('csv' , data_files={'train': 'training.csv', 'test': 'testing.csv'} )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rHa8yTl3no4M",
        "outputId": "1e4e9345-a142-4518-e790-20e7e090af73"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'xclean', 'Examination', 'Patient_Journey', 'Quality_Care', 'Treatment', 'Diagnosis', 'Medication_Vaccination', 'Safety_Inceidents', 'Skills_conducts', 'Administrative_Policies_procedures', 'Environment', 'Safety_Security', 'Finance_Billing', 'Staffing', 'Resources', 'Medical_Records', 'Access', 'Patient_Disposition', 'Delays', 'Referrals', 'Patient-Staff_Communication', 'Incorrect_Information', 'Emotional_Support', 'Assault_Harassment', 'Confidentiality', 'Consent'],\n",
              "        num_rows: 11536\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Unnamed: 0', 'xclean', 'Examination', 'Patient_Journey', 'Quality_Care', 'Treatment', 'Diagnosis', 'Medication_Vaccination', 'Safety_Inceidents', 'Skills_conducts', 'Administrative_Policies_procedures', 'Environment', 'Safety_Security', 'Finance_Billing', 'Staffing', 'Resources', 'Medical_Records', 'Access', 'Patient_Disposition', 'Delays', 'Referrals', 'Patient-Staff_Communication', 'Incorrect_Information', 'Emotional_Support', 'Assault_Harassment', 'Confidentiality', 'Consent'],\n",
              "        num_rows: 2887\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGeyeB7hno65",
        "outputId": "8ee018ba-8a0a-4257-c05a-da2c6d608d5c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'Unnamed: 0': 0,\n",
              " 'xclean': 'الدكتور يتكلم المريض يلبس القفازات يدلي باي معلومات رغم وقت الانتظار الطويل يوجد فوضي وعدم ترتيب المواعيد نظافه سييه',\n",
              " 'Examination': 0,\n",
              " 'Patient_Journey': 0,\n",
              " 'Quality_Care': 1,\n",
              " 'Treatment': 0,\n",
              " 'Diagnosis': 0,\n",
              " 'Medication_Vaccination': 0,\n",
              " 'Safety_Inceidents': 0,\n",
              " 'Skills_conducts': 0,\n",
              " 'Administrative_Policies_procedures': 0,\n",
              " 'Environment': 1,\n",
              " 'Safety_Security': 0,\n",
              " 'Finance_Billing': 0,\n",
              " 'Staffing': 0,\n",
              " 'Resources': 0,\n",
              " 'Medical_Records': 0,\n",
              " 'Access': 1,\n",
              " 'Patient_Disposition': 0,\n",
              " 'Delays': 0,\n",
              " 'Referrals': 0,\n",
              " 'Patient-Staff_Communication': 1,\n",
              " 'Incorrect_Information': 0,\n",
              " 'Emotional_Support': 0,\n",
              " 'Assault_Harassment': 0,\n",
              " 'Confidentiality': 0,\n",
              " 'Consent': 0}"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['train'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ja-mVt0Sno9J",
        "outputId": "8c7a1903-24fd-4d71-9ed1-b466faa00d7e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['xclean', 'Examination', 'Patient_Journey', 'Quality_Care', 'Treatment', 'Diagnosis', 'Medication_Vaccination', 'Safety_Inceidents', 'Skills_conducts', 'Administrative_Policies_procedures', 'Environment', 'Safety_Security', 'Finance_Billing', 'Staffing', 'Resources', 'Medical_Records', 'Access', 'Patient_Disposition', 'Delays', 'Referrals', 'Patient-Staff_Communication', 'Incorrect_Information', 'Emotional_Support', 'Assault_Harassment', 'Confidentiality', 'Consent'],\n",
              "        num_rows: 11536\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['xclean', 'Examination', 'Patient_Journey', 'Quality_Care', 'Treatment', 'Diagnosis', 'Medication_Vaccination', 'Safety_Inceidents', 'Skills_conducts', 'Administrative_Policies_procedures', 'Environment', 'Safety_Security', 'Finance_Billing', 'Staffing', 'Resources', 'Medical_Records', 'Access', 'Patient_Disposition', 'Delays', 'Referrals', 'Patient-Staff_Communication', 'Incorrect_Information', 'Emotional_Support', 'Assault_Harassment', 'Confidentiality', 'Consent'],\n",
              "        num_rows: 2887\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.remove_columns('Unnamed: 0')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YDisjVgDno_j"
      },
      "outputs": [],
      "source": [
        "labels = [label for label in dataset['train'].features.keys() if label not in ['Journey#refnumber', 'cleaned_comment' , 'classes', 'Unnamed: 0','xclean']]\n",
        "id2label = {idx:label for idx, label in enumerate(labels)}\n",
        "label2id = {label:idx for idx, label in enumerate(labels)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0uVg-8LmnpCt",
        "outputId": "f3e26e4e-3e57-44f9-b092-cd909603fe6f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Examination',\n",
              " 'Patient_Journey',\n",
              " 'Quality_Care',\n",
              " 'Treatment',\n",
              " 'Diagnosis',\n",
              " 'Medication_Vaccination',\n",
              " 'Safety_Inceidents',\n",
