ner_wangchanberta_base_att.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "6hmtblfP65Wk",
        "outputId": "84d47139-068d-4626-ffd4-4eff22d1370c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n",
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            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
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            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multiprocess<0.70.17 (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py311-none-any.whl.metadata (7.2 kB)\n",
            "Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
            "  Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
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            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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            "\u001b[?25hDownloading multiprocess-0.70.16-py311-none-any.whl (143 kB)\n",
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            "\u001b[?25hDownloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
            "      Successfully uninstalled fsspec-2024.10.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "yYEB8Cnx661m",
        "outputId": "968b2fd2-86e0-4c0f-8d73-9e65402321f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (3.2.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2024.12.14)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets transformers torch scikit-learn\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "id": "aHq7ohDEIcDv",
        "outputId": "a4d37b78-447e-4610-be88-d985209c953a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleaned file saved to: train_cleaned.jsonl\n",
            "Cleaned file saved to: validation_cleaned.jsonl\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
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            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/282 [00:00<?, ?B/s]"
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          "text": [
            "Label Counts: {0: 416, 1: 16916, 2: 14132, 3: 31253, 4: 30653, 5: 14, 6: 12940, 7: 46417, 8: 44764, 9: 824, 10: 20076, 11: 92, 12: 8392, 13: 11649, 14: 27713, 15: 8992, 16: 964, 17: 28215, 18: 18280, 19: 281, 20: 44, 21: 109, 22: 8297, 23: 30685, 24: 38910, 25: 11185, 26: 1619, 27: 42709, 28: 19997, 29: 326, 30: 217, 31: 1990261}\n",
            "Label Weights: {0: 185.34720552884616, 1: 4.558077411917711, 2: 5.456017371921879, 3: 2.4671051579048413, 4: 2.5153961276220924, 5: 5507.459821428572, 6: 5.95861186244204, 7: 1.6611249649912747, 8: 1.7224653181127692, 9: 93.57334648058253, 10: 3.8406274905359634, 11: 838.0917119565217, 12: 9.187850035748331, 13: 6.6189748047042665, 14: 2.7822479522245875, 15: 8.57478175044484, 16: 79.98385632780084, 17: 2.732746322877902, 18: 4.2179670404814, 19: 274.3930160142349, 20: 1752.3735795454545, 21: 707.3801605504588, 22: 9.29305019886706, 23: 2.512772934658628, 24: 1.9816098046774608, 25: 6.893557219490389, 26: 47.62472977146387, 27: 1.8053440141422183, 28: 3.8558002450367557, 29: 236.51667944785277, 30: 355.3199884792627, 31: 0.03874086740382292}\n"
          ]
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        {
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          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/423M [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of CamembertForTokenClassification were not initialized from the model checkpoint at airesearch/wangchanberta-base-att-spm-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
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              "      \n",
              "      <progress value='23742' max='23742' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [23742/23742 2:41:32, Epoch 3/3]\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",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.382500</td>\n",
              "      <td>0.633428</td>\n",
              "      <td>0.420189</td>\n",
              "      <td>0.675748</td>\n",
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              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.621100</td>\n",
              "      <td>0.441493</td>\n",
              "      <td>0.444611</td>\n",
              "      <td>0.743531</td>\n",
              "      <td>0.526504</td>\n",
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              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.528000</td>\n",
              "      <td>0.369715</td>\n",
              "      <td>0.465699</td>\n",
              "      <td>0.807925</td>\n",
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              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.423800</td>\n",
