{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "FlUZEghpZy-v" }, "source": [ "### In this section, we will learn how to finetune LLMs for Q/A tasks using HuggingFace 🤗\n", "### First, let's install HuggingFace libraries\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2GZnvNB5KJ_c", "outputId": "4a0f294e-a791-469b-9ccf-1e4f2e54535e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.48.2)\n", "Collecting datasets\n", " Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n", "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.5.1+cu124)\n", "Requirement already satisfied: peft in /usr/local/lib/python3.11/dist-packages (0.14.0)\n", "Requirement already satisfied: accelerate in /usr/local/lib/python3.11/dist-packages (1.3.0)\n", "Collecting 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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: fsspec\n", " Found existing installation: fsspec 2024.10.0\n", " Uninstalling fsspec-2024.10.0:\n", " Successfully uninstalled fsspec-2024.10.0\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", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed bitsandbytes-0.45.2 datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 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" ] } ], "source": [ "!pip install transformers datasets torch peft accelerate bitsandbytes" ] }, { "cell_type": "markdown", "metadata": { "id": "T7TODCfO5_fh" }, "source": [ "### Steps in finetuning LLMs model for Q/A task\n", "\n", "- Datasets\n", "- Model choosing\n", "- Vocabulary, Vectorizer\n", "- Training loop\n", "- Evaluation on the test data" ] }, { "cell_type": "markdown", "metadata": { "id": "ArIsaSOr7MFL" }, "source": [ "### Datasets\n", "\n", "We'll be choosing a simple Vietnamese Q/A dataset to finetune our LLM." ] }, { "cell_type": "markdown", "metadata": { "id": "ZeqWNfxoQR22" }, "source": [ "In this case, we'll be using the **UIT-ViQuAD** dataset.\n", "\n", "In Hugging Face, this dataset is available under the identifier ``taidng/UIT-ViQuAD2.0``. This version includes over 23,000 question-answer pairs from the original dataset, along with an additional 12,000 unanswerable questions" ] }, { "cell_type": "markdown", "metadata": { "id": "D_AXPu57bzUZ" }, "source": [ "### Choosing model to finetune." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "referenced_widgets": [ "38ebcdb0809d408196f9dbee5a11fae5", "77320600acdf465aaa2789a7447d89a5", "a7f0b664e34b4271a7a5a858bf488f8e", "3af9a1bdf72041598c83691b051fb208", "5b0bce8456bf43b3bf8aaad48afe73a3", "d027aa251dd84ad4be2a889db0b326b0", "fe80d256a0a64b3384623e9cbbfbddd5", "a3f398611bfe4bdeae0b4c57dcb98fec", "7d123e411e3f4c108fff9f391f9dbbd8", "2a87bd8615144b1fac3e3460d5dd8534", "ceaec4dadb2e44da9f54f7dc3f97f176", "d817bd238c0348e28ecaa77550ec2246", "4a13474bc8da4e1bbae5a24607ab8cdc", "f1955bb910f147abb3fbd15d2e9f51ae", "9777876122504ef0ae602ec87ad37398", "4d47e918d58f462e9ba73b7a4cb29db4", "32322f08a1e74df6bd97010532afeba6", "e3481a9653124125a687f4dd6123902a", "f263b68454c240ddbec6cba3efe4d70c", "14f258a9142548c09c895386dadbedab", "d7750228b89345d5bf4939ab54ff3d6e", "ec2cf53ec8a7424dbcae33d67e8edf05", "d94151886ddf4c9d930fa68e1a836cd6", "398a646b3a1c4346923d0c6ac087b2c4", "14d6dd4cedfb4d779d86a6a0e6f2eb09", "697bff71045f4d3fae34fd317df9d281