{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQb9oxEpD__u",
        "outputId": "c1ede41b-9b4e-404d-89b5-5cd54a09ff4e"
      },
      "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 seqeval\n",
            "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.5.0-cp310-cp310-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-py310-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-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16161 sha256=afa2bbe8de537a288b3a49eb2fc10cf628eee291cf3439f0ec573f10b46ff4ea\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
            "Successfully built seqeval\n",
            "Installing collected packages: xxhash, fsspec, dill, multiprocess, seqeval, 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 seqeval-1.2.2 xxhash-3.5.0\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.1+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.5)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.9.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (3.0.2)\n"
          ]
        }
      ],
      "source": [
        "! pip install datasets transformers seqeval\n",
        "! pip install torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import get_dataset_config_names, load_dataset\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "5papGhCZEdcK"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176,
          "referenced_widgets": [
            "dc71e5f09fa64de9bef26ac2f32ee448",
            "192c35eba2624c75ab2b83b54472c9a6",
            "1a89dfc6298b40429a50e8e5308e29b3",
            "c4896b22dc9340d9a421413e60e34dd1",
            "f61427bdbd5b464980164a517f32f1de",
            "75188f8b75284383a5874bd47069a744",
            "22b4e0ef1fac4236b6313deea21b9bfa",
            "b22ad2289bda4732bfca3938a580f9a6",
            "4b5bd654db5e4cf89a4bf77f548a3d7c",
            "c02a48fc5d0c42489cefad2986a99274",
            "925ab71d68f14deb8de95e54fabd5069"
          ]
        },
        "id": "tTcTQyhAD__v",
        "outputId": "69ae34fd-10be-4ccf-af64-da23ec232158"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/131k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc71e5f09fa64de9bef26ac2f32ee448"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "xtreme_subsets = get_dataset_config_names('xtreme')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cf-nlSZVD__w",
        "outputId": "b2ca261c-7b8c-418e-99fc-b78fe47ed786"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "183"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "len(xtreme_subsets)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "5ujfVES1D__x"
      },
      "outputs": [],
      "source": [
        "pan_subset = [s for s in xtreme_subsets if s.startswith(\"PAN\")]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pan_subset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fFcw_21FBnJ",
        "outputId": "81484665-bab8-4c47-ca69-39494cea95b0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['PAN-X.af',\n",
              " 'PAN-X.ar',\n",
              " 'PAN-X.bg',\n",
              " 'PAN-X.bn',\n",
              " 'PAN-X.de',\n",
              " 'PAN-X.el',\n",
              " 'PAN-X.en',\n",
              " 'PAN-X.es',\n",
              " 'PAN-X.et',\n",
              " 'PAN-X.eu',\n",
              " 'PAN-X.fa',\n",
              " 'PAN-X.fi',\n",
              " 'PAN-X.fr',\n",
              " 'PAN-X.he',\n",
              " 'PAN-X.hi',\n",
              " 'PAN-X.hu',\n",
              " 'PAN-X.id',\n",
              " 'PAN-X.it',\n",
              " 'PAN-X.ja',\n",
              " 'PAN-X.jv',\n",
              " 'PAN-X.ka',\n",
              " 'PAN-X.kk',\n",
              " 'PAN-X.ko',\n",
              " 'PAN-X.ml',\n",
              " 'PAN-X.mr',\n",
              " 'PAN-X.ms',\n",
              " 'PAN-X.my',\n",
              " 'PAN-X.nl',\n",
              " 'PAN-X.pt',\n",
              " 'PAN-X.ru',\n",
              " 'PAN-X.sw',\n",
              " 'PAN-X.ta',\n",
              " 'PAN-X.te',\n",
              " 'PAN-X.th',\n",
              " 'PAN-X.tl',\n",
              " 'PAN-X.tr',\n",
              " 'PAN-X.ur',\n",
              " 'PAN-X.vi',\n",
              " 'PAN-X.yo',\n",
              " 'PAN-X.zh']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXT8Y24LD__x",
        "outputId": "f91081d1-bff9-4c91-f953-6e480f083a40"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['PAN-X.af', 'PAN-X.ar', 'PAN-X.bg', 'PAN-X.bn', 'PAN-X.de']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "pan_subset[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcgPF0idD__x",
        "outputId": "fdaf2b85-dc57-4493-c6f5-20940c158772"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "import torch\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "device\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401,
          "referenced_widgets": [
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        "id": "e7i9Fdp5D__y",
        "outputId": "8dd34298-8169-4fc1-8cd9-58653e9fdb4a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train-00000-of-00001.parquet:   0%|          | 0.00/553k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "Generating train split:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "50e434b627724a13ad817aff3f6b7f77"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "96109b60a7304e7cb3609c48fa9043b1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from collections import defaultdict\n",
        "from datasets import DatasetDict\n",
        "\n",
        "langs = ['ta', 'hi', 'ur', 'ml']\n",
        "fracs = [0.629, 0.229, 0.084, 0.059]\n",
        "\n",
        "panx_ch = defaultdict(DatasetDict)\n",
        "\n",
        "for lang, frac in zip(langs, fracs):\n",
        "    ds = load_dataset(\"xtreme\", name=f\"PAN-X.{lang}\")\n",
        "\n",
        "    for split in ds:\n",
        "        panx_ch[lang][split] = (ds[split].shuffle(seed=0).select(range(int(frac * ds[split].num_rows))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "POfR-q76D__y",
        "outputId": "509465b4-68f9-4455-9384-88d34b225e3f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               ta    hi    ur   ml\n",
              "Number of Training Examples  9435  1145  1680  590"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-82414df0-d993-48d9-ba5f-7fcc2ef990c9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ta</th>\n",
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              "      <th>ur</th>\n",
              "      <th>ml</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Number of Training Examples</th>\n",
              "      <td>9435</td>\n",
              "      <td>1145</td>\n",
              "      <td>1680</td>\n",