              "      <td>0.310586</td>\n",
              "      <td>0.537295</td>\n",
              "      <td>0.822475</td>\n",
              "      <td>0.612765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.379700</td>\n",
              "      <td>0.305706</td>\n",
              "      <td>0.498068</td>\n",
              "      <td>0.830167</td>\n",
              "      <td>0.594923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.418500</td>\n",
              "      <td>0.290350</td>\n",
              "      <td>0.487455</td>\n",
              "      <td>0.835610</td>\n",
              "      <td>0.587157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.331300</td>\n",
              "      <td>0.262665</td>\n",
              "      <td>0.535259</td>\n",
              "      <td>0.846307</td>\n",
              "      <td>0.622533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.352600</td>\n",
              "      <td>0.260890</td>\n",
              "      <td>0.543329</td>\n",
              "      <td>0.830608</td>\n",
              "      <td>0.635156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.367600</td>\n",
              "      <td>0.237038</td>\n",
              "      <td>0.526180</td>\n",
              "      <td>0.847881</td>\n",
              "      <td>0.620122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.350900</td>\n",
              "      <td>0.238695</td>\n",
              "      <td>0.549981</td>\n",
              "      <td>0.859711</td>\n",
              "      <td>0.638891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.311800</td>\n",
              "      <td>0.218252</td>\n",
              "      <td>0.550575</td>\n",
              "      <td>0.862281</td>\n",
              "      <td>0.637169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.278000</td>\n",
              "      <td>0.200581</td>\n",
              "      <td>0.576046</td>\n",
              "      <td>0.869151</td>\n",
              "      <td>0.664935</td>\n",
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              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.322200</td>\n",
              "      <td>0.205975</td>\n",
              "      <td>0.564336</td>\n",
              "      <td>0.882700</td>\n",
              "      <td>0.650681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.324900</td>\n",
              "      <td>0.226431</td>\n",
              "      <td>0.533453</td>\n",
              "      <td>0.875414</td>\n",
              "      <td>0.627582</td>\n",
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              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.296000</td>\n",
              "      <td>0.188887</td>\n",
              "      <td>0.577194</td>\n",
              "      <td>0.878993</td>\n",
              "      <td>0.669862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.284000</td>\n",
              "      <td>0.213303</td>\n",
              "      <td>0.555616</td>\n",
              "      <td>0.875534</td>\n",
              "      <td>0.649729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>0.293500</td>\n",
              "      <td>0.259615</td>\n",
              "      <td>0.526134</td>\n",
              "      <td>0.855975</td>\n",
              "      <td>0.614702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.275500</td>\n",
              "      <td>0.207825</td>\n",
              "      <td>0.527934</td>\n",
              "      <td>0.876808</td>\n",
              "      <td>0.622094</td>\n",
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              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>0.272700</td>\n",
              "      <td>0.197321</td>\n",
              "      <td>0.540946</td>\n",
              "      <td>0.879208</td>\n",
              "      <td>0.633349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.259400</td>\n",
              "      <td>0.182542</td>\n",
              "      <td>0.569394</td>\n",
              "      <td>0.892936</td>\n",
              "      <td>0.654965</td>\n",
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              "    <tr>\n",
              "      <td>10500</td>\n",
              "      <td>0.261700</td>\n",
              "      <td>0.189615</td>\n",
              "      <td>0.557008</td>\n",
              "      <td>0.884647</td>\n",
              "      <td>0.649144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11000</td>\n",
              "      <td>0.225700</td>\n",
              "      <td>0.201031</td>\n",
              "      <td>0.565838</td>\n",
              "      <td>0.879453</td>\n",
              "      <td>0.655314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11500</td>\n",
