HF_Dataset.ipynb

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  "cells": [
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      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
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        },
        "id": "6EXdJDmIxHxN",
        "outputId": "fe842555-7732-4e17-f316-74be651cbb94"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-3.0.1-py3-none-any.whl.metadata (20 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (16.1.0)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n",
            "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.5)\n",
            "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 (from datasets)\n",
            "  Downloading multiprocess-0.70.17-py310-none-any.whl.metadata (7.2 kB)\n",
            "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.8)\n",
            "Requirement already satisfied: huggingface-hub>=0.22.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.24.7)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.13.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.22.0->datasets) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.8.30)\n",
            "INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Downloading datasets-3.0.1-py3-none-any.whl (471 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m471.6/471.6 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-3.0.1 dill-0.3.8 multiprocess-0.70.16 xxhash-3.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "PDnA1aMpvVQ3"
      },
      "outputs": [],
      "source": [
        "from datasets import get_dataset_config_names, get_dataset_split_names, load_dataset, load_dataset_builder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_dataset_config_names(\"ai4bharat/naamapadam\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6xcYcxovZzG",
        "outputId": "17262ec0-2f1d-4240-8f71-5c03d219a607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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": "execute_result",
          "data": {
            "text/plain": [
              "['as', 'bn', 'gu', 'hi', 'kn', 'ml', 'mr', 'or', 'pa', 'ta', 'te']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_builder = load_dataset_builder(\"ai4bharat/naamapadam\", \"hi\")\n",
        "\n",
        "ds_builder.info.description\n",
        "\n",
        "ds_builder.info.features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yb7WKoMxxQ_Z",
        "outputId": "9f049957-fbb5-41f4-d31e-2d3f8f85598e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'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)}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds = load_dataset(\"ai4bharat/naamapadam\", \"ta\")"
      ],
      "metadata": {
        "id": "RL5e7Y0yyjdp"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dXEs3DUy18h",
        "outputId": "40d3c983-3b1c-4173-feb0-3778ed64f8b0"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'ner_tags'],\n",
              "        num_rows: 497882\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'ner_tags'],\n",
              "        num_rows: 758\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['tokens', 'ner_tags'],\n",
              "        num_rows: 2795\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Q2"
      ],
      "metadata": {
        "id": "elrbuKP-0nix"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ds.cache_files"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EH6vF4-_y3dX",
        "outputId": "eedc18a1-4b0d-4849-a5cb-212f4daf704d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'train': [{'filename': '/root/.cache/huggingface/datasets/ai4bharat___naamapadam/ta/1.0.0/9d4f21ac57d11ed4f9ea64854fdc9f5618e61acc/naamapadam-train.arrow'}],\n",
              " 'test': [{'filename': '/root/.cache/huggingface/datasets/ai4bharat___naamapadam/ta/1.0.0/9d4f21ac57d11ed4f9ea64854fdc9f5618e61acc/naamapadam-test.arrow'}],\n",
              " 'validation': [{'filename': '/root/.cache/huggingface/datasets/ai4bharat___naamapadam/ta/1.0.0/9d4f21ac57d11ed4f9ea64854fdc9f5618e61acc/naamapadam-validation.arrow'}]}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Q3"
