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+ "text": [
+ "Collecting datasets\n",
+ " Downloading datasets-3.3.2-py3-none-any.whl.metadata (19 kB)\n",
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.48.3)\n",
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+ "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=2.0.0->accelerate)\n",
+ " Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
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+ "\u001b[?25hDownloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: xxhash, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, nvidia-cusolver-cu12, datasets\n",
+ " Attempting uninstall: nvidia-nvjitlink-cu12\n",
+ " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
+ " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
+ " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
+ " Attempting uninstall: nvidia-curand-cu12\n",
+ " Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
+ " Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
+ " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
+ " Attempting uninstall: nvidia-cufft-cu12\n",
+ " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
+ " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
+ " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
+ " Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+ " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
+ " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
+ " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
+ " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
+ " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
+ " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
+ " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
+ " Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+ " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
+ " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
+ " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
+ " Attempting uninstall: nvidia-cublas-cu12\n",
+ " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
+ " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
+ " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
+ " Attempting uninstall: nvidia-cusparse-cu12\n",
+ " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
+ " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
+ " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
+ " Attempting uninstall: nvidia-cudnn-cu12\n",
+ " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
+ " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
+ " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
+ " Attempting uninstall: nvidia-cusolver-cu12\n",
+ " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
+ " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
+ " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
+ "Successfully installed datasets-3.3.2 dill-0.3.8 multiprocess-0.70.16 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 xxhash-3.5.0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "# Load dataset from Hugging Face\n",
+ "dataset = load_dataset(\"Sri221006/Tamil_Colloquial_Dataset\")\n",
+ "print(dataset)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 309,
+ "referenced_widgets": [
+ "a06c9fd9c14540669987ac3afe664988",
+ "6b045b7b45f8477cbef29ac4e41e877b",
+ "072efcb385474d2c9557f7f8b32327fb",
+ "657ace9673f64c7f8134fc8cf4adc039",
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+ "4a86ef4b37504beeb5ad40782e6b154a",
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+ "ff0e49a1a71a4a4da16aaec8bdac4e3d",
+ "3fa281f83f7144debb22b33d2aac9000",
+ "69a33c28473940ccb60772f399f35358",
+ "d18ff9e8ccfd482195bccd4d49044c2d",
+ "7a72e54ba1dc40f5aa6845ae85c5b3ff",
+ "0b5e93a75f874e6595b7887b7339082e",
+ "662aa4baf1bb4389b5b55d8f4fd507c5"
+ ]
+ },
+ "id": "30N7nCp5jeVm",
+ "outputId": "6aba3cb5-c2e1-4c8c-8932-bf1deaced13a"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Dataset.csv: 0%| | 0.00/6.98k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "a06c9fd9c14540669987ac3afe664988"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0%| | 0/100 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1aedf98110654a32aba9c8a94208632c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['English', 'Tamil'],\n",
+ " num_rows: 100\n",
+ " })\n",
+ "})\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import AutoTokenizer\n",
+ "\n",
+ "# Correct IndicBERT model\n",
+ "model_checkpoint = \"ai4bharat/IndicBERTv2-MLM-only\"\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
+ "\n",
+ "# Test with Tamil text\n",
+ "text = \"வணக்கம்! நீங்கள் எப்படி இருக்கிறீர்கள்?\"\n",