              "      <td>0.245000</td>\n",
              "      <td>0.206691</td>\n",
              "      <td>0.557100</td>\n",
              "      <td>0.872088</td>\n",
              "      <td>0.647614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12000</td>\n",
              "      <td>0.236200</td>\n",
              "      <td>0.207711</td>\n",
              "      <td>0.567690</td>\n",
              "      <td>0.871687</td>\n",
              "      <td>0.657872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12500</td>\n",
              "      <td>0.300600</td>\n",
              "      <td>0.206728</td>\n",
              "      <td>0.535821</td>\n",
              "      <td>0.877789</td>\n",
              "      <td>0.628136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13000</td>\n",
              "      <td>0.260500</td>\n",
              "      <td>0.208863</td>\n",
              "      <td>0.558845</td>\n",
              "      <td>0.870958</td>\n",
              "      <td>0.648432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13500</td>\n",
              "      <td>0.280500</td>\n",
              "      <td>0.175350</td>\n",
              "      <td>0.558778</td>\n",
              "      <td>0.888192</td>\n",
              "      <td>0.654174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14000</td>\n",
              "      <td>0.255200</td>\n",
              "      <td>0.165753</td>\n",
              "      <td>0.564734</td>\n",
              "      <td>0.895043</td>\n",
              "      <td>0.657220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14500</td>\n",
              "      <td>0.215600</td>\n",
              "      <td>0.176393</td>\n",
              "      <td>0.562203</td>\n",
              "      <td>0.891056</td>\n",
              "      <td>0.656130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15000</td>\n",
              "      <td>0.231500</td>\n",
              "      <td>0.174455</td>\n",
              "      <td>0.576419</td>\n",
              "      <td>0.891029</td>\n",
              "      <td>0.668568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15500</td>\n",
              "      <td>0.237400</td>\n",
              "      <td>0.182909</td>\n",
              "      <td>0.579720</td>\n",
              "      <td>0.881543</td>\n",
              "      <td>0.672008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16000</td>\n",
              "      <td>0.255600</td>\n",
              "      <td>0.167764</td>\n",
              "      <td>0.558705</td>\n",
              "      <td>0.892711</td>\n",
              "      <td>0.651034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16500</td>\n",
              "      <td>0.180600</td>\n",
              "      <td>0.160383</td>\n",
              "      <td>0.568677</td>\n",
              "      <td>0.892108</td>\n",
              "      <td>0.654168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17000</td>\n",
              "      <td>0.191400</td>\n",
              "      <td>0.155923</td>\n",
              "      <td>0.598317</td>\n",
              "      <td>0.894013</td>\n",
              "      <td>0.686865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17500</td>\n",
              "      <td>0.204100</td>\n",
              "      <td>0.143747</td>\n",
              "      <td>0.605618</td>\n",
              "      <td>0.903241</td>\n",
              "      <td>0.694017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18000</td>\n",
              "      <td>0.184600</td>\n",
              "      <td>0.157690</td>\n",
              "      <td>0.589322</td>\n",
              "      <td>0.896436</td>\n",
              "      <td>0.678948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18500</td>\n",
              "      <td>0.162500</td>\n",
              "      <td>0.143994</td>\n",
              "      <td>0.602646</td>\n",
              "      <td>0.900839</td>\n",
              "      <td>0.692171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19000</td>\n",
              "      <td>0.191000</td>\n",
              "      <td>0.135112</td>\n",
              "      <td>0.612164</td>\n",
              "      <td>0.906154</td>\n",
              "      <td>0.699341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19500</td>\n",
              "      <td>0.180500</td>\n",
              "      <td>0.146135</td>\n",
              "      <td>0.596950</td>\n",
              "      <td>0.902856</td>\n",
              "      <td>0.684297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20000</td>\n",
              "      <td>0.201800</td>\n",
              "      <td>0.149167</td>\n",