      ],
      "metadata": {
        "id": "TiOoj5PW0qSR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dir(ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CGruNLF_0Djx",
        "outputId": "baa09e4e-3266-4bc5-8a66-bac14a14d815"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__class__',\n",
              " '__class_getitem__',\n",
              " '__contains__',\n",
              " '__delattr__',\n",
              " '__delitem__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__enter__',\n",
              " '__eq__',\n",
              " '__exit__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__getitem__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__ior__',\n",
              " '__iter__',\n",
              " '__le__',\n",
              " '__len__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__or__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__reversed__',\n",
              " '__ror__',\n",
              " '__setattr__',\n",
              " '__setitem__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_check_values_features',\n",
              " '_check_values_type',\n",
              " 'align_labels_with_mapping',\n",
              " 'cache_files',\n",
              " 'cast',\n",
              " 'cast_column',\n",
              " 'class_encode_column',\n",
              " 'cleanup_cache_files',\n",
              " 'clear',\n",
              " 'column_names',\n",
              " 'copy',\n",
              " 'data',\n",
              " 'filter',\n",
              " 'flatten',\n",
              " 'flatten_indices',\n",
              " 'formatted_as',\n",
              " 'from_csv',\n",
              " 'from_json',\n",
              " 'from_parquet',\n",
              " 'from_text',\n",
              " 'fromkeys',\n",
              " 'get',\n",
              " 'items',\n",
              " 'keys',\n",
              " 'load_from_disk',\n",
              " 'map',\n",
              " 'num_columns',\n",
              " 'num_rows',\n",
              " 'pop',\n",
              " 'popitem',\n",
              " 'push_to_hub',\n",
              " 'remove_columns',\n",
              " 'rename_column',\n",
              " 'rename_columns',\n",
              " 'reset_format',\n",
              " 'save_to_disk',\n",
              " 'select_columns',\n",
              " 'set_format',\n",
              " 'set_transform',\n",
              " 'setdefault',\n",
              " 'shape',\n",
              " 'shuffle',\n",
              " 'sort',\n",
              " 'unique',\n",
              " 'update',\n",
              " 'values',\n",
              " 'with_format',\n",
              " 'with_transform']"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Q3"
      ],
      "metadata": {
        "id": "sLlmQhlu35SG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Edit this"
      ],
      "metadata": {
        "id": "Bet0EWvuuJdW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def check_size(ds):\n",
        "  size = 0\n",
        "  for split in ds.keys():\n",
        "    size += ds[split].info.size_in_bytes\n",
        "\n",
        "  memory = size / (1024**2)\n",
        "  print(f\"Size in memory: {memory:.2f} MB\")\n",
        "check_size(ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPLNuENJ0_am",
        "outputId": "2bd127e2-0674-4126-ff29-bf768063aaec"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size in memory: 678.65 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def check_size(ds):\n",
        "  size = 0\n",
        "  for split in ds.keys():\n",
        "    size += ds[split].dataset_size\n",
        "\n",
        "  memory = size / (1024**2)\n",
        "  print(f\"Size in memory: {memory:.2f} MB\")\n",
        "check_size(ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqYJqUbRP68p",
        "outputId": "b03e6341-ca83-4e87-b715-57bad6ad814a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size in memory: 539.54 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds['train']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3HtzjZX32ehe",
        "outputId": "c04b9cae-7464-41bf-f4da-4183858b820b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['tokens', 'ner_tags'],\n",