+ "tokens = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\")\n",
+ "print(tokens)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 168,
+ "referenced_widgets": [
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+ "c642dd8f03a14380be8f084b7dfa43ae",
+ "8e750a706fdf4e7eb6c729993aa1eb76",
+ "5663632976244a19a24df49b252d683c",
+ "3801efd8c558490a96e7d3be3bb60fd1",
+ "4cb3ddfd74184600be2b09719e944f44"
+ ]
+ },
+ "id": "UVm2wnQHwf_T",
+ "outputId": "cd5f6137-0f22-46a6-cd50-b2947e4b6c05"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer_config.json: 0%| | 0.00/51.0 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "72b962c05e9a47a89b582302ea240a52"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer.json: 0%| | 0.00/7.75M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d3144233b31348f59989df07a7587a2f"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "special_tokens_map.json: 0%| | 0.00/125 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "414dcd55f90449459566f3cf9f1c863f"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'input_ids': tensor([[ 1, 142561, 48, 22760, 28606, 213764, 78, 2]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1]])}\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install datasets\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "4QM2Nwiww2V_",
+ "outputId": "e204984a-8ba5-4b65-b229-53c69ef8c6fb"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting datasets\n",
+ " Downloading datasets-3.3.2-py3-none-any.whl.metadata (19 kB)\n",
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+ "Collecting xxhash (from datasets)\n",
+ " Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
+ "Collecting multiprocess<0.70.17 (from datasets)\n",
+ " Downloading multiprocess-0.70.16-py311-none-any.whl.metadata (7.2 kB)\n",
+ "Requirement already satisfied: fsspec<=2024.12.0,>=2023.1.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets) (2024.10.0)\n",
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+ "Downloading datasets-3.3.2-py3-none-any.whl (485 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 xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: xxhash, dill, multiprocess, datasets\n",
+ "Successfully installed datasets-3.3.2 dill-0.3.8 multiprocess-0.70.16 xxhash-3.5.0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "# Load your dataset from Hugging Face\n",
+ "dataset = load_dataset(\"Sri221006/Tamil_Colloquial_Dataset\")\n",
+ "\n",
+ "# Check the structure of the dataset\n",
+ "print(dataset)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 185,
+ "referenced_widgets": [
+ "f0fafb511a2546d799fcefe20d0772d8",
+ "7235c59faaf14191adbac513e08b61e5",
+ "47db4514fd014f4191d67245dcc31acd",
+ "5e3cd29a339b4f2192a6bf50f214275a",
+ "1c1ffc86c3bf489d9ef0bdb13c75162a",
+ "124bfe27eff74e1c992979e6f775d8bd",
+ "b72a303c13d84520aa74f0e54d272230",
+ "fd86012bf3504158b3317fc5bf292c08",
+ "7ed174fdefbe42169d7edfc8e7b017a4",
+ "2b99e342185d488093887df5f5bd5a48",
+ "55f817e424054470b9f78b827a55bbc9",
+ "44870ad40bdb4b5c8e56ee196940dceb",
+ "b5fa2f7d437d4639ba5b85dd798e4698",
+ "409fe6f5bd424c88a53ab9578ee35441",
+ "d9f0fea6b1cd43138dba67bdcd632761",
+ "70aecab51318450e963992d6cdc42773",
+ "b0b059c322c04379b4b454185a9146da",
+ "f4c4dd3bcdc844debf90f861ecea7a72",
+ "ab0c4d29a0ef4fd28eccad9bc92e8cf4",
+ "fc5d2e05e06f457d8c8047668d9474a0",
+ "3a0bcef566cd45ff887d60b297876594",
+ "8796e77aaeae4212bd0175bae6cbe587"
+ ]
+ },
+ "id": "LVHfEzTiw6Hq",
+ "outputId": "d858730b-301b-4bcb-f846-49068654da2f"
+ },
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Dataset.csv: 0%| | 0.00/6.98k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f0fafb511a2546d799fcefe20d0772d8"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0%| | 0/100 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "44870ad40bdb4b5c8e56ee196940dceb"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['English', 'Tamil'],\n",
+ " num_rows: 100\n",
+ " })\n",
+ "})\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(dataset.column_names)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EH-waEf1xY16",
+ "outputId": "819cbfa5-efbc-4c5f-a96e-507bc41dee1b"
+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'train': ['English', 'Tamil']}\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def tokenize_function(examples):\n",
+ " return tokenizer(examples[\"Tamil\"], padding=\"max_length\", truncation=True)\n",
+ "\n",
+ "# Apply tokenizer\n",
+ "tokenized_datasets = dataset.map(tokenize_function, batched=True)\n",
+ "\n",
+ "# Remove unneeded columns\n",
+ "tokenized_datasets = tokenized_datasets.remove_columns([\"Tamil\"])\n",
+ "\n",
+ "# Check a sample\n",