              "      <td>0.604509</td>\n",
              "      <td>0.902088</td>\n",
              "      <td>0.692157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20500</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.147010</td>\n",
              "      <td>0.599792</td>\n",
              "      <td>0.903979</td>\n",
              "      <td>0.687673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21000</td>\n",
              "      <td>0.188200</td>\n",
              "      <td>0.144034</td>\n",
              "      <td>0.601640</td>\n",
              "      <td>0.902913</td>\n",
              "      <td>0.690628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21500</td>\n",
              "      <td>0.198700</td>\n",
              "      <td>0.138949</td>\n",
              "      <td>0.594026</td>\n",
              "      <td>0.907396</td>\n",
              "      <td>0.679850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22000</td>\n",
              "      <td>0.175900</td>\n",
              "      <td>0.135578</td>\n",
              "      <td>0.601940</td>\n",
              "      <td>0.908948</td>\n",
              "      <td>0.688418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22500</td>\n",
              "      <td>0.188800</td>\n",
              "      <td>0.135475</td>\n",
              "      <td>0.604990</td>\n",
              "      <td>0.907756</td>\n",
              "      <td>0.691501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23000</td>\n",
              "      <td>0.182400</td>\n",
              "      <td>0.135538</td>\n",
              "      <td>0.606538</td>\n",
              "      <td>0.907574</td>\n",
              "      <td>0.692841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23500</td>\n",
              "      <td>0.166600</td>\n",
              "      <td>0.134733</td>\n",
              "      <td>0.603319</td>\n",
              "      <td>0.908166</td>\n",
              "      <td>0.690305</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model and tokenizer saved!\n",
            "Submission file saved successfully!\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "import torch\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import classification_report\n",
        "from datasets import load_dataset, DatasetDict\n",
        "from transformers import (\n",
        "    AutoTokenizer,\n",
        "    AutoModelForTokenClassification,\n",
        "    DataCollatorForTokenClassification,\n",
        "    TrainingArguments,\n",
        "    Trainer,\n",
        ")\n",
        "from torch.nn import CrossEntropyLoss\n",
        "\n",
        "\n",
        "# Clean JSONL File\n",
        "def clean_jsonl(input_file, output_file):\n",
        "    fixed_lines = []\n",
        "    with open(input_file, \"r\", encoding=\"utf-8\") as file:\n",
        "        for i, line in enumerate(file, start=1):\n",
        "            try:\n",
        "                json_data = json.loads(line.strip())\n",
        "                fixed_lines.append(json.dumps(json_data))\n",
        "            except json.JSONDecodeError as e:\n",
        "                print(f\"Skipping invalid line {i}: {e}\")\n",
        "    with open(output_file, \"w\", encoding=\"utf-8\") as fixed_file:\n",
        "        fixed_file.write(\"\\n\".join(fixed_lines))\n",
        "    print(f\"Cleaned file saved to: {output_file}\")\n",
        "\n",
        "\n",
        "# Extract All Unique Labels\n",
        "def extract_labels(dataset):\n",
        "    unique_labels = set()\n",
        "    for labels in dataset[\"train\"][\"ner_tags\"]:\n",
        "        unique_labels.update(labels)\n",
        "    return sorted(list(unique_labels))\n",
        "\n",
        "\n",
        "# Convert Labels to IDs\n",
        "def convert_labels_to_ids(dataset, label2id):\n",
        "    def map_labels(example):\n",
        "        example[\"ner_tags\"] = [label2id[label] for label in example[\"ner_tags\"]]\n",
        "        return example\n",
        "    return dataset.map(map_labels)\n",
        "\n",
        "\n",
        "# Tokenize and Align Labels\n",
        "def tokenize_and_align_labels(examples, tokenizer, label2id):\n",
        "    tokenized_inputs = tokenizer(\n",
        "        examples[\"tokens\"],\n",
        "        truncation=True,\n",
        "        padding=\"max_length\",\n",
        "        is_split_into_words=True,\n",
        "        max_length=128,\n",
        "    )\n",
        "    labels = []\n",
        "    for i, label in enumerate(examples[\"ner_tags\"]):\n",
        "        word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
        "        aligned_labels = []\n",