              "    num_rows: 497882\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Q5"
      ],
      "metadata": {
        "id": "PHZtemXY6mYc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_len_feature(df):\n",
        "    df[\"num tokens\"] = len(df[\"tokens\"])\n",
        "    return df\n",
        "\n",
        "tok_sum = 0\n",
        "new_ds = {}  # Initialize a dictionary to store the modified datasets\n",
        "\n",
        "for split in ds.keys():\n",
        "    ds_cur = ds[split].map(add_len_feature)\n",
        "    temp = sum(ds_cur[\"num tokens\"])\n",
        "    tok_sum += temp\n",
        "    new_ds[split] = ds_cur  # Store the modified dataset in the new variable\n",
        "    print(f\"Current--> {split}: {temp}\")\n",
        "\n",
        "print(f\"Total tokens in {split}: {tok_sum}\")\n",
        "\n",
        "# Convert the dictionary into a DatasetDict\n",
        "from datasets import DatasetDict\n",
        "new_ds = DatasetDict(new_ds)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYrsWz5K4FSF",
        "outputId": "4f03c9e5-7b37-4bfb-d1e3-c3aa3840650b"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current--> train: 5959032\n",
            "Current--> test: 9528\n",
            "Current--> validation: 33316\n",
            "Total tokens in validation: 6001876\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Q6"
      ],
      "metadata": {
        "id": "rBrNbE_X6uyu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "check_size(new_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cM083Nk75iXD",
        "outputId": "21903579-b73f-4bb7-bd56-b2332608a32f"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size in memory: 539.54 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Q7"
      ],
      "metadata": {
        "id": "NIDBM8uV9_jB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def join_tokens(ds):\n",
        "  ds[\"text\"] = \" \".join(ds[\"tokens\"])\n",
        "  return ds\n",
        "\n",
        "ds_join = {}\n",
        "for split in ds.keys():\n",
        "  ds_cur = ds[split].map(join_tokens)\n",
        "  # ds_cur = ds_cur.remove_columns([\"tokens\", \"ner_tags\"])\n",
        "  ds_join[split] = ds_cur\n",
        "\n",
        "ds = DatasetDict(ds_join)\n",
        "\n",
        "# ds_join = ds.map(join_tokens)\n"
      ],
      "metadata": {
        "id": "PobPlbDS8Mdj"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the total number of samples in the new dataset\n",
        "total_samples = sum(len(ds[split]) for split in ds.keys())\n",
        "print(f\"Total number of samples in ds: {total_samples}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Mgkl0E_9Vr6",
        "outputId": "34a291b1-29e6-4d6b-f3a3-e4385c562f14"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of samples in ds: 501435\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYN0vKNHBwNS",
        "outputId": "0f8cab82-b392-4215-c422-fa64ead65140"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'tokens': ['பைரவருக்கு',\n",
              "  'தேய்பிறை',\n",
              "  'அஷ்டமியில்',\n",
              "  'விசேஷ',\n",
              "  'அபிஷேக',\n",
              "  'ஆராதனைகள்',\n",
              "  'நடைபெறுகின்றன',\n",
              "  '.'],\n",
              " 'ner_tags': [1, 0, 0, 0, 0, 0, 0, 0],\n",
              " 'text': 'பைரவருக்கு தேய்பிறை அஷ்டமியில் விசேஷ அபிஷேக ஆராதனைகள் நடைபெறுகின்றன .'}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds.cache_files"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ESZl7FgCXTX",
        "outputId": "7df3f778-e326-43b7-8b26-f2119c17448b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'train': [{'filename': '/root/.cache/huggingface/datasets/ai4bharat___naamapadam/ta/1.0.0/9d4f21ac57d11ed4f9ea64854fdc9f5618e61acc/cache-6fa5c2925a90a2d2.arrow'}],\n",
              " 'test': [{'filename': '/root/.cache/huggingface/datasets/ai4bharat___naamapadam/ta/1.0.0/9d4f21ac57d11ed4f9ea64854fdc9f5618e61acc/cache-f847a9d1fc817fde.arrow'}],\n",
              " 'validation': [{'filename': '/root/.cache/huggingface/datasets/ai4bharat___naamapadam/ta/1.0.0/9d4f21ac57d11ed4f9ea64854fdc9f5618e61acc/cache-a4d379fb3e2138a7.arrow'}]}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Q9"