", "039bff15a54d4b288e2b5973ed751cad", "2c2007d383aa4e6abaa941d6e2c9dce6", "424ba1ad9ff7410e886bc9f7c35ebe7e", "897732f78e6a46e09022729eb8d84359", "074405c5a7494880852fb5472b863896", "602de73ba2a8407d8ec01f87ca99a75e", "779de7394eba4d69b2c056a1a6c9f677", "8faca16729f0414ea4975b867f3e9700", "da4d389971c84f00be1eb5565a424378", "f22ff8802b8e4af584f38d08c8570402", "fbf673a8b54d41f1a9e9134dc993067b", "5acc1f7b50fb4f4ca0f3df168734fbf4", "174a7b02200741fbbcd4c0643047a9c0", "40f6f5621ad649f89fd79f70d05a90f5", "43c989b7bf7a45379b1f6f9684f8cb58", "84e47e2db34c43b9b28684a067ec9626", "5f75f8c3365c4f3783722bbb14e0d5ed", "9883923d63a94b40aaa903fcb4d4ff88", "ed824093c16b4e7f83ad9f64876bb3aa", "f1e06d8b8d8d4036ac4ee1806934437e", "abb179cc94c74f3ba27aa29f4abbb6e0", "261c09e9221f4fffbdae92e5d7c0e511", "30c3cfc8e1e243628e3dbff427e41e23", "ffc435ff8c264baa805b83ccc7d6c65b", "91ba18e09f7f46a691bd3f8cb01fb25e", "f2969b79fc864bb092631fa60cc34ab4", "81cc849a185c4bf7bd955685a26df308", "b41d1651f903438b965fcf0abfb9610b", "eee9ec3ec69c449abc5903208f78dc56", "9706debf54a84eb8b43340e33d836116", "aa584444405a42498ef4124df62cd07a", "d51ecc203e3941329abdff459073d1a0", "bd31a44a39e54ae59e36788a0c091c7d", "fb3e1be4e0574d038d93a5a8360cdab6", "875fbafa11f14cb2ab57517837774a89", "1436b360477a4d0eb929a62a2595586b", "f55084d8cf5e42bfbbecb6a45ca9b95d", "05fdf80c8c7847458ef7a0aa00393a76", "5156d46c2fc14dee9028955fdc567dc5", "478a2be60b024e3f9debf0094b1aae42", "de4b32b06a0347a293c7af036d0c8362", "4c64b63a04a04426ade8103b7edfcb6e", "f9119df2f0f94c18953a7fcfb134bd98", "946794acc5c042bbb16d664ac1c397c3", "56f6bdb8e03c4973ad580592c15180e6", "9985d83811ba47a68086190d90752b29", "71d1db133ace4eaeb4abc3efabef38ce", "f0b64fa1b8654b79b296c4ea1bd497e6", "e522dcac0e404f458b7993863eedd490", "7b72f088f12140ecbfced657e29b1b94", "7e23369e936a4296a330a543ef9811a0" ], "base_uri": "https://localhost:8080/", "height": 368 }, "id": "TlvkajEzNsug", "outputId": "8dbdf87b-80c7-433f-f129-1e935684e420" }, "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": [ "README.md: 0%| | 0.00/6.04k [00:00 Tôi thật đẹp trai \n" ] } ], "source": [ "# Translating into machine vocabs\n", "encoded_string = tokenizer(\"Tôi thật đẹp trai\")\n", "print(encoded_string)\n", "decoded_string = tokenizer.decode(encoded_string['input_ids'])\n", "print(decoded_string)" ] }, { "cell_type": "markdown", "metadata": { "id": "tjpOHw1atmng" }, "source": [ "Now let's use this for our dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "b5fa1ebc6f684492a92e67ef9a408477", "f9ddfda29aeb46a6963cef9091e826f7", "a3a2a5d110d4443ba958d9f1fede1eb2", "640eca2cbb7040849dee376d5c35240f", "dfa8926687b740be89622c382f470f85", "ab04d258aa2e486bbcc1d9a918cf1eb3", "0bb33f443a5e46088da0eb27c06cdf74", "18d464ba84ab4632a7eb743b80f45bdf", "0c188407c6774c0a9633aa752873df01", "7e30ae29591247318158b1295c0f12da", "4bb519144ebf42348641bd0b0db37bdf", "20cbf47bd1cc4eb29a3e7c1efd20a150", "2ea9afa6175b4884ae71b418672e9344", "9de37a22ff9e4315b4d116cb77c59954", "3be1906bdc5a49a5908d82b3276b2931", "67f58ff9612d4f33875b6b58c8f23bbd", "a16cc0792cef44dfa9284058166927b8", "c45553af8fe849b4a8fc96233f2f78be", "1a774cd40fc641008e4d6d43a02a3fa4", "68e20a5e74734e94b6e29f2c7e2dd262", "7990d4222c044ff5b72c4f826f10fc01", "73f6d8c6f8264f63afa78e37fb8b0590" ] }, "id": "GPpjsC-vn-77", "outputId": "fe24411d-ac59-43d9-955b-e095607425cf" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/28454 [00:00 Tên gọi nào được Phạm Văn Đồng sử dụng khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm? Phạm Văn Đồng (1 tháng 3 năm 1906 – 29 tháng 4 năm 2000) là Thủ tướng đầu tiên của nước Cộng hòa Xã hội chủ nghĩa Việt Nam từ năm 1976 (từ năm 1981 gọi là Chủ tịch Hội đồng Bộ trưởng) cho đến khi nghỉ hưu năm 1987. Trước đó ông từng giữ chức vụ Thủ tướng Chính phủ Việt Nam Dân chủ Cộng hòa từ năm 1955 đến năm 1976. Ông là vị Thủ tướng Việt Nam tại vị lâu nhất (1955–1987). Ông là học trò, cộng sự của Chủ tịch Hồ Chí Minh. Ông có tên gọi thân mật là Tô, đây từng là bí danh của ông. Ông còn có tên gọi là Lâm Bá Kiệt khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm (Chủ nhiệm là Hồ Học Lãm). \n", "Lâm Bá Kiệt\n", "\n", " Phạm Văn Đồng giữ chức vụ gì trong bộ máy Nhà nước Cộng hòa Xã hội chủ nghĩa Việt Nam? Phạm Văn Đồng (1 tháng 3 năm 1906 – 29 tháng 4 năm 2000) là Thủ tướng đầu tiên của nước Cộng hòa Xã hội chủ nghĩa Việt Nam từ năm 1976 (từ năm 1981 gọi là Chủ tịch Hội đồng Bộ trưởng) cho đến khi nghỉ hưu năm 1987. Trước đó ông từng giữ chức vụ Thủ tướng Chính phủ Việt Nam Dân chủ Cộng hòa từ năm 1955 đến năm 1976. Ông là vị Thủ tướng Việt Nam tại vị lâu nhất (1955–1987). Ông là học trò, cộng sự của Chủ tịch Hồ Chí Minh. Ông có tên gọi thân mật là Tô, đây từng là bí danh của ông. Ông còn có tên gọi là Lâm Bá Kiệt khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm (Chủ nhiệm là Hồ Học Lãm). \n", "Thủ tướng\n", "\n", " Giai đoạn năm 1955-1976, Phạm Văn Đồng nắm giữ chức vụ gì? Phạm Văn Đồng (1 tháng 3 năm 1906 – 29 tháng 4 năm 2000) là Thủ tướng đầu tiên của nước Cộng hòa Xã hội chủ nghĩa Việt Nam từ năm 1976 (từ năm 1981 gọi là Chủ tịch Hội đồng Bộ trưởng) cho đến khi nghỉ hưu năm 1987. Trước đó ông từng giữ chức vụ Thủ tướng Chính phủ Việt Nam Dân chủ Cộng hòa từ năm 1955 đến năm 1976. Ông là vị Thủ tướng Việt Nam tại vị lâu nhất (1955–1987). Ông là học trò, cộng sự của Chủ tịch Hồ Chí Minh. Ông có tên gọi thân mật là Tô, đây từng là bí danh của ông. Ông còn có tên gọi là Lâm Bá Kiệt khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm (Chủ nhiệm là Hồ Học Lãm). \n", "Thủ tướng đầu tiên của nước Cộng hòa Xã hội\n", "\n", " Tên gọi nào được Phạm Văn Đồng sử dụng trước khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm? Phạm Văn Đồng (1 tháng 3 năm 1906 – 29 tháng 4 năm 2000) là Thủ tướng đầu tiên của nước Cộng hòa Xã hội chủ nghĩa Việt Nam từ năm 1976 (từ năm 1981 gọi là Chủ tịch Hội đồng Bộ trưởng) cho đến khi nghỉ hưu năm 1987. Trước đó ông từng giữ chức vụ Thủ tướng Chính phủ Việt Nam Dân chủ Cộng hòa từ năm 1955 đến năm 1976. Ông là vị Thủ tướng Việt Nam tại vị lâu nhất (1955–1987). Ông là học trò, cộng sự của Chủ tịch Hồ Chí Minh. Ông có tên gọi thân mật là Tô, đây từng là bí danh của ông. Ông còn có tên gọi là Lâm Bá Kiệt khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm (Chủ nhiệm là Hồ Học Lãm). \n", "\n", "\n", " Hồ Học Lãm giữ chức vụ gì trong bộ máy Nhà nước Cộng hòa Xã hội chủ nghĩa Việt Nam? Phạm Văn Đồng (1 tháng 3 năm 1906 – 29 tháng 4 năm 2000) là Thủ tướng đầu tiên của nước Cộng hòa Xã hội chủ nghĩa Việt Nam từ năm 1976 (từ năm 1981 gọi là Chủ tịch Hội đồng Bộ trưởng) cho