              " 'Skills_conducts',\n",
              " 'Administrative_Policies_procedures',\n",
              " 'Environment',\n",
              " 'Safety_Security',\n",
              " 'Finance_Billing',\n",
              " 'Staffing',\n",
              " 'Resources',\n",
              " 'Medical_Records',\n",
              " 'Access',\n",
              " 'Patient_Disposition',\n",
              " 'Delays',\n",
              " 'Referrals',\n",
              " 'Patient-Staff_Communication',\n",
              " 'Incorrect_Information',\n",
              " 'Emotional_Support',\n",
              " 'Assault_Harassment',\n",
              " 'Confidentiality',\n",
              " 'Consent']"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVubebD8npE5",
        "outputId": "822a0d13-88d1-4096-8114-792a040ab94a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(labels)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "oR1wryrPn7z0"
      },
      "source": [
        "### tokenize the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VmR9spM5SD18"
      },
      "outputs": [],
      "source": [
        "#! pip install transformers --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "90miFGpgn8p2"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, BertTokenizer\n",
        "import numpy as np\n",
        "\n",
        "#tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)\n",
        "tokenizer = BertTokenizer.from_pretrained(tokenizer_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kRf2WkD3n9So"
      },
      "outputs": [],
      "source": [
        "\n",
        "def preprocess_data(examples):\n",
        "  \"\"\"try:\"\"\"\n",
        "  # take a batch of texts\n",
        "  text = examples[\"xclean\"]\n",
        "  #if text == None:\n",
        "      #text = '' # avoid the empty comments\n",
        "  # encode them\n",
        "  encoding = tokenizer(text, padding=\"max_length\", truncation=True, max_length=512) # EDIT -------------------------\n",
        "  # add labels\n",
        "  labels_batch = {k: examples[k] for k in examples.keys() if k in labels}\n",
        "  # create numpy array of shape (batch_size, num_labels)\n",
        "  labels_matrix = np.zeros((len(text), len(labels)))\n",
        "  # fill numpy array\n",
        "  for idx, label in enumerate(labels):\n",
        "      labels_matrix[:, idx] = labels_batch[label]\n",
        "\n",
        "  encoding[\"labels\"] = labels_matrix.tolist()\n",
        "  \"\"\"except:\n",
        "  print('Error occured for text: ' , text , ' at indix: ' , examples['Journey#refnumber'])\"\"\"\n",
        "  return encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119,
          "referenced_widgets": [
            "66164edabc134d27b2687507555ac8ff",
            "c963dd966f734afc86e6429410b842d7",
            "31c8415392a240f7a777d7b50429e39c",
            "c036e73e9bdd4e1daf03bfbd9639ede8",
            "a2e93a0c8a22435fb62378be64501fb7",
            "0dd9b884f16a4961a977b3fdbe37ce38",
            "2715fb63a64a457cb8a7aa40584aaa15",
            "6cc7bf95b75e40c8bfe0f4aebb74fef5",
            "2551e595c8794ca0a394469cb495b210",
            "bccee14862c246099c10ad8ced265559",
            "ba58376dfd24432ea16858a29772223d",
            "8473fa79bae6447095c48f1b9a91ea83",
            "2d880c2675ef416ab7e1b8641a680cb9",
            "2796282330a44ac99d6b9508b8bc4fa3",
            "95f1a18db856420c9a793143644a920d",
            "045c8b4e475d4622a36c056ac1f75167",
            "ad91657f2b304616bbd0b87e87449a91",
            "377e36e415af486fbb9b92757737f842",
            "5e026f783ede41c18b61e1f6a9d7d3a8",
            "4e81828068e04d6db24a7e79a974c876",
            "bbbb156bbde2427588bb75a7f5b7c94d",
            "84219dfa579c41fc973b46cf93ee75f1"
          ]
        },
        "id": "YBSlg5ysn9QB",
        "outputId": "898aa2c0-b97d-453a-9f0f-7b2bf568d54a"
      },
      "outputs": [],
      "source": [
        "encoded_dataset = dataset.map(preprocess_data, batched=True, remove_columns=dataset['train'].column_names)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4QZUEZj5n9Nt",
        "outputId": "67cc5373-d438-4768-8e9a-515e2a0b7ead"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 11536\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 2887\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "encoded_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_q5MrFjfn9LH"
      },
      "outputs": [],
      "source": [
        "#encoded_dataset[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKNDbJ0AoNHp",
        "outputId": "bfedb683-631e-4ab2-ee2c-fe12e503a2d8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'labels'])\n"
          ]
        }
      ],
      "source": [
        "example = encoded_dataset['train'][0]\n",
        "print(example.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "iLuCir9RoNFO",