      ],
      "metadata": {
        "id": "vesEzidQF8S2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ds['train']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gU_mdlHVvHG5",
        "outputId": "4ef5bd97-c7fa-4044-99a9-5afd919b8020"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['tokens', 'ner_tags', 'text'],\n",
              "    num_rows: 497882\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_filter = dict()\n",
        "\n",
        "for split in ds.keys():\n",
        "  ds_filter[split] = ds[split].filter(lambda x: len(x[\"tokens\"]) >= 6)\n",
        "\n",
        "ds_filter = DatasetDict(ds_filter)"
      ],
      "metadata": {
        "id": "OQmk5IuzDX_V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "5446059b1d094f118d2549492ff531bb",
            "837454d3183f45628d2c7bb7f773fac4",
            "abc65f5a9b3a40c8a5808fc4992ba520",
            "66b9f41a584f4a1f832aca1e9433ede0",
            "1991e8e218864cb9a10bdc5586469bef",
            "9b4155f2d2814295aea1910751d53cda",
            "d5ab353cb48046f687d37e00f72d8b3a",
            "1e47de6428ea474292bd733a1dbc3f83",
            "62bdcf2036d64e78b0680d38db3ac05f",
            "dfdafed1f19b4f69969ac57126d43b64",
            "8addfcd18359473bb9d480c8912b54fb",
            "5159472371564f40938bb456f69a2ddc",
            "76321ac71bf14cbbacb21c6b438fa5f6",
            "47f3cfa0bb7f4b4dbaa34401c36ada95",
            "3df45017acc741f8890adffa23c4150b",
            "f9d131ed3cda4710978ce33bb82904e5",
            "351020663855427da77d62c8a6d6b811",
            "35e5d47f1a9740cdaf5927efa67efa9e",
            "40e89189bf1e47d595f528d7e5558c79",
            "7d0dad6a239842118a8c88ba9b6a01e1",
            "e6c6abd3aa2f4dad8ad2af95e94e6764",
            "ad7091cd770641fcb207bb1fa1783464",
            "257c3cbbdd9c4fef9342481668b177ba",
            "564d3024691647c4b958bac79022938e",
            "9b72667943f445ca89f241db362e5204",
            "e37fe03ae8e341bfaf4b1b8a3660e1ac",
            "d0bacf9ab19e454c89549fb20daf692a",
            "63bcf49acb384175b4669e15125dc038",
            "c09c961d1ccf4489bb553325c0665279",
            "6364261b09f34b62acfe224ae71fb406",
            "4d64505730994755a433893cee25ae09",
            "4ff5378986d543f69b2a11cb12ca9e7d",
            "8b190767efaa4861868aba7548350943"
          ]
        },
        "outputId": "3d1e18c3-7ac8-4357-eb48-7f14c8f4024e"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/497882 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5446059b1d094f118d2549492ff531bb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/758 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5159472371564f40938bb456f69a2ddc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/2795 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "257c3cbbdd9c4fef9342481668b177ba"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_filter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rc_3wfAZF5zs",
        "outputId": "a118891d-1f05-4963-de98-8ed508e0a96e"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'text'],\n",
              "        num_rows: 367791\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'text'],\n",
              "        num_rows: 627\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'text'],\n",
              "        num_rows: 2077\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(len(ds_filter[split]) for split in ds_filter.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctyatlP6GAA-",
        "outputId": "065dd80a-79c3-4c27-9b11-46192ca7e2f5"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "370495"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Q10"
      ],
      "metadata": {
        "id": "_E24waRSGnt4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "get_dataset_config_names(\"ai4bharat/indic_glue\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "228377b398d34b95b042205288eae7e7",
            "b6e436ee81e14fb9a1f9a45ba13e90eb",
            "297a0a51a079424eb4067c2aaf329a11",
            "586796345f2d44da868c2d8a36ccfdd4",
            "53465af7d0074dab910d809b9f886a05",