đến khi nghỉ hưu năm 1987. Trước đó ông từng giữ chức vụ Thủ tướng Chính phủ Việt Nam Dân chủ Cộng hòa từ năm 1955 đến năm 1976. Ông là vị Thủ tướng Việt Nam tại vị lâu nhất (1955–1987). Ông là học trò, cộng sự của Chủ tịch Hồ Chí Minh. Ông có tên gọi thân mật là Tô, đây từng là bí danh của ông. Ông còn có tên gọi là Lâm Bá Kiệt khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm (Chủ nhiệm là Hồ Học Lãm). \n", "\n", "\n" ] } ], "source": [ "for i in range(5):\n", " print(tokenizer.decode(tokenized_dataset['train'][i]['input_ids']))\n", " start = tokenized_dataset['train'][i]['start_positions']\n", " end = tokenized_dataset['train'][i]['end_positions']\n", " print(tokenizer.decode(tokenized_dataset['train'][i]['input_ids'][start:end+1]))\n", " print(\"\")" ] }, { "cell_type": "markdown", "metadata": { "id": "93wfqRcaDGXE" }, "source": [ "Now, we need a way to load our data into the model for each batch.\n", "\n", "In this case, we'll use ``DataLoader`` from torch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fXEfIq6BDFL9", "outputId": "c3237984-7099-43ef-e7ab-f4a1dc706b5a" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'input_ids': torch.Size([8, 256]),\n", " 'attention_mask': torch.Size([8, 256]),\n", " 'start_positions': torch.Size([8]),\n", " 'end_positions': torch.Size([8])}" ] }, "metadata": {}, "execution_count": 13 } ], "source": [ "import torch\n", "from torch.utils.data import DataLoader\n", "from transformers import default_data_collator\n", "\n", "# First, let's set the format of our datasets to PyTorch\n", "tokenized_dataset.set_format('torch')\n", "\n", "# Define batch size\n", "batch_size = 8\n", "\n", "# Create dataloaders for each split\n", "train_dataloader = DataLoader(\n", " tokenized_dataset[\"train\"],\n", " shuffle=True,\n", " batch_size=batch_size,\n", " collate_fn=default_data_collator\n", ")\n", "\n", "eval_dataloader = DataLoader(\n", " tokenized_dataset[\"validation\"],\n", " shuffle=False,\n", " batch_size=batch_size,\n", " collate_fn=default_data_collator\n", ")\n", "\n", "for batch in train_dataloader:\n", " break\n", "\n", "{k: v.shape for k, v in batch.items()}\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "95sNx4lZOlW0", "outputId": "65855c87-3fb5-4f6d-df9f-f4e705344f50" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "QuestionAnsweringModelOutput(loss=tensor(5.5407, grad_fn=), start_logits=tensor([[ 0.1397, -0.1169, 0.1776, ..., -0.3804, -0.3804, -0.3804],\n", " [-0.1584, -0.2989, 0.0334, ..., 0.1870, -0.0650, -0.0820],\n", " [ 0.0149, -0.0096, -0.2407, ..., -0.2507, -0.2507, -0.2507],\n", " ...,\n", " [-0.0521, -0.2565, -0.3789, ..., -0.0603, -0.1941, -0.0440],\n", " [ 0.1136, 0.0829, -0.3910, ..., 0.2426, 0.4847, 0.1718],\n", " [ 0.0746, 0.0500, -0.2106, ..., -0.3196, -0.3196, -0.3196]],\n", " grad_fn=), end_logits=tensor([[-0.1534, -0.2662, 0.3161, ..., -0.0379, -0.0379, -0.0379],\n", " [-0.0637, -0.1978, 0.4623, ..., 0.1602, -0.0075, -0.1060],\n", " [ 0.1163, -0.1961, 0.1324, ..., 0.0805, 0.0805, 0.0805],\n", " ...,\n", " [ 0.1294, 0.0033, 0.4598, ..., 0.3093, -0.0456, 0.1031],\n", " [ 0.1094, -0.0109, -0.0307, ..., 0.1556, 0.2610, 0.1068],\n", " [ 0.2712, -0.0949, 0.1981, ..., 0.0832, 0.0832, 0.0832]],\n", " grad_fn=), hidden_states=None, attentions=None)" ] }, "metadata": {}, "execution_count": 14 } ], "source": [ "outputs = model(**batch)\n", "outputs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bTIHHCFPLfqF", "outputId": "3232f1e7-d05b-450d-ddcc-7ac1e9123556" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "tensor(5.5407, grad_fn=) torch.Size([8, 256]) torch.Size([8, 256])\n" ] } ], "source": [ "print(outputs.loss, outputs.start_logits.shape, outputs.end_logits.