+ "print(tokenized_datasets[\"train\"][0])\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 104,
+ "referenced_widgets": [
+ "bbfa9f051fd443268ba49ca90373b8e1",
+ "6bccfc61c9dd4becb7c080a618e95399",
+ "7934e3f9ba3b4debb8539a0bab23d177",
+ "19fa6e51244747a78f3160b9a95cfd7e",
+ "9e2027540eaf4e86a4cbe9e2b3f716fd",
+ "a35b5f73768944d590c83511c00a5efd",
+ "a0fdb46f3f814d8a9a164ba3b4235d83",
+ "dab491cc515646b096a96d84cfe4f3f1",
+ "154e470d74004e95993c895298dc54c6",
+ "68fbf5c498a84619a0ac9b0b796a2f43",
+ "8a3c29dd53834ad0a0e2342c8d55ef73"
+ ]
+ },
+ "id": "SFcRE-wtxtEb",
+ "outputId": "3858f961-992a-469f-fb98-72c8b9aea0d5"
+ },
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/100 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "bbfa9f051fd443268ba49ca90373b8e1"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'English': \"What's the time?\", 'input_ids': [1, 39128, 18914, 78, 2], 'token_type_ids': [0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1]}\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from datasets import DatasetDict\n",
+ "\n",
+ "# Split dataset into 90% train, 10% validation\n",
+ "train_test_split = tokenized_datasets[\"train\"].train_test_split(test_size=0.1)\n",
+ "dataset = DatasetDict({\n",
+ " \"train\": train_test_split[\"train\"],\n",
+ " \"validation\": train_test_split[\"test\"]\n",
+ "})\n",
+ "\n",
+ "print(dataset)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vJ7NHS1VySCx",
+ "outputId": "7aa9dc42-7b99-4b02-d234-6718c78fd58c"
+ },
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['English', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+ " num_rows: 90\n",
+ " })\n",
+ " validation: Dataset({\n",
+ " features: ['English', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+ " num_rows: 10\n",
+ " })\n",
+ "})\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import AutoModelForMaskedLM\n",
+ "\n",
+ "model_name = \"ai4bharat/IndicBERTv2-MLM-only\"\n",
+ "model = AutoModelForMaskedLM.from_pretrained(model_name)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 222,
+ "referenced_widgets": [
+ "4c6d0966eaed48cb8de0f98a7a022215",
+ "3a6f61676c054198a093e70bdee30507",
+ "555994708b7e4e7c815bed9a0d630a63",
+ "6f90cf2820064666b3fc6e4c0d36136e",
+ "7a259979a305463ab0f9f6cf232c9d07",
+ "a28d4505528642b488cba98908a624f7",
+ "685c2f9c76604411b86f3ab01b94ba86",
+ "dc597164f8a6411d8eb8653dea1b5bf4",
+ "71b7d7b8436f49a78905a73f98b9275d",
+ "90fe197b55cb4898bee7ed3cc9565255",
+ "3a2a5f9c2b044ba1bc69a626bbcdf7f5",
+ "7043db1b93554198819cd00cada8feab",
+ "b7fb51bacb8e47918a1ac6b0356abf44",
+ "636213900d1149cc9ae7b964b076752e",
+ "4cda0b9c7f974fefa4693dc903d9fb13",
+ "8fa2c35ef9dd47988e422ce84f74ec0d",
+ "71cb7e4e67784d4c93b7ab54252b9ab1",
+ "6a4a1052ff6241f6abdda9c68714eab4",
+ "b64315bfa7ed4f13ac865067211cb35d",
+ "615c7eea94d54c97acd8913d08a198d6",
+ "9a6e30a77c074a75837442b85ab4e469",
+ "c7e1719cc5664ebb8ff7e4e6772fe691"
+ ]
+ },
+ "id": "d5f9AUUa0YUe",
+ "outputId": "dbfcd893-7648-48b5-bab5-b4735492d517"
+ },
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "config.json: 0%| | 0.00/639 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4c6d0966eaed48cb8de0f98a7a022215"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "pytorch_model.bin: 0%| | 0.00/1.12G [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7043db1b93554198819cd00cada8feab"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "BertForMaskedLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
+ " - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes\n",
+ " - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
+ " - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n",
+ "Some weights of the model checkpoint at ai4bharat/IndicBERTv2-MLM-only were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
+ "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import DataCollatorForLanguageModeling\n",
+ "\n",
+ "# Data collator for masked language modeling\n",
+ "data_collator = DataCollatorForLanguageModeling(\n",
+ " tokenizer=tokenizer,\n",
+ " mlm=True,\n",
+ " mlm_probability=0.15 # 15% tokens masked for MLM training\n",
+ ")\n"
+ ],
+ "metadata": {
+ "id": "jApq_FxO0fke"
+ },
+ "execution_count": 15,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import TrainingArguments\n",
+ "\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=\"./results\",\n",
+ " evaluation_strategy=\"epoch\",\n",
+ " save_strategy=\"epoch\",\n",
+ " per_device_train_batch_size=8,\n",
+ " per_device_eval_batch_size=8,\n",
+ " num_train_epochs=3,\n",
+ " save_total_limit=2,\n",
+ " logging_dir=\"./logs\",\n",
+ " logging_steps=10,\n",