        "outputId": "e3168ed3-46d3-4e6c-b7a3-5a049d4e8fde"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'[CLS] الدكتور يتكلم المريض يلبس القفازات يدلي باي معلومات رغم وقت الانتظار الطويل يوجد فوضي وعدم ترتيب المواعيد نظافه سييه [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] [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] [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] [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] [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] [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] [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] [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] [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]'"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.decode(example['input_ids'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HBbrVHWPoUiG",
        "outputId": "404dbd6f-2337-4962-ce37-9924489c6bde"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[0.0,\n",
              " 0.0,\n",
              " 1.0,\n",
              " 0.0,\n",
              " 0.0,\n",
              " 0.0,\n",
              " 0.0,\n",
              " 0.0,\n",
              " 0.0,\n",
              " 1.0,\n",
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              " 0.0,\n",
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              " 0.0,\n",
              " 1.0,\n",
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              " 0.0,\n",
              " 0.0,\n",
              " 0.0,\n",
              " 0.0,\n",
              " 0.0]"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example['labels']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghNWFH0CoUkS",
        "outputId": "53d54b30-335d-43b4-9ee1-7c9c5f919220"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Quality_Care', 'Environment', 'Access', 'Patient-Staff_Communication']"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "[id2label[idx] for idx, label in enumerate(example['labels']) if label == 1.0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p3lnfDO9oUmc"
      },
      "outputs": [],
      "source": [
        "encoded_dataset.set_format(\"torch\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "DTpNWhycouPI"
      },
      "source": [
        "## Config the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exO_e8OzoUo3",
        "outputId": "2061a37a-b6b3-4b4c-dc4d-5295e7e6610a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at /content/drive/My Drive/Colab/thesis_2/arnab_10e/model were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at /content/drive/My Drive/Colab/thesis_2/arnab_10e/model and are newly initialized: ['classifier.bias', 'bert.pooler.dense.weight', 'classifier.weight', 'bert.pooler.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForSequenceClassification, AutoModel, BertForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, problem_type=\"multi_label_classification\",\n",
        "                                                           num_labels=len(labels),id2label=id2label,label2id=label2id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Qm7MiokowK9"
      },
      "outputs": [],
      "source": [
        "batch_size = 16 # * strategy.num_replicas_in_sync #TPU thing\n",
        "metric_name = \"f1_macro\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gw3VuW7WowNj"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "args = TrainingArguments(\n",
        "    output_dir='PX_finetuned2', #-----------\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=5,\n",
        "    weight_decay=0.01,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=metric_name,\n",
        "    #push_to_hub=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H9h0vOPIowQM"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score, precision_score, recall_score\n",
        "from transformers import EvalPrediction\n",
        "#import torch\n",
        "\n",
        "# source: https://jesusleal.io/2021/04/21/Longformer-multilabel-classification/\n",
        "def multi_label_metrics(predictions, labels, threshold=0.5):\n",
        "    # first, apply sigmoid on predictions which are of shape (batch_size, num_labels)\n",
        "    sigmoid = torch.nn.Sigmoid()\n",
        "    probs = sigmoid(torch.Tensor(predictions))\n",
        "    # next, use threshold to turn them into integer predictions\n",
        "    y_pred = np.zeros(probs.shape)\n",
        "    y_pred[np.where(probs >= threshold)] = 1\n",
        "    # finally, compute metrics\n",
        "    y_true = labels\n",
        "    f1_micro_average = f1_score(y_true=y_true, y_pred=y_pred, average='micro', zero_division=1)\n",
        "    f1_macro_average = f1_score(y_true=y_true, y_pred=y_pred, average='macro', zero_division=1)\n",
        "    f1_weighted_average = f1_score(y_true=y_true, y_pred=y_pred, average='weighted', zero_division=1)\n",
        "    roc_auc = roc_auc_score(y_true, y_pred, average = 'micro')\n",
        "    accuracy = accuracy_score(y_true, y_pred)\n",