              "      <td>590</td>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\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: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-82414df0-d993-48d9-ba5f-7fcc2ef990c9 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-82414df0-d993-48d9-ba5f-7fcc2ef990c9');\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",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"ta\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 9435,\n        \"max\": 9435,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          9435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1145,\n        \"max\": 1145,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1145\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ur\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1680,\n        \"max\": 1680,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1680\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ml\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 590,\n        \"max\": 590,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          590\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "pd.DataFrame({lang : panx_ch[lang][\"train\"].num_rows for lang in langs}, index = [\"Number of Training Examples\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ptzYSJgJD__y",
        "outputId": "16cbf487-887a-443f-b860-904b458849f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tokens ['வழிமாற்று', 'பெண்கள்', 'இடஒதுக்கீடு', 'வரைவுச்', 'சட்டம்']\n",
            "ner_tags [0, 3, 4, 4, 4]\n",
            "langs ['ta', 'ta', 'ta', 'ta', 'ta']\n"
          ]
        }
      ],
      "source": [
        "element = panx_ch[\"ta\"][\"train\"][0]\n",
        "for key, value in element.items():\n",
        "    print(f\"{key} {value}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p4VTCaIjD__y",
        "outputId": "53860934-c242-444d-d606-66d023f9038b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tokens Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)\n",
            "ner_tags Sequence(feature=ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], id=None), length=-1, id=None)\n",
            "langs Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)\n"
          ]
        }
      ],
      "source": [
        "for key, value in panx_ch[\"ta\"][\"train\"].features.items():\n",
        "    print(f\"{key} {value}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7gjDEnTD__z",
        "outputId": "a7c5b7e1-e1b6-4b14-ded2-1368206d800d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], id=None)\n"
          ]
        }
      ],
      "source": [
        "tags = panx_ch[\"ta\"][\"train\"].features[\"ner_tags\"].feature\n",
        "print(tags)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "6435d6c88b134a06b39a510591c83f1b",
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          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9435 [00:00<?, ? examples/s]"
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      "source": [
        "def create_tag_names(batch, tags):\n",
        "    return {\"ner_tags_str\": [tags.int2str(idx) for idx in batch[\"ner_tags\"]]}\n",
        "\n",
        "panx_de = panx_ch[\"ta\"].map(lambda x : create_tag_names(x, tags))"
      ]
    },
    {
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      "execution_count": 20,
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        {
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            "text/plain": [
              "                0        1            2        3       4\n",
              "Tokens  வழிமாற்று  பெண்கள்  இடஒதுக்கீடு  வரைவுச்  சட்டம்\n",
              "Tags            O    B-ORG        I-ORG    I-ORG   I-ORG"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u0bb5\\u0bb4\\u0bbf\\u0bae\\u0bbe\\u0bb1\\u0bcd\\u0bb1\\u0bc1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"B-ORG\",\n          \"\\u0baa\\u0bc6\\u0ba3\\u0bcd\\u0b95\\u0bb3\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"I-ORG\",\n          \"\\u0b87\\u0b9f\\u0b92\\u0ba4\\u0bc1\\u0b95\\u0bcd\\u0b95\\u0bc0\\u0b9f\\u0bc1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"I-ORG\",\n          \"\\u0bb5\\u0bb0\\u0bc8\\u0bb5\\u0bc1\\u0b9a\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"I-ORG\",\n          \"\\u0b9a\\u0b9f\\u0bcd\\u0b9f\\u0bae\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "de_example = panx_de[\"train\"][0]\n",
        "\n",
        "pd.DataFrame([de_example[\"tokens\"], de_example[\"ner_tags_str\"]], [\"Tokens\", \"Tags\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87,
          "referenced_widgets": [
            "f48694a508f34c97bdc502eb06379baf",
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        "id": "nWZchcPYD__z",
        "outputId": "30ab2f98-ea04-4f36-94f6-7dbfe2968b11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f48694a508f34c97bdc502eb06379baf"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch.nn as nn\n",
        "from transformers import XLMRobertaConfig\n",
        "from transformers.modeling_outputs import TokenClassifierOutput\n",
        "from transformers.models.roberta.modeling_roberta import RobertaModel\n",
        "from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "Ths9lrHSD__z"
      },
      "outputs": [],
      "source": [
        "class XLMRobertaForTokenClassification(RobertaPreTrainedModel):\n",
        "    config_class = XLMRobertaConfig\n",
        "\n",
        "    def __init__(self, config):\n",
        "        super().__init__(config)\n",
        "        self.num_labels = config.num_labels\n",
        "        self.roberta = RobertaModel(config, add_pooling_layer=False)\n",
        "        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n",
        "        self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n",
        "        self.init_weights()\n",
        "\n",
        "    def forward(self, input_ids = None, attention_mask = None, token_type_ids = None, num_items_in_batch= None, labels = None, **kwargs):\n",
        "        outputs = self.roberta(input_ids, attention_mask = attention_mask, token_type_ids = token_type_ids, **kwargs)\n",
        "        sequence_output = self.dropout(outputs[0])\n",
        "        logits = self.classifier(sequence_output)\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            loss_fct = nn.CrossEntropyLoss()\n",
        "            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "08wy1KKUD__z"
      },
      "outputs": [],
      "source": [
        "index2tag = {idx : tag for idx, tag in enumerate(tags.names)}\n",
        "tag2index = {tag: idx for idx, tag in enumerate(tags.names)}\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "cbe93307dfda4ed2920e36ab02a65c63",
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            "07c7e9a99bf4409e979c33648d897529",
            "714abc2d1c564ff7b3f6d5a450756919",
            "36a7707dda1a43a1a550d5eaa8c73197",
            "8982a2edf4a64cab99518c8502b98ace",
            "58c7b311b9ef4887a5af3d4b119ada54",
            "5838288941c4427ebd95d8c8043a4de1",
            "3c22960d5df94ad59575a676ee0da0a3",
            "861ca7a628b54457b1c62416f09a2b28",
            "a2ef143ca4dd45558a2e7af2dfe133cf"
          ]
        },
        "id": "Ad0ZFb92D__0",