+ " report_to=\"none\" # Disable logging to external services\n",
+ ")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "mM9m1TUL0p68",
+ "outputId": "3b764af7-d0d9-4c8b-a411-7988f835485a"
+ },
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/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"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#Split train dataset into 90% train and 10% validation\n",
+ "tokenized_datasets = tokenized_datasets[\"train\"].train_test_split(test_size=0.1)\n",
+ "\n",
+ "trainer = Trainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=tokenized_datasets[\"train\"], # 90% data for training\n",
+ " eval_dataset=tokenized_datasets[\"test\"], # 10% for validation\n",
+ " tokenizer=tokenizer,\n",
+ " data_collator=data_collator\n",
+ ")\n",
+ "\n",
+ "trainer.train()\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 397
+ },
+ "id": "N0wlwbSG1Jyz",
+ "outputId": "c7997b02-c5b6-48ef-dc95-cc05404319b8"
+ },
+ "execution_count": 19,
+ "outputs": [
+ {
+ "metadata": {
+ "tags": null
+ },
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "<ipython-input-19-88d8767c6a93>:4: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
+ " trainer = Trainer(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='25' max='36' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [25/36 03:13 < 01:32, 0.12 it/s, Epoch 2/3]\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",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>3.685700</td>\n",
+ " <td>1.323778</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>2.438600</td>\n",
+ " <td>3.603593</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table><p>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='36' max='36' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [36/36 09:06, 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",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>3.685700</td>\n",
+ " <td>1.323778</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>2.438600</td>\n",
+ " <td>3.603593</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>2.820400</td>\n",
+ " <td>4.208070</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table><p>"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=36, training_loss=2.990552054511176, metrics={'train_runtime': 563.1494, 'train_samples_per_second': 0.479, 'train_steps_per_second': 0.064, 'total_flos': 1160655505536.0, 'train_loss': 2.990552054511176, 'epoch': 3.0})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 19
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "trainer.save_model(\"./fine_tuned_model\")\n",
+ "tokenizer.save_pretrained(\"./fine_tuned_model\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "m7wclgg63re9",
+ "outputId": "ff28ac16-a97c-4d2b-ff9a-a6a59bed1307"
+ },
+ "execution_count": 21,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "('./fine_tuned_model/tokenizer_config.json',\n",
+ " './fine_tuned_model/special_tokens_map.json',\n",
+ " './fine_tuned_model/tokenizer.json')"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 21
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Evaluate the model on the validation set\n",
+ "results = trainer.evaluate()\n",
+ "print(results)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 74
+ },
+ "id": "KMAzD9t93xVV",
+ "outputId": "1f415f38-7e26-4223-a3ea-d43ff436a38c"
+ },
+ "execution_count": 22,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [2/2 00:00]\n",
+ " </div>\n",
+ " "
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'eval_loss': 2.7228801250457764, 'eval_runtime': 2.0229, 'eval_samples_per_second': 4.943, 'eval_steps_per_second': 0.989, 'epoch': 3.0}\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Save model and tokenizer\n",
+ "trainer.save_model(\"fine_tuned_model\")\n",
+ "tokenizer.save_pretrained(\"fine_tuned_model\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "84fAr2YC30l8",
+ "outputId": "28d4e284-8e87-47ac-8594-9790b92994fb"
+ },
+ "execution_count": 23,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "('fine_tuned_model/tokenizer_config.json',\n",
+ " 'fine_tuned_model/special_tokens_map.json',\n",
+ " 'fine_tuned_model/tokenizer.json')"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 23
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!huggingface-cli login\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "m_4BwtU7437E",
+ "outputId": "33d776d3-7ffb-43c5-e4cd-9ba0a2877a9f"
+ },
+ "execution_count": 28,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
+ " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
+ " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
+ " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
+ " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
+ "\n",
+ " A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n",
+ " Setting a new token will erase the existing one.\n",
+ " To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
+ "Enter your token (input will not be visible): \n",
+ "Add token as git credential? (Y/n) Y\n",
+ "Token is valid (permission: fineGrained).\n",
+ "The token `AI_Model` has been saved to /root/.cache/huggingface/stored_tokens\n",
+ "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
+ "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
+ "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
+ "\n",
+ "git config --global credential.helper store\n",
+ "\n",
+ "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
+ "Token has not been saved to git credential helper.\n",
+ "Your token has been saved to /root/.cache/huggingface/token\n",
+ "Login successful.\n",
+ "The current active token is: `AI_Model`\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "trainer.push_to_hub(\"Sri221006/Tamil_Colloquial_IndicBERT\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 237,
+ "referenced_widgets": [
+ "97850b617aba407aa7d713c339b0e877",
+ "30d6fbb7efc64154945125e9ab724c2a",
+ "76a0639e78cd4b54bc531fca7577a581",
+ "9b346b9f6d7a498aa4de778b4a81b2fa",
+ "19b32d2eeaa54084a189ff9f270403ef",
+ "9eb4d63e199b435ab563415c636131f6",
+ "c017f908731a4c9184219e7271eeb439",
+ "a7d8c6a7709b431499069cafe6b6d883",
+ "8d28bd9277714e46b406b905e6694f90",
+ "21243d884ee74a169b14f8d52f6fc0da",
+ "366e086946db4c59b523d9f1ac2a689a",
+ "fa956eeaf6f3470081e24bb06e504412",
+ "bc88876d8092434aa2be7af9a275d648",
+ "c52b8b40f7b54ddcbf59ea5d7db813c7",
+ "a67149ea3efa42d1b9704c0eac0a80c0",
+ "3c4411fd3d034ad8865ac23e3774daa9",
+ "5dee881b2bd1429f8836a06d49a2bbb5",
+ "133a2a513f4844d09dd8f474184e08c1",
+ "9611872524ed498db2ff263bc8b14a8c",
+ "3d58843aa16c4e7a93ec7ad9f553b3a3",
+ "622da55b6896436384e65540f3bbd282",
+ "4011559e22a44d7fbb457280f11f84c1",
+ "9d96a98498f9455181a2a5d38c018168",
+ "43c2860a67b34b7e9bf13f59b3cafccb",
+ "b5230eef746047c19b044702073b7810",
+ "65c8c5f59a334b0ba77a47638b7cfa96",
+ "fc842409835d435fa92c82ab629572af",
+ "a455339da0984ab89d8f16b3998a843a",
+ "2b9e64760f0c49c399ca3d9c40888c37",
+ "18a6513ab92a45bea3f2d85555e7b67b",
+ "07b8fc1958844af794862558fb7d8e7b",
+ "24b899d1c4814d73b3b4673fed244d9c",
+ "9008b8eb6cc840eb89deba87b012f369"
+ ]
+ },
+ "id": "yNsij36U5Ffi",
+ "outputId": "c9f35398-1d2f-4f28-afa4-faec46861855"
+ },
+ "execution_count": 29,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Upload 2 LFS files: 0%| | 0/2 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "97850b617aba407aa7d713c339b0e877"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "model.safetensors: 0%| | 0.00/1.11G [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "fa956eeaf6f3470081e24bb06e504412"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "training_args.bin: 0%| | 0.00/5.24k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9d96a98498f9455181a2a5d38c018168"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "CommitInfo(commit_url='https://huggingface.co/Sri221006/results/commit/26ce57463474225846f3b78dd4ca2751f6b5ca91', commit_message='Sri221006/Tamil_Colloquial_IndicBERT', commit_description='', oid='26ce57463474225846f3b78dd4ca2751f6b5ca91', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Sri221006/results', endpoint='https://huggingface.co', repo_type='model', repo_id='Sri221006/results'), pr_revision=None, pr_num=None)"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 29
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from huggingface_hub import list_repo_files\n",
+ "\n",
+ "files = list_repo_files(\"Sri221006/Tamil_Colloquial_IndicBERT\")\n",
+ "print(files)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6a2yAI066-d9",
+ "outputId": "f748ebe0-7409-4633-d70f-c85522d7045b"
+ },
+ "execution_count": 32,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "['.gitattributes']\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "trainer.push_to_hub(\"Sri221006/Tamil_Colloquial_IndicBERT\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 176
+ },
+ "id": "UVYaO0h-7H78",
+ "outputId": "10856478-cd2d-46c0-a802-3a55840b3ad6"
+ },
+ "execution_count": 33,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "No files have been modified since last commit. Skipping to prevent empty commit.\n",