        "    recall = recall_score(y_true, y_pred, average='macro', zero_division=1)\n",
        "    precision = precision_score(y_true, y_pred, average='macro', zero_division=1)\n",
        "\n",
        "\n",
        "    # return as dictionary\n",
        "    metrics = {'eval_f1_micro': f1_micro_average,\n",
        "            'eval_f1_macro': f1_macro_average,\n",
        "            'eval_f1_wighted': f1_weighted_average,\n",
        "               'eval_roc_auc': roc_auc,\n",
        "               'eval_accuracy': accuracy,\n",
        "               'eval_precision': precision,\n",
        "               'eval_recall' : recall}\n",
        "    return metrics\n",
        "\n",
        "def compute_metrics(p: EvalPrediction):\n",
        "    preds = p.predictions[0] if isinstance(p.predictions,\n",
        "            tuple) else p.predictions\n",
        "    result = multi_label_metrics(\n",
        "        predictions=preds,\n",
        "        labels=p.label_ids)\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "U73MYvolowSO",
        "outputId": "639075d0-cf2b-45db-d663-88f3fcfd85d3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'torch.FloatTensor'"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# assume this works without any modifications.\n",
        "encoded_dataset['train'][0]['labels'].type()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T413wCpEo_In",
        "outputId": "c40f28c2-dcca-483f-fedf-651b3a1bf6dc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
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            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "encoded_dataset['train']['input_ids'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_QL_Onwo_LT",
        "outputId": "2186d3b8-3531-4485-e289-44877326481e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SequenceClassifierOutput(loss=tensor(0.6718, grad_fn=<BinaryCrossEntropyWithLogitsBackward0>), logits=tensor([[-0.4182,  0.1906,  0.1998, -0.0262,  0.1060, -0.4752,  0.1018,  0.0988,\n",
              "         -0.1378,  0.2592, -0.5302, -0.4606,  0.0951,  0.2822, -0.0216, -0.0247,\n",
              "         -0.0256, -0.0810,  0.0834, -0.1525, -0.1884,  0.0462, -0.3347,  0.3123,\n",
              "          0.2234]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#forward pass\n",
        "outputs = model(input_ids=encoded_dataset['train']['input_ids'][0].unsqueeze(0), labels=encoded_dataset['train'][0]['labels'].unsqueeze(0))\n",
        "outputs"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "PmG5vCI1pC5j"
      },
      "source": [
        "### Training the customized BERT model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e9WCCu06owUa"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset= encoded_dataset[\"train\"],\n",
        "    eval_dataset= encoded_dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HISPKDcVpQNR",
        "outputId": "fd3870c0-fc73-432e-e20b-fa30160fd749"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 11536\n",
            "  Num Epochs = 5\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 = 3605\n",
            "  Number of trainable parameters = 81933337\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3605' max='3605' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3605/3605 13:18, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1 Micro</th>\n",
              "      <th>F1 Macro</th>\n",
              "      <th>F1 Wighted</th>\n",
              "      <th>Roc Auc</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.185900</td>\n",
              "      <td>0.079618</td>\n",
              "      <td>0.708493</td>\n",
              "      <td>0.248942</td>\n",
              "      <td>0.642003</td>\n",
              "      <td>0.812517</td>\n",
              "      <td>0.626948</td>\n",
              "      <td>0.913386</td>\n",
              "      <td>0.234178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.127300</td>\n",
              "      <td>0.070392</td>\n",
              "      <td>0.692452</td>\n",
              "      <td>0.278675</td>\n",
              "      <td>0.651715</td>\n",
              "      <td>0.812618</td>\n",
              "      <td>0.583997</td>\n",
              "      <td>0.849575</td>\n",
              "      <td>0.271558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.097800</td>\n",
              "      <td>0.068154</td>\n",
              "      <td>0.685934</td>\n",
              "      <td>0.309648</td>\n",
              "      <td>0.656799</td>\n",
              "      <td>0.817928</td>\n",
              "      <td>0.568756</td>\n",
              "      <td>0.721772</td>\n",
              "      <td>0.305345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.069729</td>\n",
              "      <td>0.685998</td>\n",
              "      <td>0.312850</td>\n",
              "      <td>0.666282</td>\n",
              "      <td>0.835727</td>\n",
              "      <td>0.560097</td>\n",