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "jsKcY2jcTdk6" }, "source": [ "### Now let's try to finetune our model\n" ] }, { "cell_type": "markdown", "metadata": { "id": "CDkN6iHIfoMQ" }, "source": [ "To test if the model really training, one fast way to check is trying to overfit a single batch. We'll try using the AdamW optimizer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QzJE0CetfqA9" }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "KgDqBzo8f10n" }, "source": [ "Looks good, now let's setup our model again (cause the last one we used for tested)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m2yzA7_0gNPH" }, "outputs": [], "source": [ "from transformers import AutoModelForQuestionAnswering\n", "\n", "model_name = \"vinai/phobert-base\"\n", "model = AutoModelForQuestionAnswering.from_pretrained(model_name)" ] }, { "cell_type": "markdown", "metadata": { "id": "_eTXWuYKfnNf" }, "source": [ "In this cause we'll try to fine-tune for 1 epochs with AdamW optimizer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uhJI4xWIT3CS", "outputId": "4add1628-e42f-4879-f82e-37f18ee2ae2d" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "3557" ] }, "metadata": {}, "execution_count": 16 } ], "source": [ "len(train_dataloader)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "G10EL6p6MFgM", "outputId": "9f27fa14-f046-45ff-e247-52daafdf01a3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "3557\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " warnings.warn(\n" ] } ], "source": [ "from transformers import AdamW\n", "from transformers import get_scheduler\n", "\n", "optimizer = AdamW(model.parameters(), lr=5e-5)\n", "num_epochs = 1\n", "num_training_steps = num_epochs * len(train_dataloader)\n", "lr_scheduler = get_scheduler(\n", " \"linear\",\n", " optimizer=optimizer,\n", " num_warmup_steps=0,\n", " num_training_steps=num_training_steps,\n", ")\n", "print(num_training_steps)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uyr30-1RLjrh", "outputId": "78e7687f-c5de-439f-93b7-3e806371d8ff" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "RobertaForQuestionAnswering(\n", " (roberta): RobertaModel(\n", " (embeddings): RobertaEmbeddings(\n", " (word_embeddings): Embedding(64001, 768, padding_idx=1)\n", " (position_embeddings): Embedding(258, 768, padding_idx=1)\n", " (token_type_embeddings): Embedding(1, 768)\n", " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (encoder): RobertaEncoder(\n", " (layer): ModuleList(\n", " (0-11): 12 x