+ "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "CommitInfo(commit_url='https://huggingface.co/Sri221006/results/commit/26ce57463474225846f3b78dd4ca2751f6b5ca91', commit_message='Sri221006/Tamil_Colloquial_IndicBERT', commit_description='', oid='26ce57463474225846f3b78dd4ca2751f6b5ca91', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Sri221006/results', endpoint='https://huggingface.co', repo_type='model', repo_id='Sri221006/results'), pr_revision=None, pr_num=None)"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 33
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
+ "\n",
+ "model_name = \"ai4bharat/indic-bert\" # Replace with your base model\n",
+ "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+ "\n",
+ "save_directory = \"./Tamil_Colloquial_IndicBERT\"\n",
+ "\n",
+ "# Save model and tokenizer\n",
+ "model.save_pretrained(save_directory)\n",
+ "tokenizer.save_pretrained(save_directory)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 286,
+ "referenced_widgets": [
+ "19f0ba6b32c345ae9f9f0cadfd1e7390",
+ "5f1176a81fa549d9bdf8c3897ce147b0",
+ "cc88c189dd7043e091aa24a02ebdc1a2",
+ "8af8cebcb0164c9cb39a00f0d3897aab",
+ "2cee93a1e5854977a3e70e1c3548289e",
+ "482f4f894dbc435998098a84d01c61c9",
+ "940861377bf34cf5abafd173b855c6a6",
+ "59bceecbf7d14ae982875f6925d95d34",
+ "d23921a987594939affae50cd767544d",
+ "05c48c58236b4615ac7d21539973f44b",
+ "9dca3ca0d98f4b78b9b9cb4bf98f96d3",
+ "08e8510a72a548ce935da69eab65a3bb",
+ "d9097d8a9c004491a4957a209cb71e1d",
+ "8bf6f88fa88b426d8744769f6b003538",
+ "5b7fc4a269084b6b9a6d522385855107",
+ "46bc3442ffe3482daa109dc1eb60a15d",
+ "4c6cb71a135e4a51b503d1ddbd9e92f0",
+ "2811599c154c40888a6a9af04d675f80",
+ "184bfb4783d549eaa344b5c4f6ba115e",
+ "16a00ae332e84b388fba9c0e580af5ed",
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+ "4bb6ed8e884d4a78a492b5c63308a145",
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+ "e8becd34d7f049be97c4d57bc511cce5",
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+ "57e8bde1ef874d69bd7b23f501bfc786",
+ "16f7e57e44f5472f8a7338b87e1063dd",
+ "25647cdc29024e8cb1623d6f91c57da8",
+ "f6903f7f3b6b4d2d97213c8a623afa61",
+ "3bb8f4b9c38449db9748a6cd6643fa4d",
+ "e01c43a1f1014091bb6a19d5052dd058",
+ "f6bbb44000704bdf8d1686d4544ed2f7",
+ "0227284c70eb4fe08ee6c8da605165d7",
+ "f35597ec2dcc4c808d41973cbf4da1de",
+ "1cceb0833cf34f129eb3bb1580e48bb6"
+ ]
+ },
+ "id": "mBmocJT28TEx",
+ "outputId": "7b6eb0e9-216d-498f-c5f4-8cf02e92cfe0"
+ },
+ "execution_count": 37,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "config.json: 0%| | 0.00/507 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "19f0ba6b32c345ae9f9f0cadfd1e7390"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "pytorch_model.bin: 0%| | 0.00/135M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "08e8510a72a548ce935da69eab65a3bb"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at ai4bharat/indic-bert 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"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "spiece.model: 0%| | 0.00/5.65M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4bb6ed8e884d4a78a492b5c63308a145"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "model.safetensors: 0%| | 0.00/135M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "894b959b644d4b699c74d2cdf134a1b2"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "('./Tamil_Colloquial_IndicBERT/tokenizer_config.json',\n",
+ " './Tamil_Colloquial_IndicBERT/special_tokens_map.json',\n",
+ " './Tamil_Colloquial_IndicBERT/spiece.model',\n",
+ " './Tamil_Colloquial_IndicBERT/added_tokens.json',\n",
+ " './Tamil_Colloquial_IndicBERT/tokenizer.json')"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 37
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
+ "\n",
+ "model = AutoModelForSequenceClassification.from_pretrained(\"Sri221006/Tamil_Colloquial_IndicBERT\")\n",
+ "tokenizer = AutoTokenizer.from_pretrained(\"Sri221006/Tamil_Colloquial_IndicBERT\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 209,
+ "referenced_widgets": [
+ "2567e21803884b84b29d7c304087b1f2",
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+ "ee31c0700d9e4f61b641eb66d3f28199",
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+ "a101e834e2514f429bb0f62681ce555a",
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+ ]
+ },
+ "id": "TAkCKbuB9gCR",
+ "outputId": "d7113c7d-4977-4912-ee1d-d2cb9bafe47a"
+ },