              "      <td>0.669793</td>\n",
              "      <td>0.320901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.078400</td>\n",
              "      <td>0.070928</td>\n",
              "      <td>0.676159</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.662784</td>\n",
              "      <td>0.827967</td>\n",
              "      <td>0.551437</td>\n",
              "      <td>0.636078</td>\n",
              "      <td>0.325409</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2887\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to PX_finetuned2/checkpoint-721\n",
            "Configuration saved in PX_finetuned2/checkpoint-721/config.json\n",
            "Model weights saved in PX_finetuned2/checkpoint-721/pytorch_model.bin\n",
            "tokenizer config file saved in PX_finetuned2/checkpoint-721/tokenizer_config.json\n",
            "Special tokens file saved in PX_finetuned2/checkpoint-721/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2887\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to PX_finetuned2/checkpoint-1442\n",
            "Configuration saved in PX_finetuned2/checkpoint-1442/config.json\n",
            "Model weights saved in PX_finetuned2/checkpoint-1442/pytorch_model.bin\n",
            "tokenizer config file saved in PX_finetuned2/checkpoint-1442/tokenizer_config.json\n",
            "Special tokens file saved in PX_finetuned2/checkpoint-1442/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2887\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to PX_finetuned2/checkpoint-2163\n",
            "Configuration saved in PX_finetuned2/checkpoint-2163/config.json\n",
            "Model weights saved in PX_finetuned2/checkpoint-2163/pytorch_model.bin\n",
            "tokenizer config file saved in PX_finetuned2/checkpoint-2163/tokenizer_config.json\n",
            "Special tokens file saved in PX_finetuned2/checkpoint-2163/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2887\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to PX_finetuned2/checkpoint-2884\n",
            "Configuration saved in PX_finetuned2/checkpoint-2884/config.json\n",
            "Model weights saved in PX_finetuned2/checkpoint-2884/pytorch_model.bin\n",
            "tokenizer config file saved in PX_finetuned2/checkpoint-2884/tokenizer_config.json\n",
            "Special tokens file saved in PX_finetuned2/checkpoint-2884/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2887\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to PX_finetuned2/checkpoint-3605\n",
            "Configuration saved in PX_finetuned2/checkpoint-3605/config.json\n",
            "Model weights saved in PX_finetuned2/checkpoint-3605/pytorch_model.bin\n",
            "tokenizer config file saved in PX_finetuned2/checkpoint-3605/tokenizer_config.json\n",
            "Special tokens file saved in PX_finetuned2/checkpoint-3605/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from PX_finetuned2/checkpoint-3605 (score: 0.31578933977912876).\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=3605, training_loss=0.10997242590260076, metrics={'train_runtime': 798.8249, 'train_samples_per_second': 72.206, 'train_steps_per_second': 4.513, 'total_flos': 7643853566361600.0, 'train_loss': 0.10997242590260076, 'epoch': 5.0})"
            ]
          },
          "execution_count": 164,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "GZF1MCgFpQP2",
        "outputId": "f0f17e1e-83b6-423d-ac56-0f4acbfc527b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2887\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='181' max='181' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [181/181 00:12]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "{'eval_f1_micro': 0.6761593226861651,\n",
              " 'eval_f1_macro': 0.31578933977912876,\n",
              " 'eval_f1_wighted': 0.662783759061472,\n",
              " 'eval_roc_auc': 0.8279672403818117,\n",
              " 'eval_accuracy': 0.5514374783512297,\n",
              " 'eval_precision': 0.6360776960118231,\n",
              " 'eval_recall': 0.32540860260107296,\n",
              " 'eval_loss': 0.07092835009098053,\n",
              " 'eval_runtime': 12.3921,\n",
              " 'eval_samples_per_second': 232.972,\n",
              " 'eval_steps_per_second': 14.606,\n",
              " 'epoch': 5.0}"
            ]
          },
          "execution_count": 165,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.evaluate()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "pUl5Tsq6pVb3"
      },
      "source": [
        "## Find classification report and Confusion matrix"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "e8y0sK6AR1l4"
      },
      "source": [
        "#### Predict:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "referenced_widgets": [
            "66e7878d3c534f218c2c30539228d5ab",
            "59fad34aefb44febb3d0129b9b4e1c3f",
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            "3628547bb4de4d2288547941013e980f",