        "outputId": "b0ea6c18-983a-41d1-b1c8-50e83f29b73d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/615 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cbe93307dfda4ed2920e36ab02a65c63"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoConfig\n",
        "\n",
        "xlmr_model_name = \"xlm-roberta-base\"\n",
        "xlmr_config = AutoConfig.from_pretrained(xlmr_model_name, num_labels = tags.num_classes,\n",
        "                                         id2label = index2tag, label2id=tag2index)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "a39c5442669747d68166033972e97c2b",
            "7ae6ffa3a09e455b8f102cf0f6d7d99e",
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            "0b2c2763a1b24e709a8d8e8374b4a556",
            "fa758410771b4d699aea835f142bd968",
            "eda662da15e44203866a9e5e42ac6c52",
            "c994927835bc43b286a735a74bd73032",
            "620b20dff76941889eefae479484780a",
            "4f92625eb7364583858577ee37b6a73c",
            "11b89f4483f649708e9cd1286e5cacc0",
            "13451101c2554090a357c9410228fb2e"
          ]
        },
        "id": "llBwUcGiD__0",
        "outputId": "db0244a1-edc0-4f49-859f-c4bfd3f620a0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a39c5442669747d68166033972e97c2b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of XLMRobertaForTokenClassification were not initialized from the model checkpoint at xlm-roberta-base 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"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "\n",
        "xlmr_model = (XLMRobertaForTokenClassification.from_pretrained(xlmr_model_name, config=xlmr_config).to(device))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "IjUn-jjsD__0"
      },
      "outputs": [],
      "source": [
        "def tag_text(text, tags, model, tokenizer):\n",
        "\n",
        "    tokens = tokenizer(text).tokens()\n",
        "\n",
        "    input_ids = tokenizer(text, return_tensors = \"pt\").input_ids.to(device)\n",
        "\n",
        "    outputs = model(input_ids)[0]\n",
        "\n",
        "    predictions = torch.argmax(outputs, axis = -1)\n",
        "\n",
        "    preds = [tags.names[p] for p in predictions[0].cpu().numpy()]\n",
        "\n",
        "    return pd.DataFrame([tokens, preds], index=[\"Tokens\", \"Tags\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "b1e6776709a84867bad9c7bb83f8ff61",
            "a8a27875d53a43bb9a6c66fcae1509f0",
            "9cc37c43d8ff405ab81ac974914c7bc6",
            "57fa7fd8025443548bc20a170e5b2247",
            "812af71c9e9e429699ec6f0346bf2513",
            "790e978d474a4f2985f92e7b593bf53f",
            "c54a4e84f247477eadf9875972cc1690",
            "fd9968c5ed53496ab66cbc30e1a8320a",
            "94c642ad5b124ad6968e69cae40f3122",
            "4bc577dceb0349b193eeabd7a984de3c",
            "7ea291880aa24dfd96b844349c8ec552",
            "8bd921a793524bb08097fc73b73b62d7",
            "d30ece1279104a40b6cc1c69bba491e4",
            "eb27a23cce344aa682c82419b855a396",
            "96beef8dad0442af96eab32696f44c09",
            "79eb8d851de14259bb61c4f3f737d8ac",
            "6b446a1cedc74a579279049993f91ca1",
            "71be956dbce647d0aca0a5b7452110a0",
            "e9928071c7674918a7034ba931910771",
            "86a0ed8ed8f245db93eb7c6d9841cf93",
            "7ad854233b844a768daaf761ca0e1007",
            "263f1163ed4945f7b050a34dd27faeae",
            "d085b4291a614ad78c898691cdf2e01d",
            "ecc977ca4e1847beb9bfb593336a0213",
            "fb31860aec184c6b935180df2c4d4015",
            "b47fd6381ecb4731ae165ff27530a802",
            "b9d82573e8ac4eceae798c2b72393312",
            "d44eb3655bd045468cbbc378720a1279",
            "deaf00543e5040c484033226123c826f",
            "4ddac990bedf45b1aa9196bcfacc0f2d",
            "29506cdc02684cefa5bfdba14cc40948",
            "579185c5c40543a685e3bcf1ead95bfb",
            "7633c470b87c44f6ac7084c5706c0ef1"
          ]
        },
        "id": "6-iLPD5QD__0",
        "outputId": "8934440d-648d-4721-daf8-f1811c388f51"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1e6776709a84867bad9c7bb83f8ff61"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bd921a793524bb08097fc73b73b62d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/9.10M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d085b4291a614ad78c898691cdf2e01d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "xlmr_tokenizer = AutoTokenizer.from_pretrained(xlmr_model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "y7iaCi3rD__0",
        "outputId": "718bbe7f-9acd-4667-f0c4-3ba3f4ebfe25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0      1      2        3      4      5      6      7      8   \\\n",
              "Tokens    <s>   ▁அரு     ண்  ▁இந்திய     ன்     ▁ஓ   யில்   கார்    ப்ப   \n",
              "Tags    I-PER  I-PER  I-PER    I-PER  I-PER  I-PER  I-PER  I-PER  I-PER   \n",
              "\n",
              "           9      10     11     12        13     14     15  \n",
              "Tokens     ரே      ஷ   னில்  ▁வேலை  ▁செய்கிற    ார்   </s>  \n",
              "Tags    I-PER  I-PER  I-PER  I-PER     I-PER  I-PER  I-PER  "
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              "      <th>Tokens</th>\n",
              "      <td>&lt;s&gt;</td>\n",
              "      <td>▁அரு</td>\n",
              "      <td>ண்</td>\n",
              "      <td>▁இந்திய</td>\n",
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              "      <td>I-PER</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "text = \"அருண் இந்தியன் ஓயில்கார்ப்பரேஷனில் வேலை செய்கிறார்\"\n",
        "tag_text(text, tags, xlmr_model, xlmr_tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "CJGGwuwLD__0"
      },
      "outputs": [],
      "source": [
        "words, labels = de_example[\"tokens\"], de_example[\"ner_tags\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
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        "id": "AaFHX2a5D__0",
        "outputId": "9f6aeaff-37e9-470b-ef86-b7d30de9900d"
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              "         0     1   2     3         4    5  6   7    8    9     10  11  12  \\\n",
              "Tokens  <s>  ▁வழி  மா  ற்று  ▁பெண்கள்  ▁இட  ஒ  து  க்க  ீடு  ▁வரை  வு  ச்   \n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "tokenized_input = xlmr_tokenizer(de_example[\"tokens\"], is_split_into_words=True)\n",
        "tokens = xlmr_tokenizer.convert_ids_to_tokens(tokenized_input[\"input_ids\"])\n",
        "pd.DataFrame([tokens], index = [\"Tokens\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
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        },
        "id": "E3fANqOGD__0",