        "        previous_word_idx = None\n",
        "        for word_idx in word_ids:\n",
        "            if word_idx is None:\n",
        "                aligned_labels.append(-100)\n",
        "            elif word_idx != previous_word_idx:\n",
        "                aligned_labels.append(label[word_idx])\n",
        "            else:\n",
        "                aligned_labels.append(-100)\n",
        "            previous_word_idx = word_idx\n",
        "        labels.append(aligned_labels)\n",
        "    tokenized_inputs[\"labels\"] = labels\n",
        "    return tokenized_inputs\n",
        "\n",
        "\n",
        "# Compute Label Weights\n",
        "def compute_label_weights(dataset, label_list):\n",
        "    label_counts = {label: 0 for label in range(len(label_list))}\n",
        "    for labels in dataset[\"train\"][\"labels\"]:\n",
        "        for label in labels:\n",
        "            if label != -100:\n",
        "                label_counts[label] += 1\n",
        "    total_count = sum(label_counts.values())\n",
        "    weights = {\n",
        "        label: (total_count / (len(label_list) * count)) if count > 0 else 0.0\n",
        "        for label, count in label_counts.items()\n",
        "    }\n",
        "    print(f\"Label Counts: {label_counts}\")\n",
        "    print(f\"Label Weights: {weights}\")\n",
        "    return torch.tensor([weights[label] for label in range(len(label_list))]).to(\n",
        "        \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "    )\n",
        "\n",
        "\n",
        "# Custom Trainer with Weighted Loss\n",
        "class WeightedLossTrainer(Trainer):\n",
        "    def __init__(self, *args, label_weights, **kwargs):\n",
        "        super().__init__(*args, **kwargs)\n",
        "        self.label_weights = label_weights\n",
        "\n",
        "    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):\n",
        "        labels = inputs.pop(\"labels\")\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.logits\n",
        "        loss_fct = CrossEntropyLoss(weight=self.label_weights, ignore_index=-100)\n",
        "        loss = loss_fct(logits.view(-1, len(self.label_weights)), labels.view(-1))\n",
        "        return (loss, outputs) if return_outputs else loss\n",
        "\n",
        "\n",
        "# Metrics for Evaluation\n",
        "def compute_metrics(predictions, label_list):\n",
        "    preds, labels = predictions\n",
        "    preds = np.argmax(preds, axis=2)\n",
        "    true_preds = [[p for p, l in zip(pred, label) if l != -100] for pred, label in zip(preds, labels)]\n",
        "    true_labels = [[l for p, l in zip(pred, label) if l != -100] for pred, label in zip(preds, labels)]\n",
        "    flat_preds = [p for seq in true_preds for p in seq]\n",
        "    flat_labels = [l for seq in true_labels for l in seq]\n",
        "    report = classification_report(flat_labels, flat_preds, output_dict=True, zero_division=0)\n",
        "    return {\n",
        "        \"precision\": report[\"macro avg\"][\"precision\"],\n",
        "        \"recall\": report[\"macro avg\"][\"recall\"],\n",
        "        \"f1\": report[\"macro avg\"][\"f1-score\"],\n",
        "    }\n",
        "\n",
        "\n",
        "# Main Pipeline\n",
        "def main_pipeline(train_file, val_file, model_checkpoint, submission_file, output_dir=\"./results\"):\n",
        "    # Step 1: Clean JSONL files\n",
        "    train_cleaned = \"train_cleaned.jsonl\"\n",
        "    val_cleaned = \"validation_cleaned.jsonl\"\n",
        "    clean_jsonl(train_file, train_cleaned)\n",
        "    clean_jsonl(val_file, val_cleaned)\n",
        "\n",
        "    # Load datasets\n",
        "    dataset_dict = DatasetDict({\n",
        "        \"train\": load_dataset(\"json\", data_files=train_cleaned, split=\"train\"),\n",
        "        \"validation\": load_dataset(\"json\", data_files=val_cleaned, split=\"train\"),\n",
        "    })\n",
        "\n",
        "    #  Extract and map labels\n",
        "    unique_labels = extract_labels(dataset_dict)\n",
        "    label2id = {label: idx for idx, label in enumerate(unique_labels)}\n",
        "    id2label = {idx: label for idx, label in enumerate(unique_labels)}\n",
        "    dataset_dict = convert_labels_to_ids(dataset_dict, label2id)\n",
        "\n",
        "    # Tokenize dataset\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
        "    tokenized_datasets = dataset_dict.map(\n",
        "        lambda examples: tokenize_and_align_labels(examples, tokenizer, label2id),\n",
        "        batched=True,\n",
        "        remove_columns=[\"tokens\", \"ner_tags\"],\n",