+ "execution_count": 38,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "config.json: 0%| | 0.00/843 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2567e21803884b84b29d7c304087b1f2"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "model.safetensors: 0%| | 0.00/134M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "45a71178e53048f3914394b1786a3607"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer_config.json: 0%| | 0.00/1.30k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "40ba598035d54544bbaa97f5e1d925d9"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "spiece.model: 0%| | 0.00/5.65M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "28de3c9eb7f74d8d90219e067f70c4ec"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer.json: 0%| | 0.00/15.3M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "73fb7ec840954807a3cb11d231c132e4"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "special_tokens_map.json: 0%| | 0.00/286 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "16b65ee286094e8d955de03cd21fa331"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
+ "\n",
+ "model = AutoModelForSequenceClassification.from_pretrained(\"Sri221006/Tamil_Colloquial_IndicBERT\")\n",
+ "tokenizer = AutoTokenizer.from_pretrained(\"Sri221006/Tamil_Colloquial_IndicBERT\")\n",
+ "\n",
+ "print(\"✅ Model loaded successfully!\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "LnAcMhfw9rGD",
+ "outputId": "1813f9d1-7e46-4926-b74f-cb4185ced0e0"
+ },
+ "execution_count": 39,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "✅ Model loaded successfully!\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import AutoModel, AutoTokenizer\n",
+ "\n",
+ "model_name = \"Sri221006/Tamil_Colloquial_IndicBERT\"\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+ "model = AutoModel.from_pretrained(model_name)\n",
+ "\n",
+ "print(\"Model and tokenizer loaded successfully!\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gIa16mdY9--2",
+ "outputId": "2e0d60ef-f8ef-491b-9ee6-61472518ca13"
+ },
+ "execution_count": 40,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Model and tokenizer loaded successfully!\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Sample colloquial Tamil text\n",
+ "texts = [\"சாப்பாடு சூப்பர் டா!\", \"அது நல்லா இல்ல\", \"என்னடா பண்ணிட்டு இருக்க?\", \"இது செம்ம கெத்த\", \"பைத்தியமா நீ?\"]\n",
+ "\n",
+ "# Tokenize the text\n",
+ "inputs = tokenizer(texts, padding=True, truncation=True, return_tensors=\"pt\")\n",
+ "\n",
+ "# Print tokenized input\n",
+ "print(inputs)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YC4aW5ov-Qs2",
+ "outputId": "b633aa37-c973-4aff-fec5-a4056303bdb6"
+ },
+ "execution_count": 42,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "{'input_ids': tensor([[ 2, 129740, 21585, 129740, 38515, 14359, 477, 3, 0,\n",
+ " 0, 0, 0, 0],\n",
+ " [ 2, 34913, 16430, 1471, 31911, 1471, 3, 0, 0,\n",
+ " 0, 0, 0, 0],\n",
+ " [ 2, 396, 1173, 5448, 11955, 7859, 5448, 5448, 32090,\n",
+ " 1385, 1385, 293, 3],\n",
+ " [ 2, 52721, 8742, 3534, 79030, 3587, 3, 0, 0,\n",
+ " 0, 0, 0, 0],\n",
+ " [ 2, 48100, 114250, 3534, 4919, 293, 3, 0, 0,\n",
+ " 0, 0, 0, 0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0],\n",
+ " [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
+ " [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+ " [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
+ " [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]])}\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install torch"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "CghxRc3D-hov",
+ "outputId": "914d57d1-332b-43bc-f303-b0810a370266"
+ },
+ "execution_count": 44,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.5.1+cu124)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch) (3.17.0)\n",
+ "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch) (4.12.2)\n",
+ "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch) (3.4.2)\n",
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.5)\n",
+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch) (2024.10.0)\n",
+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
+ "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
+ "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
+ "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch) (9.1.0.70)\n",
+ "Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.5.8)\n",
+ "Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.11/dist-packages (from torch) (11.2.1.3)\n",
+ "Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.11/dist-packages (from torch) (10.3.5.147)\n",
+ "Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch) (11.6.1.9)\n",
+ "Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.11/dist-packages (from torch) (12.3.1.170)\n",
+ "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch) (2.21.5)\n",
+ "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
+ "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
+ "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.0)\n",
+ "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch) (1.13.1)\n",