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              "            0     1   2     3         4    5  6   7    8    9     10  11  12  \\\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1bd99123-9b9f-4a64-8ad8-cf777fa759e5 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-1bd99123-9b9f-4a64-8ad8-cf777fa759e5');\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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              "        element.appendChild(docLink);\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bcc24719-ecfe-4b80-b5a1-7ac7e68019c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"<s>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 5,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 6,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 7,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 8,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 9,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 10,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 11,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 12,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 13,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 14,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"</s>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "word_ids = tokenized_input.word_ids()\n",
        "pd.DataFrame([tokens, word_ids], index = [\"Tokens\", \"Word IDs\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "DPPr0WdoD__0",
        "outputId": "feca25e5-02cb-4fdb-ca7b-70382990dd44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0     1     2     3         4      5     6     7     8     9   \\\n",
              "Tokens      <s>  ▁வழி    மா  ற்று  ▁பெண்கள்    ▁இட     ஒ    து   க்க   ீடு   \n",
              "Word IDs   None     0     0     0         1      2     2     2     2     2   \n",
              "Label IDs  -100     0  -100  -100         3      4  -100  -100  -100  -100   \n",
              "Labels      IGN     O   IGN   IGN     B-ORG  I-ORG   IGN   IGN   IGN   IGN   \n",
              "\n",
              "              10    11    12       13    14  \n",
              "Tokens      ▁வரை    வு    ச்  ▁சட்டம்  </s>  \n",
              "Word IDs       3     3     3        4  None  \n",
              "Label IDs      4  -100  -100        4  -100  \n",
              "Labels     I-ORG   IGN   IGN    I-ORG   IGN  "
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
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              "      <th>1</th>\n",
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              "      <th>Tokens</th>\n",
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              "    <tr>\n",
              "      <th>Label IDs</th>\n",
              "      <td>-100</td>\n",
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              "    <tr>\n",
              "      <th>Labels</th>\n",
              "      <td>IGN</td>\n",
              "      <td>O</td>\n",
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              "      <td>IGN</td>\n",
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              "      <td>IGN</td>\n",
              "      <td>I-ORG</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1f18248b-9ea4-4e7b-b28a-2523cb0c1f3e 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-1f18248b-9ea4-4e7b-b28a-2523cb0c1f3e');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5892ed44-2dcc-4107-b29e-99136018df2b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5892ed44-2dcc-4107-b29e-99136018df2b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"<s>\",\n          -100,\n          \"IGN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u2581\\u0bb5\\u0bb4\\u0bbf\",\n          0,\n          \"O\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          \"IGN\",\n          \"\\u0bae\\u0bbe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          \"IGN\",\n          \"\\u0bb1\\u0bcd\\u0bb1\\u0bc1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          \"B-ORG\",\n          \"\\u2581\\u0baa\\u0bc6\\u0ba3\\u0bcd\\u0b95\\u0bb3\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 5,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"I-ORG\",\n          \"\\u2581\\u0b87\\u0b9f\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 6,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"IGN\",\n          \"\\u0b92\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 7,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"IGN\",\n          \"\\u0ba4\\u0bc1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 8,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"IGN\",\n          \"\\u0b95\\u0bcd\\u0b95\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 9,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"IGN\",\n          \"\\u0bc0\\u0b9f\\u0bc1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 10,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"I-ORG\",\n          \"\\u2581\\u0bb5\\u0bb0\\u0bc8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 11,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"IGN\",\n          \"\\u0bb5\\u0bc1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 12,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"IGN\",\n          \"\\u0b9a\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 13,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u2581\\u0b9a\\u0b9f\\u0bcd\\u0b9f\\u0bae\\u0bcd\",\n          4,\n          \"I-ORG\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 14,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"</s>\",\n          -100,\n          \"IGN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "previous_word_idx = None\n",
        "label_ids = []\n",
        "\n",
        "for word_idx in word_ids:\n",
        "    if word_idx is None or word_idx == previous_word_idx:\n",
        "        label_ids.append(-100)\n",
        "    elif word_idx != previous_word_idx:\n",
        "        label_ids.append(labels[word_idx])\n",
        "    previous_word_idx = word_idx\n",
        "\n",
        "labels = [index2tag[l] if l != -100 else \"IGN\" for l in label_ids]\n",
        "index = [\"Tokens\", \"Word IDs\", \"Label IDs\", \"Labels\"]\n",
        "\n",
        "pd.DataFrame([tokens, word_ids, label_ids, labels], index = index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "5ixTifQ4D__1"
      },
      "outputs": [],
      "source": [
        "def tokenize_and_align_labels(examples, xlmr_tokenizer):\n",
        "    tokenized_inputs = xlmr_tokenizer(examples[\"tokens\"], truncation=True,\n",
        "                                      is_split_into_words=True)\n",
        "    labels = []\n",
        "    for idx, label in enumerate(examples[\"ner_tags\"]):\n",
        "        word_ids = tokenized_inputs.word_ids(batch_index=idx)\n",
        "        previous_word_idx = None\n",
        "        label_ids = []\n",
        "        for word_idx in word_ids:\n",
        "            if word_idx is None or word_idx == previous_word_idx:\n",
        "                label_ids.append(-100)\n",
        "            else:\n",
        "                label_ids.append(label[word_idx])\n",
        "            previous_word_idx = word_idx\n",
        "        labels.append(label_ids)\n",
        "    tokenized_inputs[\"labels\"] = labels\n",
        "    return tokenized_inputs\n",
        "\n",
        "def encode_panx_dataset(corpus, xlmr_tokenizer):\n",