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            "bb0893939a7541218ea7c494622f80a4",
            "3e1eae799ef142dead9163d8950ce264",
            "8fb82c79e8164c509bb4bcc61a58aa06",
            "62cf7b70f1e14e0f8cd919e2b8e27663",
            "6847779fe74e4d1c899238cad6f49acb",
            "4229663a824941fd85dfa7d5e131b888",
            "6e519d7bdccf44b4b407c107f5960b9d",
            "bde2c6810b384ed38909ca3862b3df60",
            "f33ca9549e6747318ce84bd39c3b8c43",
            "3ced2d2af0c645568fe8f2423ad59798",
            "e692e42588b348a4a077939187e64963",
            "6c785ab320e44cf3a6b7cb86e4b419e7",
            "052aa723b6674967814ce5d89bf1b046",
            "5258df98390145328c311967d0e261bb",
            "cde70fee495d444fbc08dc28ed96416b",
            "3783492c5e40498fa352598f7b81f8fe",
            "fe1e2d8344b14e76a13f5905f0ad46ce",
            "cda06bcad25148e190b543c5e9a8798d",
            "1a74b523b6f34f50872d73d68edb357d",
            "8c52aeec57684673a5349cdcae1c93b7",
            "6d7f7fb4374a45b28a0b9fa5fb538615",
            "d6be90569d1141169df6e926d1181c60",
            "551e8536001e4bd78966920f58e66eb4",
            "243374150ba740e9b7932c2df8a444c8",
            "dede081101884008ad3d59ecbdf3b872",
            "1513602bf645404b924b807e719dda42",
            "c302e1b9838c4823a039f50d80a16089",
            "41420abe495d44cb94368db79fa405af",
            "d5d057f8d6a2439892e4d33599c70bee",
            "804044c5d8c6495c9076f709b0ad9681",
            "ac1c68b0e57640dfb72308eac9277958",
            "482773e3376d44c882550f89f63d87a8",
            "e7db0c524441409eac1c5e2da4d7d199",
            "f4f622aeba244f49aed6f2667cc5c332"
          ]
        },
        "id": "QvChGw1hwnGT",
        "outputId": "205711ec-64ba-4c8a-e498-74c4c270d459"
      },
      "outputs": [],
      "source": [
        "testing_set = load_dataset('csv' , data_files={'test': 'testing.csv'} )\n",
        "testing_set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lH_bydT9wnGT"
      },
      "outputs": [],
      "source": [
        "testing_set['test']\n",
        "testing_labels = [label for label in testing_set['test'].features.keys() if label not in ['Journey#refnumber', 'cleaned_comment' , 'classes', 'Unnamed: 0']]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "referenced_widgets": [
            "8e9c437f37c242ca873ecb46bbd76421",
            "aeac48455ba34860bbe22e7369dfbe8c",
            "d01ac49d298840fea42b25c50c9a4177",
            "4cc30e57f4114381aa23c0676bacb5e6",
            "abe730b624884590ab57d3f6bedac7b8",
            "e45a703aaa564276b8233c07c0d258d6",
            "9724e80fe1c84b2fb1bc0531af0d7f56",
            "dd5818d8e9c24db385c532fa691ff4b2",
            "050dd363c10f45648e92c068023d31a1",
            "692a4eaf29734fedbd8bb7d35dab6690",
            "edebc07d2b2a49b996545a882dbf6f94"
          ]
        },
        "id": "_i8z-VCpwnGT",
        "outputId": "3f54c055-3ef3-4543-dda0-e25ed8d9fa5b"
      },
      "outputs": [],
      "source": [
        "encoded_testing_set = testing_set.map(preprocess_data, batched=True, remove_columns=dataset['test'].column_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3wDAGd5wnGR",
        "outputId": "5fefa261-a6cb-4f20-f99c-98fbc919fa42"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Prediction *****\n",
            "  Num examples = 2887\n",
            "  Batch size = 16\n"
          ]
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "trainer_pred = trainer.predict(encoded_testing_set['test'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NIF1DnYzwnGS",
        "outputId": "80bbb303-c9dd-4065-9fe6-0a1dee18bc63"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PredictionOutput(predictions=array([[-4.379275 , -4.79945  , -1.486343 , ..., -5.0861974, -5.6341724,\n",
              "        -6.536087 ],\n",
              "       [-4.270345 , -3.3798876, -4.512876 , ..., -4.3418384, -3.8979924,\n",
              "        -4.5678124],\n",
              "       [-3.1566002, -4.1799965,  2.5087142, ..., -3.7455308, -6.1417303,\n",
              "        -6.575617 ],\n",
              "       ...,\n",
              "       [-3.1621642, -5.473138 , -4.081207 , ..., -3.4150841, -4.4314957,\n",
              "        -6.5485206],\n",
              "       [-4.620775 , -4.679479 , -3.8299007, ..., -3.2842774, -2.7619631,\n",
              "        -5.691662 ],\n",
              "       [-4.461535 , -5.6737366, -4.0782666, ..., -5.5038023, -5.4350505,\n",
              "        -6.7707887]], dtype=float32), label_ids=array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 1., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 1., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32), metrics={'test_loss': 0.07092835009098053, 'test_eval_f1_micro': 0.6761593226861651, 'test_eval_f1_macro': 0.31578933977912876, 'test_eval_f1_wighted': 0.662783759061472, 'test_eval_roc_auc': 0.8279672403818117, 'test_eval_accuracy': 0.5514374783512297, 'test_eval_precision': 0.6360776960118231, 'test_eval_recall': 0.32540860260107296, 'test_runtime': 13.2404, 'test_samples_per_second': 218.045, 'test_steps_per_second': 13.67})"