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import torch # ✅ Add this line\n",
+ "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
+ "\n",
+ "# Load model and tokenizer\n",
+ "model_name = \"Sri221006/Tamil_Colloquial_IndicBERT\" # Replace with your actual model name\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+ "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
+ "\n",
+ "# Sample colloquial Tamil text\n",
+ "texts = [\"சாப்பாடு சூப்பர் டா!\", \"அது நல்லா இல்ல\", \"என்னடா பண்ணிட்டு இருக்க?\", \"இது செம்ம கெத்த\", \"பைத்தியமா நீ?\"]\n",
+ "\n",
+ "# Tokenize the text\n",
+ "inputs = tokenizer(texts, padding=True, truncation=True, return_tensors=\"pt\")\n",
+ "\n",
+ "# ✅ Run inference\n",
+ "with torch.no_grad():\n",
+ " outputs = model(**inputs)\n",
+ " logits = outputs.logits\n",
+ "\n",
+ "# Convert logits to class predictions\n",
+ "predictions = torch.argmax(logits, dim=-1)\n",
+ "print(\"Predicted classes:\", predictions)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "xeSYQCo2It0y",
+ "outputId": "18456e9c-e275-4f37-a1fd-c81754874c03"
+ },
+ "execution_count": 48,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Predicted classes: tensor([1, 1, 1, 1, 1])\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from sklearn.metrics import accuracy_score\n",
+ "\n",
+ "# Example: Assume these are the actual labels\n",
+ "true_labels = [1, 0, 2, 1, 0] # Change this based on your dataset\n",
+ "\n",
+ "# Compute accuracy\n",
+ "accuracy = accuracy_score(true_labels, predictions.numpy())\n",
+ "print(f\"Model Accuracy: {accuracy * 100:.2f}%\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "C77hCyhzI0SO",
+ "outputId": "7d540bed-68a8-4d19-9bd0-9dd59511d9af"
+ },
+ "execution_count": 49,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Model Accuracy: 40.00%\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import torch.nn.functional as F\n",
+ "import math\n",
+ "\n",
+ "# Get probabilities from logits\n",
+ "log_probs = F.log_softmax(logits, dim=-1)\n",
+ "ppl = math.exp(-log_probs.mean().item())\n",
+ "\n",
+ "print(f\"Model Perplexity: {ppl:.2f}\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "9giLaZEYI2Er",
+ "outputId": "abbdc764-00c8-4c90-bafa-f586210fbfa0"
+ },
+ "execution_count": 50,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Model Perplexity: 2.00\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install sacrebleu\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "1zr6LOmJJLNo",
+ "outputId": "873f7c06-94f8-48ca-930f-c34b104d2699"
+ },
+ "execution_count": 52,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting sacrebleu\n",
+ " Downloading sacrebleu-2.5.1-py3-none-any.whl.metadata (51 kB)\n",
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+ "\u001b[?25hCollecting portalocker (from sacrebleu)\n",
+ " Downloading portalocker-3.1.1-py3-none-any.whl.metadata (8.6 kB)\n",
+ "Requirement already satisfied: regex in /usr/local/lib/python3.11/dist-packages (from sacrebleu) (2024.11.6)\n",
+ "Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.11/dist-packages (from sacrebleu) (0.9.0)\n",
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from sacrebleu) (1.26.4)\n",
+ "Collecting colorama (from sacrebleu)\n",
+ " Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)\n",
+ "Requirement already satisfied: lxml in /usr/local/lib/python3.11/dist-packages (from sacrebleu) (5.3.1)\n",
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+ "\u001b[?25hDownloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
+ "Downloading portalocker-3.1.1-py3-none-any.whl (19 kB)\n",
+ "Installing collected packages: portalocker, colorama, sacrebleu\n",
+ "Successfully installed colorama-0.4.6 portalocker-3.1.1 sacrebleu-2.5.1\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from sacrebleu import corpus_bleu\n",
+ "\n",
+ "# Example generated text from model\n",
+ "generated_texts = [\"சாப்பாடு சூப்பர் டா!\", \"அது நல்லா இல்ல\", \"என்ன பண்ணிட்டு இருக்க?\", \"இது செம்ம!\", \"பைத்தியம் நீ?\"]\n",
+ "\n",
+ "# Actual ground truth sentences\n",
+ "references = [[\"சாப்பாடு சூப்பர்\", \"சாப்பாடு செம்ம\"], [\"அது நல்லது இல்லை\"], [\"என்னடா பண்ணிக்கிட்டு இருக்க?\", \"என்ன செய்ற\"], [\"இது செம்ம கெத்த\"], [\"நீ பைத்தியமா?\"]]\n",
+ "\n",
+ "# Compute BLEU score\n",
+ "bleu = corpus_bleu(generated_texts, references)\n",
+ "print(f\"Model BLEU Score: {bleu.score:.2f}\")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "TyCdUAtmI6Mc",
+ "outputId": "3e021474-a577-4806-862a-065eee038fbf"
+ },
+ "execution_count": 53,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Model BLEU Score: 31.95\n"
+ ]
+ }
+ ]
+ }
+ ]
+}
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