        "    return corpus.map(\n",
        "        lambda x: tokenize_and_align_labels(x, xlmr_tokenizer),\n",
        "        batched=True,\n",
        "        remove_columns=['langs', 'ner_tags', 'tokens']\n",
        "    )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "a04c01b17fc440c98515304a546299fd",
            "5d646870e4cf4eac8d7bab607d4594c1",
            "bf42f08dc47b4c6ca4a360f1cecef2dc",
            "cf3b9db60e1d4d5093d6333bc726382c",
            "a7efef5a88b9448d9996e25468eb3698",
            "6502889a5e744c2e8c471cc0ab38a5ea",
            "d3652e9f1edf4146aaa156d929a29da5",
            "eb71b61b10c74f7385f5cd4da1522c66",
            "c4603dbe52344a1a8b492c8fbb224cae",
            "c9d0eb9c905e413fbd079e64b7b05ca3",
            "101b4644f4da469f96874b14d4c630cb"
          ]
        },
        "id": "_rYuClUOD__1",
        "outputId": "bc4e7d32-0060-42df-d3d4-e43a90c6d9ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9435 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a04c01b17fc440c98515304a546299fd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "panx_ta_encoded = encode_panx_dataset(panx_ch[\"ta\"], xlmr_tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJ2bQDufD__1",
        "outputId": "28e25bb5-0414-48ca-c204-d7751ddc25fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [0,\n",
              "  27906,\n",
              "  6161,\n",
              "  35867,\n",
              "  87417,\n",
              "  37901,\n",
              "  183177,\n",
              "  2802,\n",
              "  2798,\n",
              "  105723,\n",
              "  34155,\n",
              "  7967,\n",
              "  6149,\n",
              "  196247,\n",
              "  2],\n",
              " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              " 'labels': [-100,\n",
              "  0,\n",
              "  -100,\n",
              "  -100,\n",
              "  3,\n",
              "  4,\n",
              "  -100,\n",
              "  -100,\n",
              "  -100,\n",
              "  -100,\n",
              "  4,\n",
              "  -100,\n",
              "  -100,\n",
              "  4,\n",
              "  -100]}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "panx_ta_encoded[\"train\"][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "kecM4C33D__1"
      },
      "outputs": [],
      "source": [
        "from seqeval.metrics import classification_report\n",
        "import numpy as np\n",
        "\n",
        "def align_predictions(predictions, label_ids):\n",
        "    preds = np.argmax(predictions, axis = 2)\n",
        "    batch_size, seq_len = preds.shape\n",
        "    labels_list, preds_list = [], []\n",
        "\n",
        "    for batch_idx in range(batch_size):\n",
        "        example_labels, example_preds = [], []\n",
        "        for seq_idx in range(seq_len):\n",
        "            if label_ids[batch_idx, seq_idx] != -100:\n",
        "                example_labels.append(index2tag[label_ids[batch_idx][seq_idx]])\n",
        "                example_preds.append(index2tag[preds[batch_idx][seq_idx]])\n",
        "        labels_list.append(example_labels)\n",
        "        preds_list.append(example_preds)\n",
        "\n",
        "    return preds_list, labels_list\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TDke1eyZD__1",
        "outputId": "dcf265db-d693-4934-c803-722f050a9dc0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (1.2.1)\n",
            "Requirement already satisfied: numpy<3.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.2)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.5.1+cu121)\n",
            "Requirement already satisfied: huggingface-hub>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.27.1)\n",
            "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.5.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (3.16.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (2024.9.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (4.67.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.0->accelerate) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.5)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (3.0.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.21.0->accelerate) (2024.12.14)\n"
          ]
        }
      ],
      "source": [
        "! pip install -U accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J3CZ7di3D__1",
        "outputId": "35da70c6-e916-42ec-9892-fc9c73abd36a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1575: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "num_epochs = 3\n",
        "batch_size = 24\n",
        "\n",
        "logging_steps = len(panx_ta_encoded[\"train\"]) // batch_size\n",
        "model_name = f\"{xlmr_model_name}-finetuned-panx-tamil\"\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=model_name, log_level=\"error\", num_train_epochs = num_epochs,\n",
        "    per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_steps=1e6, weight_decay=0.01, disable_tqdm=False, logging_steps=logging_steps\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "8PfjWRPTD__1"
      },
      "outputs": [],
      "source": [
        "from seqeval.metrics import f1_score\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    y_pred, y_true = align_predictions(eval_pred.predictions, eval_pred.label_ids)\n",
        "    return {\"f1\" : f1_score(y_true, y_pred)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "7APsCoLxD__2"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForTokenClassification\n",
        "\n",
        "data_collator = DataCollatorForTokenClassification(xlmr_tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2pHZ-0GD__2",
        "outputId": "08fd5eff-4c4a-49ed-8bdb-4f6a2295d51e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-48-cd9a10521d20>:3: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
            "  trainer = Trainer(model=xlmr_model, args=training_args, data_collator=data_collator, compute_metrics=compute_metrics,\n"
          ]
        }
      ],
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(model=xlmr_model, args=training_args, data_collator=data_collator, compute_metrics=compute_metrics,\n",
        "                  train_dataset=panx_ta_encoded[\"train\"],eval_dataset=panx_ta_encoded[\"validation\"], tokenizer = xlmr_tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "w2fgOBbUD__2",
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
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            " ··········\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
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              "Syncing run <strong><a href='https://wandb.ai/waranlokesh84-zoho/huggingface/runs/76tcrg1i' target=\"_blank\">xlm-roberta-base-finetuned-panx-tamil</a></strong> to <a href='https://wandb.ai/waranlokesh84-zoho/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