RobertaLayer(\n", " (attention): RobertaAttention(\n", " (self): RobertaSdpaSelfAttention(\n", " (query): Linear(in_features=768, out_features=768, bias=True)\n", " (key): Linear(in_features=768, out_features=768, bias=True)\n", " (value): Linear(in_features=768, out_features=768, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (output): RobertaSelfOutput(\n", " (dense): Linear(in_features=768, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (intermediate): RobertaIntermediate(\n", " (dense): Linear(in_features=768, out_features=3072, bias=True)\n", " (intermediate_act_fn): GELUActivation()\n", " )\n", " (output): RobertaOutput(\n", " (dense): Linear(in_features=3072, out_features=768, bias=True)\n", " (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " )\n", " )\n", " (qa_outputs): Linear(in_features=768, out_features=2, bias=True)\n", ")" ] }, "metadata": {}, "execution_count": 18 } ], "source": [ "import torch\n", "\n", "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", "model.to(device)" ] }, { "cell_type": "markdown", "metadata": { "id": "VToXGcF7UhtQ" }, "source": [ "We also need something to follow our progress, ``tqdm`` module is a great one to use\n", "\n", "Let's combine this with our training loop" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "referenced_widgets": [ "4089382075a24b6d8fab5738394ba076" ] }, "id": "2YazV2HCUquk", "outputId": "48e42a54-ebde-45db-cb2f-79ce13111ef4" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/3557 [00:00=2.0.0 in /usr/local/lib/python3.11/dist-packages (from evaluate) (3.2.0)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from evaluate) (1.26.4)\n", "Requirement already satisfied: dill in /usr/local/lib/python3.11/dist-packages (from evaluate) (0.3.8)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from evaluate) (2.2.2)\n", "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.11/dist-packages (from evaluate) (2.32.3)\n", "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.11/dist-packages (from evaluate) (4.67.1)\n", "Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from evaluate) (3.5.0)\n", 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Args:\n", " batch: Current batch from dataloader\n", " model: The QA model\n", " tokenizer: Tokenizer for decoding predictions\n", " dataset: The validation dataset containing ground truth answers\n", " device: torch device for computation\n", " \"\"\"\n", " inputs = {\n", " \"input_ids\": batch[\"input_ids\"].to(device),\n", " \"attention_mask\": batch[\"attention_mask\"].to(device),\n", " }\n", "\n", " # Run inference\n", " with torch.no_grad():\n", " outputs = model(**inputs)\n", "\n", " start_logits = outputs.start_logits.cpu().numpy()\n", " end_logits = outputs.end_logits.cpu().numpy()\n", "\n", " predictions = []\n", " references = []\n", "\n", " for i in range(len(batch[\"input_ids\"])):\n", " start_idx = np.argmax(start_logits[i])\n", " end_idx = np.argmax(end_logits[i])\n", "\n", " # Ensure end_idx is not before start_idx\n", " if end_idx < start_idx:\n", " end_idx = start_idx\n", "\n", " # Decode answer from token IDs\n", " pred_answer = tokenizer.decode(batch[\"input_ids\"][i][start_idx:end_idx + 1], skip_special_tokens=True)\n", " pred_answer = pred_answer.strip()\n", "\n", " # If prediction is empty, use a placeholder\n", " if not pred_answer:\n", " pred_answer = \"no answer\"\n", "\n", " # Get example ID - if not available, use index as string\n", " example_id = str(batch[\"example_id\"][i]) if \"example_id\" in batch else str(i)\n", "\n", " # Format prediction\n", " prediction = {\n", " \"id\": example_id,\n", " \"prediction_text\": pred_answer\n", " }\n", "\n", " # Get