            ]
          },
          "execution_count": 170,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xu1wqafhwnGS",
        "outputId": "1a4a1056-d67c-4b65-d06f-7a21b13ecea8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([-4.270345  , -3.3798876 , -4.512876  , -4.093629  , -3.8853452 ,\n",
              "       -3.5019667 , -3.4584525 , -4.0076795 , -4.14427   , -2.9798176 ,\n",
              "       -4.4448695 , -4.033309  , -4.5611653 , -3.9152997 ,  0.54741365,\n",
              "       -2.9987519 , -4.7571836 , -3.8120952 , -3.4229524 , -2.202561  ,\n",
              "       -2.7662127 , -3.9602778 , -4.3418384 , -3.8979924 , -4.5678124 ],\n",
              "      dtype=float32)"
            ]
          },
          "execution_count": 171,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer_pred.predictions[1]"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "4QgqTgc_SAKu"
      },
      "source": [
        "#### Convert the testing and prediction labels to compatible format:\n",
        "INTEGER."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "31PW8w1RXH1t"
      },
      "outputs": [],
      "source": [
        "#trainer_pred.predictions\n",
        "#preds = np.argmax(trainer_pred.predictions, axis=-1)\n",
        "\n",
        "# now convert the results of prediction into integer rather than float\n",
        "def convert_preds(y_preds):\n",
        "  # 1st convert from float to boolean\n",
        "  y_pred1_bool = (y_preds > 0.5)\n",
        "  #print(y_pred1_bool[0:2])\n",
        "\n",
        "  # 2nd match the boolean to integer\n",
        "  y_pred1_int = []\n",
        "  for row in y_pred1_bool:\n",
        "    temp = []\n",
        "    for j in row:\n",
        "      if j: # if True\n",
        "        temp.append(1)\n",
        "      else: # if False\n",
        "        temp.append(0)\n",
        "    y_pred1_int.append(temp)\n",
        "\n",
        "  #print(\"first 2 rows:\\n\" ,y_pred1_int[0:2])\n",
        "  return y_pred1_int\n",
        "\n",
        "# y_pred1_int = convert_preds(y_pred1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swrc7bCTwnGT",
        "outputId": "d13e158c-ce93-4b7a-ec24-17eb6c073334"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0]), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0]), array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0]), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0]), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0])]\n"
          ]
        }
      ],
      "source": [
        "#trainer_pred[1]\n",
        "\n",
        "# the following, is combining the labels for each row of data in an array:\n",
        "\"\"\" \"\"\"\n",
        "test_labels = []\n",
        "for i in range(0,len(encoded_dataset['test'])):\n",
        "    test_labels.append(encoded_dataset[\"test\"][i]['labels'].numpy().astype(int)\n",
        ")\n",
        "\n",
        "print(test_labels[0:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Sk7EGJSwnGS"
      },
      "outputs": [],
      "source": [
        "#trainer_pred[1]\n",
        "#type(test_labels)\n",
        "reshaped_testing_labels = np.array(test_labels)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WmH2ciG2wnGS",
        "outputId": "865a0954-3235-4c4e-ece8-9479ec58819e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 1, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 1, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "execution_count": 175,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "reshaped_testing_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0HOmwO-bPxJo"
      },
      "outputs": [],
      "source": [
        "y_pred1_int = convert_preds(trainer_pred.predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fS9L6kNsP2ZT",
        "outputId": "228acb1d-f58d-48dd-ac59-6292431a8ae5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              " [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]"
            ]
          },
          "execution_count": 177,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred1_int[:3]"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Je-gCmVXSJ8J"
      },
      "source": [
        "#### Print Classification Report, Confusion Matrix, and Plot Confusion Matrix."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m89dZ5LoP5mA",
        "outputId": "fb78985c-4f83-40da-c4d5-ff1eb5c4e156"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.17      0.27        12\n",
            "           1       0.00      0.00      0.00        10\n",
            "           2       0.56      0.46      0.50       399\n",
            "           3       0.00      0.00      0.00        99\n",
            "           4       0.00      0.00      0.00        17\n",