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              " View run at <a href='https://wandb.ai/waranlokesh84-zoho/huggingface/runs/76tcrg1i' target=\"_blank\">https://wandb.ai/waranlokesh84-zoho/huggingface/runs/76tcrg1i</a>"
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1182' max='1182' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1182/1182 06:25, Epoch 3/3]\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</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.414100</td>\n",
              "      <td>0.261846</td>\n",
              "      <td>0.779618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.206400</td>\n",
              "      <td>0.182687</td>\n",
              "      <td>0.824427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.134400</td>\n",
              "      <td>0.192282</td>\n",
              "      <td>0.845960</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1182, training_loss=0.2513707867884999, metrics={'train_runtime': 425.4821, 'train_samples_per_second': 66.525, 'train_steps_per_second': 2.778, 'total_flos': 628800282287496.0, 'train_loss': 0.2513707867884999, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7ANaof0D__2",
        "outputId": "3c7d7f72-9a9f-44ab-dcd9-3bd36d1266b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<transformers.trainer.Trainer at 0x7e4d7b7c0b50>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "trainer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.nn.functional import cross_entropy\n",
        "\n",
        "def forward_pass_with_labels(batch):\n",
        "  features = [dict(zip(batch, t)) for t in zip(*batch.values())]\n",
        "\n",
        "  batch = data_collator(features)\n",
        "  input_ids = batch[\"input_ids\"].to(device)\n",
        "  attention_mask = batch[\"attention_mask\"].to(device)\n",
        "  labels = batch[\"labels\"].to(device)\n",
        "  with torch.no_grad():\n",
        "    output = trainer.model(input_ids, attention_mask)\n",
        "    predicted_label = torch.argmax(output.logits, axis = -1).cpu().numpy()\n",
        "\n",
        "  loss = cross_entropy(output.logits.view(-1, 7), labels.view(-1), reduction = \"none\")\n",
        "  loss = loss.view(len(input_ids), -1).cpu().numpy()\n",
        "\n",
        "  return {\"loss\": loss, \"predicted_label\": predicted_label}\n"
      ],
      "metadata": {
        "id": "vcdgB0YUQVF-"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "valid_set = panx_ta_encoded[\"validation\"]\n",
        "valid_set_pred = valid_set.map(forward_pass_with_labels, batched = True, batch_size = 32)\n",
        "df = valid_set_pred.to_pandas()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "877f87f91a174b54a3a19609818b8225",
            "1e2748e2a2f84514960c5f1db24a31c3",
            "3ff54a314ce34ff19ceeae05298bf26d",
            "57a404fa818d470aa4ba895247265631",
            "0bdb976cc27347698dd27bbfb1b80483",
            "144c06c9a789409689da51730fbe0e31",
            "b4b8848aacd840f7b3c0a6419d6d4b73",
            "18a114afcd674a99b1a170bf2899fcd2",
            "367a624b7a8e438bbddba95a1a73f926",
            "c4c778eb93c64a95b3f6b247b54ad0c8",
            "4339b73276334a24b9950c8785c7ced3"
          ]
        },
        "id": "2P-gNqZaR_eR",
        "outputId": "f96cbd51-f21b-4610-ae4d-c806f54ee906"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/629 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "877f87f91a174b54a3a19609818b8225"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "GfKnrd4wTixz",
        "outputId": "d0d296b9-2f2b-4746-8c02-99be546d90cf"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           input_ids  \\\n",
              "0  [0, 6, 187871, 111224, 4575, 6, 6016, 17553, 5...   \n",
              "1  [0, 7216, 3937, 35867, 20539, 361, 7963, 2802,...   \n",
              "2  [0, 27906, 6161, 35867, 42143, 5, 37255, 5, 13...   \n",
              "3    [0, 242, 5106, 7963, 40983, 9313, 5106, 242, 2]   \n",
              "4  [0, 12928, 7827, 20663, 13945, 27906, 3770, 70...   \n",
              "\n",
              "                                      attention_mask  \\\n",
              "0      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "1  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "2               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "3                        [1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
              "4  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "\n",
              "                                              labels  \\\n",
              "0  [-100, 1, -100, -100, -100, 2, -100, -100, -10...   \n",
              "1  [-100, 0, -100, -100, -100, 0, 3, -100, -100, ...   \n",
              "2  [-100, 0, -100, -100, 1, -100, 2, -100, 2, -10...   \n",
              "3            [-100, 0, 0, 5, -100, -100, 0, 0, -100]   \n",
              "4  [-100, 3, -100, -100, -100, 4, -100, -100, -10...   \n",
              "\n",
              "                                                loss  \\\n",
              "0  [0.0, 0.0036092158, 0.0, 0.0, 0.0, 0.003991138...   \n",
              "1  [0.0, 0.0002467328, 0.0, 0.0, 0.0, 0.001535309...   \n",
              "2  [0.0, 0.00028224775, 0.0, 0.0, 0.0019426538, 0...   \n",
              "3  [0.0, 0.0011607822, 0.0007962394, 0.038503557,...   \n",
              "4  [0.0, 0.012848098, 0.0, 0.0, 0.0, 0.016217574,...   \n",
              "\n",
              "                                     predicted_label  \n",
              "0  [2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...  \n",
              "1  [0, 0, 0, 0, 0, 0, 3, 3, 3, 4, 4, 4, 0, 0, 0, ...  \n",
              "2  [1, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, ...  \n",
              "3  [5, 0, 0, 5, 5, 6, 0, 0, 5, 5, 5, 5, 5, 5, 5, ...  \n",
              "4  [0, 3, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, ...  "
            ],
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              "      <th></th>\n",
              "      <th>input_ids</th>\n",
              "      <th>attention_mask</th>\n",
              "      <th>labels</th>\n",
              "      <th>loss</th>\n",
              "      <th>predicted_label</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[0, 6, 187871, 111224, 4575, 6, 6016, 17553, 5...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[-100, 1, -100, -100, -100, 2, -100, -100, -10...</td>\n",
              "      <td>[0.0, 0.0036092158, 0.0, 0.0, 0.0, 0.003991138...</td>\n",
              "      <td>[2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[0, 7216, 3937, 35867, 20539, 361, 7963, 2802,...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[-100, 0, -100, -100, -100, 0, 3, -100, -100, ...</td>\n",
              "      <td>[0.0, 0.0002467328, 0.0, 0.0, 0.0, 0.001535309...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 3, 3, 3, 4, 4, 4, 0, 0, 0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[0, 27906, 6161, 35867, 42143, 5, 37255, 5, 13...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[-100, 0, -100, -100, 1, -100, 2, -100, 2, -10...</td>\n",