            "0bc25f0ea96040eea8d8139d18be9995",
            "d05597c10da6402f94f988d0e75e1c18",
            "cabab1096bf941979c462ce135d1590d",
            "75bab5160d8a4e7fb4a5f4eafb71ebfb",
            "1dcbb17ff868463d9314750d90ef2396",
            "d51e6d3ecdd94c31af20046aa054ea1d"
          ]
        },
        "id": "zSJL5ECsGuTf",
        "outputId": "b8e5cc9c-e825-4764-9733-5e0fa06e1af8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/49.5k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "228377b398d34b95b042205288eae7e7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['actsa-sc.te',\n",
              " 'bbca.hi',\n",
              " 'copa.en',\n",
              " 'copa.gu',\n",
              " 'copa.hi',\n",
              " 'copa.mr',\n",
              " 'csqa.as',\n",
              " 'csqa.bn',\n",
              " 'csqa.gu',\n",
              " 'csqa.hi',\n",
              " 'csqa.kn',\n",
              " 'csqa.ml',\n",
              " 'csqa.mr',\n",
              " 'csqa.or',\n",
              " 'csqa.pa',\n",
              " 'csqa.ta',\n",
              " 'csqa.te',\n",
              " 'cvit-mkb-clsr.en-bn',\n",
              " 'cvit-mkb-clsr.en-gu',\n",
              " 'cvit-mkb-clsr.en-hi',\n",
              " 'cvit-mkb-clsr.en-ml',\n",
              " 'cvit-mkb-clsr.en-mr',\n",
              " 'cvit-mkb-clsr.en-or',\n",
              " 'cvit-mkb-clsr.en-ta',\n",
              " 'cvit-mkb-clsr.en-te',\n",
              " 'cvit-mkb-clsr.en-ur',\n",
              " 'iitp-mr.hi',\n",
              " 'iitp-pr.hi',\n",
              " 'inltkh.gu',\n",
              " 'inltkh.ml',\n",
              " 'inltkh.mr',\n",
              " 'inltkh.ta',\n",
              " 'inltkh.te',\n",
              " 'md.hi',\n",
              " 'sna.bn',\n",
              " 'wiki-ner.as',\n",
              " 'wiki-ner.bn',\n",
              " 'wiki-ner.gu',\n",
              " 'wiki-ner.hi',\n",
              " 'wiki-ner.kn',\n",
              " 'wiki-ner.ml',\n",
              " 'wiki-ner.mr',\n",
              " 'wiki-ner.or',\n",
              " 'wiki-ner.pa',\n",
              " 'wiki-ner.ta',\n",
              " 'wiki-ner.te',\n",
              " 'wnli.en',\n",
              " 'wnli.gu',\n",
              " 'wnli.hi',\n",
              " 'wnli.mr',\n",
              " 'wstp.as',\n",
              " 'wstp.bn',\n",
              " 'wstp.gu',\n",
              " 'wstp.hi',\n",
              " 'wstp.kn',\n",
              " 'wstp.ml',\n",
              " 'wstp.mr',\n",
              " 'wstp.or',\n",
              " 'wstp.pa',\n",
              " 'wstp.ta',\n",
              " 'wstp.te']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"ai4bharat/indic_glue\", 'inltkh.ta')"
      ],
      "metadata": {
        "id": "sUt8OZHlGOFl"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iPnqTEnoG1lr",
        "outputId": "70b5c669-8d78-4e0b-8e2e-bfe0b4feab0e"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 5346\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 669\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 669\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"train\"][:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BMVrkmxG8Hk",
        "outputId": "5e9bbe84-248d-4a1c-dd6b-49eb64ddba7d"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': ['கே.வி.ஆனந்தே ட்விட்டரில் இதை அறிவித்துள்ளார். இந்தப் படத்துக்கு கேவ்மிக் ஆரி ஒளிப்பதிவு செய்ய, ஹாரிஸ் ஜெயராஜ் இசையமைக்கிறார். பட்டுக்கோட்டை பிரபாகர் வசனம் எழுத, கலை இயக்குநராக கிரண் பணியாற்றுகிறார். இந்தப் படத்தை லைகா புரொடக்\\u200cஷன்ஸ் நிறுவனம் தயாரிக்கிறது.',\n",
              "  'பரிகாரம்: சுக்கிரன், கேதுவுக்குப் பிரீதி, பரிகாரங்கள் செய்து கொள்ளவும்.',\n",
              "  '8 லட்சம் கார்களை திரும்பப் பெற்றது ஹோண்டா நிறுவனம்',\n",
              "  '2003-ம் ஆண்டு அக்டோபர் மாதத்திலிருந்து 2013-ம் ஆண்டு மார்ச் மாதம் வரை ஜெனரல் டைனமிக் கார்ப்பரேஷன் நிறுவனத்தின் நிர்வாகத் துணைத்தலைவர் பொறுப்பில் இருந்தவர்.',\n",
              "  'தொழில் வியாபாரத்தில் பணத்தேவை ஏற்படலாம். கடன் விவகாரங்களில் கவனமாக செயல்படுவது நல்லது. உத்தியோகத்தில் இருப்பவர்கள் ஓய்வு இல்லாமல் பணியாற்ற வேண்டி இருக்கும். இயந்திரம், நெருப்பு, ஆயுதத்தை பயன்படுத்துவோர் மிகவும் கவனமாக இருப்பது அவசியம்.'],\n",
