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Fine-tuning BERT | Token classification (PyTorch)
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/Daethyra/f51448bd30a56f13dfc6e129cf8386ed/copy-of-token-classification-pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "o3dp2e3GXyFT" | |
| }, | |
| "source": [ | |
| "# Token classification (PyTorch)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "8n9vuCx2XyFX" | |
| }, | |
| "source": [ | |
| "Install the Transformers, Datasets, and Evaluate libraries to run this notebook." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "id": "b8uRB7v5XyFY", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "b8ebfe9e-fdaf-455f-f425-c66a26ba4664" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting datasets\n", | |
| " Downloading datasets-2.14.5-py3-none-any.whl (519 kB)\n", | |
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| "\u001b[?25hCollecting evaluate\n", | |
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| " Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n", | |
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| "Installing collected packages: tokenizers, sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n", | |
| "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.17.3 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.33.3 xxhash-3.3.0\n", | |
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| "Installing collected packages: accelerate\n", | |
| "Successfully installed accelerate-0.23.0\n", | |
| "\u001b[31mERROR: torch_xla-1.9-cp37-cp37m-linux_x86_64.whl is not a supported wheel on this platform.\u001b[0m\u001b[31m\n", | |
| "Reading package lists... Done\n", | |
| "Building dependency tree... Done\n", | |
| "Reading state information... Done\n", | |
| "git-lfs is already the newest version (3.0.2-1ubuntu0.2).\n", | |
| "0 upgraded, 0 newly installed, 0 to remove and 18 not upgraded.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!pip install datasets evaluate transformers[sentencepiece]\n", | |
| "!pip install accelerate\n", | |
| "# To run the training on TPU, you will need to uncomment the following line:\n", | |
| "!pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n", | |
| "!apt install git-lfs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "TJBP3TTpXyFZ" | |
| }, | |
| "source": [ | |
| "You will need to setup git, adapt your email and name in the following cell." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "id": "wZalGQDPXyFa" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "!git config --global user.email \"dev-daethyra@protonmail.com\"\n", | |
| "!git config --global user.name \"Daethyra\"" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "vXcAzco0XyFa" | |
| }, | |
| "source": [ | |
| "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "id": "4_W4aKyGXyFb", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 145, | |
| "referenced_widgets": [ | |
| "23db3a1dee8743ae9d7f5a8d2844c4e8", | |
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| "ae4bd1fee603412f9bd62c5325f4ee83", | |
| "40380c7f7aad4698ac696c9ba447d20e", | |
| "2ab61029d04e4043b93fa4add864df1c", | |
| "c4faeba2bbe44f8d905dd7e7cd8d92e2", | |
| "dd5a37e2437446ea9248ebdcf11c4ab3", | |
| "0a9a013c1921462eb1eba0d1eba6adb7", | |
| "6a5d26d1e8d641f78579a69da6dfdf8e", | |
| "6530f0317dd743429aacf66997be4e9f", | |
| "c17f5d38a45c4629a3635068efd075b8", | |
| "8448c599265c4e6ca2ce2961a0fb7394", | |
| "c00be9853b354bda97c8a5f6f4d2ec42", | |
| "e0a57da7f7054b3bad06434a5e8a575a", | |
| "a5b271dfd0204d98bde84585c27adad9", | |
| "1ef749a0ced846438edebd3836ed1940", | |
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| "2e3998f348b84664b1be32913302ad1d", | |
| "5634f8ee2ccc45b391ab0afe54a67de4", | |
| "cb5f1a185ee240c990595b220fbfb202", | |
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| "092d2cd04d584dce8fd0eeb53dbe177e", | |
| "6c0d379b8c7e4efc9fb7807a7561d877", | |
| "d61c551bb1d4428fa301c56cdcc23e70", | |
| "aaa7969d80c04d5eb35eba984e943bef" | |
| ] | |
| }, | |
| "outputId": "52ceca97-59c7-4eaa-bf15-51c9aef24be6" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "23db3a1dee8743ae9d7f5a8d2844c4e8" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "from huggingface_hub import notebook_login\n", | |
| "\n", | |
| "notebook_login()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "id": "Y7iqqR7YXyFb", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 241, | |
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| ] | |
| }, | |
| "outputId": "82666338-31f0-4c38-d208-1e938be29d61" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| "model_id": "b916b92e00e1410fb2a45bb7390f01e2" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
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| "model_id": "6962d58ef72c46278a3f66647cb38138" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
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| "model_id": "8ab397af0c0b4df4b2508255c0bffae6" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| "application/vnd.jupyter.widget-view+json": { | |