              "      <td>[0.0, 0.00028224775, 0.0, 0.0, 0.0019426538, 0...</td>\n",
              "      <td>[1, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[0, 242, 5106, 7963, 40983, 9313, 5106, 242, 2]</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
              "      <td>[-100, 0, 0, 5, -100, -100, 0, 0, -100]</td>\n",
              "      <td>[0.0, 0.0011607822, 0.0007962394, 0.038503557,...</td>\n",
              "      <td>[5, 0, 0, 5, 5, 6, 0, 0, 5, 5, 5, 5, 5, 5, 5, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[0, 12928, 7827, 20663, 13945, 27906, 3770, 70...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[-100, 3, -100, -100, -100, 4, -100, -100, -10...</td>\n",
              "      <td>[0.0, 0.012848098, 0.0, 0.0, 0.0, 0.016217574,...</td>\n",
              "      <td>[0, 3, 3, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "<div id=\"df-26f456c8-a1b1-4de8-82cb-954f85069a87\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26f456c8-a1b1-4de8-82cb-954f85069a87')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-26f456c8-a1b1-4de8-82cb-954f85069a87 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 629,\n  \"fields\": [\n    {\n      \"column\": \"input_ids\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attention_mask\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"labels\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loss\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_label\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index2tag[-100] = \"IGN\"\n",
        "df[\"input_tokens\"] = df[\"input_ids\"].apply(lambda x : xlmr_tokenizer.convert_ids_to_tokens(x))\n",
        "df[\"predicted_label\"] = df[\"predicted_label\"].apply(lambda x : [index2tag[i] for i in x])\n",
        "df[\"labels\"] = df[\"labels\"].apply(lambda x : [index2tag[i] for i in x])\n",
        "df['loss'] = df.apply(lambda x : x['loss'][:len(x['input_ids'])], axis = 1)\n",
        "df['predicted_label'] = df.apply(lambda x : x['predicted_label'][:len(x['input_ids'])], axis = 1)"
      ],
      "metadata": {
        "id": "VXBfwtxIUi09"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_tokens = df.apply(pd.Series.explode)\n",
        "df_tokens = df_tokens.query(\"labels != 'IGN'\")\n",
        "df_tokens['loss'] = df_tokens['loss'].astype(float).round(2)"
      ],
      "metadata": {
        "id": "vcqrfIwESc5j"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_confusion_matrix(y_preds, y_true, labels):\n",
        "  cm = confusion_matrix(y_true, y_preds, normalize = \"true\")\n",
        "  fig, ax = plt.subplots(figsize = (6, 6))\n",
        "  disp = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = labels)\n",
        "  disp.plot(cmap = plt.cm.Blues, values_format = \".2f\", ax = ax, colorbar = False)\n",
        "  plt.title(\"Normalized confusion matrix\")\n",
        "  plt.show()\n"
      ],
      "metadata": {
        "id": "sDDQJYsML9Mf"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(df_tokens[\"predicted_label\"], df_tokens[\"labels\"], tags.names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Z1qearweOBue",
        "outputId": "f60f5cdf-7419-41ce-8332-fd696483645e"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_f1_score(trainer, dataset):\n",
        "  return trainer.predict(dataset).metrics[\"test_f1\"]\n",
        "\n",
        "def evaluate_lang_performance(lang, trainer):\n",
        "  panx_ds = encode_panx_dataset(panx_ch[lang], xlmr_tokenizer)\n",
        "  return get_f1_score(trainer, panx_ds[\"test\"])"
      ],
      "metadata": {
        "id": "-KmGgxwYOBxW"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1_scores = defaultdict(dict)\n",
        "f1_scores[\"ta\"][\"ta\"] = get_f1_score(trainer, panx_ta_encoded[\"test\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "xD2fBfklJoeF",
        "outputId": "7fdaa374-8b57-41bc-f77a-1ea585f561a2"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f1_scores[\"ta\"][\"ta\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xL_brsMaKHnZ",
        "outputId": "379c6050-4e4b-4534-fd5d-fbba840080df"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8200890019071838"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"அருண் இந்தியன் ஓயில்கார்ப்பரேஷனில் வேலை செய்கிறார்\"\n",
        "tag_text(text, tags, xlmr_model, xlmr_tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "o2GsE4qCKV0U",
        "outputId": "05c0a06f-2838-4feb-9d6c-0e5019263325"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         0      1      2        3      4   5     6     7    8   9  10    11  \\\n",
              "Tokens  <s>   ▁அரு     ண்  ▁இந்திய     ன்  ▁ஓ  யில்  கார்  ப்ப  ரே  ஷ  னில்   \n",
              "Tags      O  B-PER  B-PER    I-PER  I-PER   O     O     O    O   O  O     O   \n",
              "\n",
              "           12        13   14    15  \n",
              "Tokens  ▁வேலை  ▁செய்கிற  ார்  </s>  \n",
              "Tags        O         O    O     O  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e96f920-eb06-45d1-8b22-4c4db4346caf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Tokens</th>\n",
              "      <td>&lt;s&gt;</td>\n",
              "      <td>▁அரு</td>\n",
              "      <td>ண்</td>\n",
              "      <td>▁இந்திய</td>\n",
              "      <td>ன்</td>\n",
              "      <td>▁ஓ</td>\n",
              "      <td>யில்</td>\n",
              "      <td>கார்</td>\n",
              "      <td>ப்ப</td>\n",
              "      <td>ரே</td>\n",
              "      <td>ஷ</td>\n",
              "      <td>னில்</td>\n",
              "      <td>▁வேலை</td>\n",
              "      <td>▁செய்கிற</td>\n",
              "      <td>ார்</td>\n",
              "      <td>&lt;/s&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tags</th>\n",
              "      <td>O</td>\n",
              "      <td>B-PER</td>\n",
              "      <td>B-PER</td>\n",
              "      <td>I-PER</td>\n",
              "      <td>I-PER</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e96f920-eb06-45d1-8b22-4c4db4346caf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1e96f920-eb06-45d1-8b22-4c4db4346caf 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-1e96f920-eb06-45d1-8b22-4c4db4346caf');\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",
              "\n",
              "\n",