and validate ground truth answers\n", " answers = dataset['validation']['answers'][i]\n", " answer_texts = []\n", "\n", " # Handle different answer formats\n", " if isinstance(answers, dict):\n", " if 'text' in answers:\n", " answer_texts = answers['text'] if isinstance(answers['text'], list) else [answers['text']]\n", " elif isinstance(answers, list):\n", " # If it's a list of answer dictionaries\n", " answer_texts = [ans['text'] for ans in answers if 'text' in ans]\n", " if not answer_texts and answers: # If answers exist but not in expected format\n", " answer_texts = [str(ans) for ans in answers] # Convert answers to strings\n", "\n", " # Ensure we have at least one answer\n", " if not answer_texts:\n", " answer_texts = [\"no answer\"]\n", "\n", " # Format reference\n", " reference = {\n", " \"id\": example_id,\n", " \"answers\": {\n", " \"text\": answer_texts,\n", " \"answer_start\": [0] * len(answer_texts) # Placeholder starts\n", " }\n", " }\n", "\n", " predictions.append(prediction)\n", " references.append(reference)\n", "\n", " return predictions, references\n", "\n", "def evaluate_model(model, eval_dataloader, tokenizer, dataset, device, batch_size=16):\n", " \"\"\"\n", " Evaluate the entire dataset using the SQuAD metric.\n", " \"\"\"\n", " metric = evaluate.load(\"squad\")\n", " model.eval()\n", "\n", " all_predictions = []\n", " all_references = []\n", "\n", " for batch_idx, batch in enumerate(eval_dataloader):\n", " try:\n", " batch_predictions, batch_references = evaluate_batch(batch, model, tokenizer, dataset, device)\n", " all_predictions.extend(batch_predictions)\n", " all_references.extend(batch_references)\n", " except Exception as e:\n", " print(f\"Error processing batch {batch_idx}: {str(e)}\")\n", " continue\n", "\n", " if not all_predictions or not all_references:\n", " raise ValueError(\"No valid predictions or references were generated\")\n", "\n", " # Compute final scores\n", " final_score = metric.compute(predictions=all_predictions, references=all_references)\n", " return final_score\n", "\n", "final_score = evaluate_model(\n", " model=model,\n", " eval_dataloader=eval_dataloader,\n", " tokenizer=tokenizer,\n", " dataset=dataset,\n", " device=device\n", ")\n", "print(f\"Evaluation Results: {final_score}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MvDwyswx_NtG", "outputId": "4d5ccc15-e2a1-4c9d-c9a4-81bafde80c49" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Evaluation Results: {'exact_match': 12.506554798112218, 'f1': 12.506554798112218}\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "JTyZat-6wwzh" }, "source": [ "### Test with the test split or inference (Bonus)" ] }, { "cell_type": "markdown", "metadata": { "id": "eZ6UsFePdq3_" }, "source": [ "### How can we improve finetuning?\n", "- Use Accelerator for distributed training\n", "- Maybe try to use LoRA\n", "- Gradient accumulation\n", "- etc" ] }, { "cell_type": "markdown", "metadata": { "id": "pdptBysJwMxT" }, "source": [ "### References\n", "\n", "- HuggingFace NLP course: https://huggingface.co/learn/nlp-course/chapter1/1\n", "- HuggingFace Q/A: https://huggingface.co/docs/transformers/tasks/question_answering" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, 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