            "           5       0.79      0.81      0.80       115\n",
            "           6       0.00      0.00      0.00        63\n",
            "           7       0.30      0.19      0.23        16\n",
            "           8       0.58      0.20      0.30        75\n",
            "           9       0.91      0.84      0.87      1246\n",
            "          10       1.00      0.08      0.15        37\n",
            "          11       0.00      0.00      0.00         7\n",
            "          12       0.38      0.64      0.48        47\n",
            "          13       0.55      0.32      0.40       113\n",
            "          14       0.41      0.39      0.40        23\n",
            "          15       0.74      0.69      0.72        78\n",
            "          16       0.00      0.00      0.00         2\n",
            "          17       0.67      0.74      0.70       239\n",
            "          18       0.00      0.00      0.00         8\n",
            "          19       0.00      0.00      0.00         1\n",
            "          20       0.00      0.00      0.00         0\n",
            "          21       0.00      0.00      0.00         8\n",
            "          22       0.00      0.00      0.00         1\n",
            "          23       0.00      0.00      0.00        16\n",
            "          24       0.00      0.00      0.00         0\n",
            "\n",
            "   micro avg       0.74      0.63      0.68      2632\n",
            "   macro avg       0.30      0.22      0.23      2632\n",
            "weighted avg       0.70      0.63      0.65      2632\n",
            " samples avg       0.56      0.51      0.53      2632\n",
            "\n",
            "0.5691028749567024\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, multilabel_confusion_matrix,accuracy_score\n",
        "\n",
        "print(classification_report(test_labels,y_pred1_int))\n",
        "print(accuracy_score(test_labels,y_pred1_int))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "miXzKJiZQqxm"
      },
      "outputs": [],
      "source": [
        "cf_bert = multilabel_confusion_matrix(test_labels,y_pred1_int)\n",
        "#print(cf_bert)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "pZcLAuMrwnGU"
      },
      "source": [
        "## Plot Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpSWlCxGwnGU",
        "outputId": "4367854c-04a5-439d-b42f-05c90d29c535"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2874 2877 2346 2788 2870 2748 2824 2864 2801 1535 2850 2880 2791 2744\n",
            " 2851 2790 2885 2560 2879 2816 2887 2879 2869 2870 2887] \n",
            " [   2    0  182    0    0   93    0    3   15 1048    3    0   30   36\n",
            "    9   54    0  178    0    0    0    0    0    0    0] \n",
            " [ 10  10 217  99  17  22  63  13  60 198  34   7  17  77  14  24   2  61\n",
            "   8   1   0   8   1  16   0] \n",
            " [  1   0 142   0   0  24   0   7  11 106   0   0  49  30  13  19   0  88\n",
            "   0  70   0   0  17   1   0]\n"
          ]
        }
      ],
      "source": [
        "tn_cf_bert = cf_bert[:, 0, 0]\n",
        "tp_cf_bert = cf_bert[:, 1, 1]\n",
        "fn_cf_bert = cf_bert[:, 1, 0]\n",
        "fp_cf_bert = cf_bert[:, 0, 1]\n",
        "print(tn_cf_bert , '\\n',tp_cf_bert, '\\n', fn_cf_bert ,'\\n' , fp_cf_bert)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvQm26h4XYWD",
        "outputId": "c7eb78d0-3dae-49fd-d850-ec97e0d13b99"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Examination', 'Patient_Journey', 'Quality_Care', 'Treatment',\n",
              "       'Diagnosis', 'Medication_Vaccination', 'Safety_Inceidents',\n",
              "       'Skills_conducts', 'Administrative_Policies_procedures', 'Environment',\n",
              "       'Safety_Security', 'Finance_Billing', 'Staffing', 'Resources',\n",
              "       'Medical_Records', 'Access', 'Patient_Disposition', 'Delays',\n",
              "       'Referrals', 'Patient-Staff_Communication', 'Incorrect_Information',\n",
              "       'Emotional_Support', 'Assault_Harassment', 'Confidentiality',\n",
              "       'Consent'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 181,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cols"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "_ZgWddbOwnGU",
        "outputId": "0842f47e-bb65-4625-903d-fbd06b3b3160"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x720 with 25 Axes>"
            ]
          },
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          },
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        }
      ],
      "source": [
        "# plot cf\n",
        "#sys.path.insert(0,\"/content/drive/My Drive/Colab/thesis_2/\")\n",
        "import PlotConfusion\n",
        "PlotConfusion.print_cf(cf_bert,cols)"
      ]
    }
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