        "    )\n",
        "\n",
        "    # Compute label weights\n",
        "    label_weights = compute_label_weights(tokenized_datasets, unique_labels)\n",
        "\n",
        "    # Load model and training arguments\n",
        "    model = AutoModelForTokenClassification.from_pretrained(\n",
        "        model_checkpoint, num_labels=len(unique_labels)\n",
        "    )\n",
        "    training_args = TrainingArguments(\n",
        "        output_dir=output_dir,\n",
        "        eval_strategy=\"steps\",\n",
        "        eval_steps=500,\n",
        "        save_steps=1000,\n",
        "        learning_rate=2e-5,\n",
        "        per_device_train_batch_size=8,\n",
        "        per_device_eval_batch_size=8,\n",
        "        num_train_epochs=3,\n",
        "        weight_decay=0.01,\n",
        "        logging_dir=\"./logs\",\n",
        "        save_total_limit=2,\n",
        "        report_to=\"none\",\n",
        "    )\n",
        "\n",
        "    # Train the model\n",
        "    trainer = WeightedLossTrainer(\n",
        "        model=model,\n",
        "        args=training_args,\n",
        "        train_dataset=tokenized_datasets[\"train\"],\n",
        "        eval_dataset=tokenized_datasets[\"validation\"],\n",
        "        data_collator=DataCollatorForTokenClassification(tokenizer),\n",
        "        compute_metrics=lambda p: compute_metrics(p, unique_labels),\n",
        "        label_weights=label_weights,\n",
        "    )\n",
        "    trainer.train()\n",
        "    trainer.evaluate()\n",
        "\n",
        "    # Save the model\n",
        "    trainer.save_model(\"./ner_model\")\n",
        "    tokenizer.save_pretrained(\"./ner_model\")\n",
        "    print(\"Model and tokenizer saved!\")\n",
        "\n",
        "    # Load sample submission and predict\n",
        "    submission_df = pd.read_csv(submission_file)\n",
        "    predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
        "    preds = np.argmax(predictions.predictions, axis=2)\n",
        "    submission_updates = []\n",
        "    for i, example in enumerate(tokenized_datasets[\"validation\"]):\n",
        "        for j, token in enumerate(example[\"input_ids\"]):\n",
        "            if token != tokenizer.pad_token_id:  # Exclude padding\n",
        "                submission_updates.append((f\"{i}_{j}\", int(preds[i][j])))\n",
        "\n",
        "    updates_df = pd.DataFrame(submission_updates, columns=[\"id\", \"ne\"])\n",
        "    updates_df[\"id\"] = updates_df[\"id\"].apply(lambda x: f\"{int(x.split('_')[0]):05d}_{x.split('_')[1]}\")\n",
        "    updates_df = updates_df[updates_df[\"id\"].isin(submission_df[\"id\"])]\n",
        "    submission_df = submission_df.merge(updates_df, on=\"id\", how=\"left\")\n",
        "    submission_df[\"ne\"] = submission_df[\"ne_y\"].fillna(0).astype(int)\n",
        "    submission_df.drop(columns=[\"ne_y\"], inplace=True)\n",
        "    submission_df.to_csv(\"submission.csv\", index=False)\n",
        "    print(\"Submission file saved successfully!\")\n",
        "\n",
        "\n",
        "# Define inputs and run pipeline\n",
        "train_file = \"/content/train.jsonl\"\n",
        "val_file = \"/content/validation.jsonl\"\n",
        "submission_file = \"/content/sample_submission.csv\"\n",
        "model_checkpoint = \"airesearch/wangchanberta-base-att-spm-uncased\"\n",
        "main_pipeline(train_file, val_file, model_checkpoint, submission_file)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "submission_df = pd.read_csv(\"submission.csv\")\n",
        "print(submission_df.head())\n",
        "print(submission_df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xTKCgg4U6Ydu",
        "outputId": "0f4ed899-f9cb-4aed-c29a-ea255d34d561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        id  ne_x  ne\n",
            "0  03795_0   0.0  31\n",
            "1  03795_1   0.0  31\n",
            "2  03795_2   1.0   1\n",
            "3  03795_3   6.0   1\n",
            "4  03795_4   6.0  30\n",
            "Index(['id', 'ne_x', 'ne'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "submission_df = pd.read_csv(\"sample_submission.csv\")\n",
        "print(submission_df.head())\n",
        "print(submission_df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4shLzS16k0Q",
        "outputId": "e462efb8-765a-4787-d974-499e66854ff3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        id   ne\n",
            "0  03795_0  0.0\n",
            "1  03795_1  0.0\n",
            "2  03795_2  1.0\n",
            "3  03795_3  6.0\n",
            "4  03795_4  6.0\n",
            "Index(['id', 'ne'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "submission_df = pd.read_csv(\"submission.csv\")\n",