              " 'label': [6, 5, 1, 1, 5]}"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_filter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XtZigXySxbT2",
        "outputId": "f2b6887b-c9b5-4a1d-ba90-a71a39a3915e"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'text'],\n",
              "        num_rows: 367791\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'text'],\n",
              "        num_rows: 627\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'text'],\n",
              "        num_rows: 2077\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "# Concatenate the train, test, and validation splits\n",
        "merged_dataset = concatenate_datasets([ds_filter['train'], ds_filter['test'], ds_filter['validation']])\n",
        "\n",
        "# Print the total number of rows in the merged dataset\n",
        "print(f\"Merged dataset size: {merged_dataset.num_rows}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vajD5Q50xoXr",
        "outputId": "0963e089-3e53-479e-921f-668adb3eebcb"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Merged dataset size: 370495\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_dataset = merged_dataset.remove_columns([\"tokens\", \"ner_tags\"])"
      ],
      "metadata": {
        "id": "FZs1dhVLx2GP"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, interleave_datasets\n",
        "\n",
        "# Load naamapadam dataset (assuming it's already filtered for >=6 words)\n",
        "ds_naamapadam = merged_dataset  # Assuming this is your filtered naamapadam dataset\n",
        "\n",
        "# Load the Tamil sub-dataset from indic_glue\n",
        "ds_indic_glue = load_dataset(\"ai4bharat/indic_glue\", \"inltkh.ta\", split = \"train+test+validation\")\n",
        "\n",
        "# Define the word filter function to filter the dataset with >=6 words\n",
        "def word_filter(ds):\n",
        "    temp = ds[\"text\"].split(\" \")\n",
        "    return len(temp) >= 6\n",
        "\n",
        "# Apply filter to indic_glue dataset\n",
        "filtered_indic_glue = ds_indic_glue.filter(word_filter)\n",
        "\n",
        "# Interleave datasets with 80% samples from naamapadam and 20% from indic_glue\n",
        "ds_interleaved = interleave_datasets(\n",
        "    [ds_naamapadam, filtered_indic_glue],\n",
        "    probabilities=[0.8, 0.2],\n",
        "    seed=42\n",
        ")\n",
        "\n",
        "# Get the number of samples in the interleaved dataset\n",
        "num_samples = len(ds_interleaved)\n",
        "print(f\"Total number of samples after interleaving: {num_samples}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "f131b18d19fb4dbea9c5c11eb18400e9",
            "433c76969a9d47f59b90ea7e4812917f",
            "4a9636161a40430ab808d75e7dfabef5",
            "c2e506f927644828bbfd5acab15f832b",
            "336f51f8345e4ce9af43746b20a9a1c9",
            "fba537e88db14f3eb873575eb768277d",
            "b55a70b6be9a492bbb3f01a997d9dedc",
            "34f6c57f10ad498780bba135833b5a91",
            "2f7f98552d62486e84b089368a072785",
            "b14ff53e2b5a4494a4430e30876a3d6e",
            "bd3f637cd34248b6b6ec548b6a6f9763"
          ]
        },
        "id": "woAEDfFIHaMq",
        "outputId": "98885815-5a16-4d58-fdbb-e510c3d4cf5f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/6684 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f131b18d19fb4dbea9c5c11eb18400e9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of samples after interleaving: 32354\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_indic_glue"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yugyk5S8JtSq",
        "outputId": "f17cdbdb-e888-470f-d8e3-61de010adf54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label'],\n",
              "    num_rows: 5153\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(ds)"
      ],
      "metadata": {
        "id": "LJvspko6Mz8n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74cbc2de-ea47-4305-bc2c-db4742de936a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datasets.dataset_dict.DatasetDict"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    }
  ]
}