              "<div id=\"df-c39b529e-cad2-42a3-8e1f-bd4d5c3be6bc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c39b529e-cad2-42a3-8e1f-bd4d5c3be6bc')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c39b529e-cad2-42a3-8e1f-bd4d5c3be6bc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"tag_text(text, tags, xlmr_model, xlmr_tokenizer)\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"<s>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"B-PER\",\n          \"\\u2581\\u0b85\\u0bb0\\u0bc1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"B-PER\",\n          \"\\u0ba3\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"I-PER\",\n          \"\\u2581\\u0b87\\u0ba8\\u0bcd\\u0ba4\\u0bbf\\u0baf\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"I-PER\",\n          \"\\u0ba9\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 5,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u2581\\u0b93\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 6,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u0baf\\u0bbf\\u0bb2\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 7,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u0b95\\u0bbe\\u0bb0\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 8,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u0baa\\u0bcd\\u0baa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 9,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u0bb0\\u0bc7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 10,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u0bb7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 11,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u0ba9\\u0bbf\\u0bb2\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 12,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u2581\\u0bb5\\u0bc7\\u0bb2\\u0bc8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 13,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u2581\\u0b9a\\u0bc6\\u0baf\\u0bcd\\u0b95\\u0bbf\\u0bb1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 14,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"\\u0bbe\\u0bb0\\u0bcd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 15,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"O\",\n          \"</s>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f1_scores[\"ta\"][\"hi\"] = evaluate_lang_performance(\"hi\", trainer)"
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      "execution_count": 69,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1145 [00:00<?, ? examples/s]"
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        {
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              "Map:   0%|          | 0/229 [00:00<?, ? examples/s]"
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      "cell_type": "code",
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        "id": "DpqqI15DLcPT",
        "outputId": "cbd34fe7-b14c-480f-8380-4dd24539ecfb"
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      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "0.6839378238341969"
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          "metadata": {},
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        "panx_hi_encoded = encode_panx_dataset(panx_ch[\"hi\"], xlmr_tokenizer)"
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            "897e9f0f08d547adac7f17171a637045"
          ]
        },
        "id": "ebdKUyFiL4CE",
        "outputId": "20e3c863-8fc1-453b-d75d-27ad808bda28"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1145 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26252462bb4e4354bd7938cc3389ff73"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "def concatenate_splits(corpora):\n",
        "  multi_corpus = DatasetDict()\n",
        "  for split in corpora[0].keys():\n",
        "    multi_corpus[split] = concatenate_datasets([corpus[split] for corpus in corpora]).shuffle(seed = 0)\n",
        "  return multi_corpus\n",
        "\n",
        "panx_ta_hi_encoded = concatenate_splits([panx_ta_encoded, panx_hi_encoded])"
      ],
      "metadata": {
        "id": "-Bj6DCFdLsAA"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args.logging_steps = len(panx_ta_hi_encoded[\"train\"]) // batch_size\n",
        "training_args.push_to_hub = True\n",
        "training_args.output_dir = \"xlm-roberta-base-fintuned-panx-tamil-hindi\""
      ],
      "metadata": {
        "id": "cPFWrj1KMMow"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "login(\"XXXXXXXXX\") # hugging face api key"
      ],
      "metadata": {
        "id": "gLVKi1HbN5-_"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(model=xlmr_model, args = training_args, data_collator=data_collator,\n",
        "                  compute_metrics = compute_metrics, tokenizer = xlmr_tokenizer, train_dataset = panx_ta_hi_encoded[\"train\"],\n",
        "                  eval_dataset = panx_ta_hi_encoded[\"validation\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XMvMKwnTMkYA",
        "outputId": "98d9d5dc-f4b7-47be-c3af-2570a959f398"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-81-4db166850b85>:1: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
            "  trainer = Trainer(model=xlmr_model, args = training_args, data_collator=data_collator,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "2l169qtMOTTD",
        "outputId": "d07b04de-92f4-4f57-d908-e4c63567341e"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1323' max='1323' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1323/1323 07:17, Epoch 3/3]\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</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.188600</td>\n",
              "      <td>0.241305</td>\n",
              "      <td>0.809634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.125200</td>\n",
              "      <td>0.241477</td>\n",
              "      <td>0.820084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.075200</td>\n",
              "      <td>0.247983</td>\n",
              "      <td>0.834734</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1323, training_loss=0.12965581494574283, metrics={'train_runtime': 438.1925, 'train_samples_per_second': 72.434, 'train_steps_per_second': 3.019, 'total_flos': 693647678231376.0, 'train_loss': 0.12965581494574283, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(commit_message = \"Training complete for Tamil and Hindi language\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "Qg5JUFx5P2J0",
        "outputId": "6247592f-07db-471e-d7de-c33795a9e030"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Lokeshwaran/xlm-roberta-base-fintuned-panx-ta-hi/commit/e16bf7ef2d68f0bf1f454479ab64277d3df6d2b3', commit_message='Training complete for Tamil and Hindi language', commit_description='', oid='e16bf7ef2d68f0bf1f454479ab64277d3df6d2b3', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Lokeshwaran/xlm-roberta-base-fintuned-panx-ta-hi', endpoint='https://huggingface.co', repo_type='model', repo_id='Lokeshwaran/xlm-roberta-base-fintuned-panx-ta-hi'), pr_revision=None, pr_num=None)"
            ],
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              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for lang in langs:\n",
        "  f1 = evaluate_lang_performance(lang, trainer)\n",
        "  f1_scores[\"ta\"][lang] = f1\n",
        "  print(f\"F1 score for {lang} is {f1}\")"
      ],
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          "text": [
            "F1 score for ta is 0.8349267049075845\n"
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            "F1 score for hi is 0.8515205724508049\n"
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              "Map:   0%|          | 0/1680 [00:00<?, ? examples/s]"
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