        "\n",
        "# Drop the 'ne_x' column if it exists\n",
        "if 'ne_x' in submission_df.columns:\n",
        "    submission_df = submission_df.drop(columns=['ne_x'])\n",
        "\n",
        "# Debug: Check the structure of submission_df\n",
        "print(submission_df.head())\n",
        "print(submission_df.columns)\n",
        "sample_submission_df = pd.read_csv(\"sample_submission.csv\")\n",
        "\n",
        "# Check column names\n",
        "print(\"Sample Submission Columns:\", sample_submission_df.columns)\n",
        "print(\"Submission Columns:\", submission_df.columns)\n",
        "\n",
        "# Ensure columns match\n",
        "submission_df.columns = sample_submission_df.columns\n",
        "\n",
        "# Debug: Final check\n",
        "print(submission_df.head())\n",
        "print(\"Final Submission Columns:\", submission_df.columns)\n",
        "\n",
        "# Save the corrected submission file\n",
        "submission_df.to_csv(\"submission_corrected.csv\", index=False)\n",
        "print(\"Corrected submission file saved as 'submission_corrected.csv'\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dmZBkups6qc4",
        "outputId": "e8b7875b-8002-40a0-b03e-0bd914427ede"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        id  ne\n",
            "0  03795_0  31\n",
            "1  03795_1  31\n",
            "2  03795_2   1\n",
            "3  03795_3   1\n",
            "4  03795_4  30\n",
            "Index(['id', 'ne'], dtype='object')\n",
            "Sample Submission Columns: Index(['id', 'ne'], dtype='object')\n",
            "Submission Columns: Index(['id', 'ne'], dtype='object')\n",
            "        id  ne\n",
            "0  03795_0  31\n",
            "1  03795_1  31\n",
            "2  03795_2   1\n",
            "3  03795_3   1\n",
            "4  03795_4  30\n",
            "Final Submission Columns: Index(['id', 'ne'], dtype='object')\n",
            "Corrected submission file saved as 'submission_corrected.csv'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(sample_submission_df))\n",
        "print(len(submission_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2EkM3lQ7nEb",
        "outputId": "6699e236-82f6-4d5c-b9ed-70b6a404c25a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "213091\n",
            "213091\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission_df = pd.read_csv(\"submission_corrected.csv\")\n",
        "print(submission_df[\"ne\"].value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsZmAOYu70db",
        "outputId": "ab131706-f940-439d-ca3f-8e55600b709a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ne\n",
            "0     178051\n",
            "31     24645\n",
            "8       1001\n",
            "28       886\n",
            "23       773\n",
            "27       753\n",
            "7        717\n",
            "17       650\n",
            "18       623\n",
            "1        512\n",
            "24       491\n",
            "12       450\n",
            "4        434\n",
            "2        426\n",
            "3        405\n",
            "14       381\n",
            "13       364\n",
            "22       335\n",
            "10       295\n",
            "25       260\n",
            "6        250\n",
            "15       142\n",
            "9         64\n",
            "30        44\n",
            "26        40\n",
            "29        37\n",
            "19        21\n",
            "11        18\n",
            "16        15\n",
            "21         5\n",
            "20         3\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract unique labels from the dataset\n",
        "unique_labels = set()\n",
        "for example in dataset:\n",
        "    unique_labels.update(example[\"ner_tags\"])\n",
        "\n",
        "# Create a mapping of labels to integers\n",
        "label_mapping = {label: idx for idx, label in enumerate(sorted(unique_labels))}\n",
        "print(f\"Generated Label Mapping: {label_mapping}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-cE6_jWvCsjg",
        "outputId": "4dd22495-6d0e-40ac-808d-9c51aff863d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated Label Mapping: {'B_BRN': 0, 'B_DES': 1, 'B_DTM': 2, 'B_LOC': 3, 'B_MEA': 4, 'B_NAME': 5, 'B_NUM': 6, 'B_ORG': 7, 'B_PER': 8, 'B_TRM': 9, 'B_TTL': 10, 'E_BRN': 11, 'E_DES': 12, 'E_DTM': 13, 'E_LOC': 14, 'E_MEA': 15, 'E_NUM': 16, 'E_ORG': 17, 'E_PER': 18, 'E_TRM': 19, 'E_TTL': 20, 'I_BRN': 21, 'I_DES': 22, 'I_DTM': 23, 'I_LOC': 24, 'I_MEA': 25, 'I_NUM': 26, 'I_ORG': 27, 'I_PER': 28, 'I_TRM': 29, 'I_TTL': 30, 'O': 31}\n"
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