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| "model_id": "33c2374b5cb147a4bb0927047d4d98f6" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| ], | |
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| "version_major": 2, | |
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| "model_id": "06c4932153d848e79ac33bd2fc3577d5" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
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| ], | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| ], | |
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| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "from datasets import load_dataset\n", | |
| "\n", | |
| "raw_datasets = load_dataset(\"conll2003\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "id": "Jv-Y2eSIXyFc", | |
| "outputId": "bfb3e396-524c-4b19-ab63-b86383d5e3a5", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "DatasetDict({\n", | |
| " train: Dataset({\n", | |
| " features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n", | |
| " num_rows: 14041\n", | |
| " })\n", | |
| " validation: Dataset({\n", | |
| " features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n", | |
| " num_rows: 3250\n", | |
| " })\n", | |
| " test: Dataset({\n", | |
| " features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n", | |
| " num_rows: 3453\n", | |
| " })\n", | |
| "})" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 5 | |
| } | |
| ], | |
| "source": [ | |
| "raw_datasets" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "id": "6MN6prf9XyFc", | |
| "outputId": "5ab5fda1-648d-4272-a01e-79dcde97ecab", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 6 | |
| } | |
| ], | |
| "source": [ | |
| "raw_datasets[\"train\"][0][\"tokens\"]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "id": "6WOqRhEOXyFd", | |
| "outputId": "54ab7187-59a7-4435-9c45-e801a1c92f9f", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[3, 0, 7, 0, 0, 0, 7, 0, 0]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ], | |
| "source": [ | |
| "raw_datasets[\"train\"][0][\"ner_tags\"]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "id": "s_M2ZwnJXyFd", | |
| "outputId": "6e9b97bc-a1e1-4376-f974-6dbf4cd2f5a4", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Sequence(feature=ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], id=None), length=-1, id=None)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 8 | |
| } | |
| ], | |
| "source": [ | |
| "ner_feature = raw_datasets[\"train\"].features[\"ner_tags\"]\n", | |
| "ner_feature" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "id": "SAfjBk1-XyFd", | |
| "outputId": "1595786e-ffc6-48d2-b3bd-1a0b0629abd4", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ], | |
| "source": [ | |
| "label_names = ner_feature.feature.names\n", | |
| "label_names" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "id": "iCyHA-bkXyFd", | |
| "outputId": "eeb94e76-2a67-4ebe-b9ee-92bc6296e954", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "EU rejects German call to boycott British lamb . \n", | |
| "B-ORG O B-MISC O O O B-MISC O O \n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "words = raw_datasets[\"train\"][0][\"tokens\"]\n", | |
| "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n", | |
| "line1 = \"\"\n", | |
| "line2 = \"\"\n", | |
| "for word, label in zip(words, labels):\n", | |
| " full_label = label_names[label]\n", | |
| " max_length = max(len(word), len(full_label))\n", | |
| " line1 += word + \" \" * (max_length - len(word) + 1)\n", | |
| " line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n", | |
| "\n", | |
| "print(line1)\n", | |
| "print(line2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "id": "32IiEO4XXyFe", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 145, | |
| "referenced_widgets": [ | |
| "b325b111ad2649f09687265b3fe68b6f", | |
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| "fed0c8487d084876affbf141736d7f87", | |
| "d682a535e9ae4c7e834394963b00e6a4", | |
| "88a2b16c60754db08dab14b884e050e0", | |
| "bafc2d5eef3041d6b8359302af1f4dcf", | |
| "2cddaa5244814113b7a7e083cc6f4e00", | |
| "ae48967f7ad54a3183dc8cd7524f62de", | |
| "58fe3a0d604949eb98356dc4ff62c4c8", | |
| "7151dc91a9834c4d8eb09406dfd734e9", | |
| "80c28853bcb24f038424bf1531680c36", | |
| "07088e6de8464b6bbd4c61048e6f52c8", | |
| "8be76f5ef85a4d1d93b38691b6609f81", | |
| "7f0e0c055c484ea4831a6718d5d4064c", | |
| "46e36a66b41d4d62aa397d52ee11424b", | |
| "4adab11797484022a1b4fd915fce2b54", | |
| "ff5244a163a5485a9b32385b067852c8", | |
| "06e61fc592144db09bbabf7ff0baab74", | |
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| "65ccda27f5f149f487fbb79622e80149", | |
| "92878227004a4049a40552f325af9d8b", | |
| "f6f56ea5f935484f8f001e7b941ed67b", | |
| "ebb7ec935dad4df0ba988a4a5d46468e", | |
| "17924f180c5140a5ad776a731dd703a8", | |
| "cec6774410dd40b385284f66aba2c75e", | |
| "af1410e53b074b1f9f5d25ff52ce612f", | |
| "1b9ac140c5ce4eea8b6e3d2d1927dfd2", | |
| "9df23dab653a422eb6ef1174a40c0bf2", | |
| "86381572a4d0412e8d463260fe2fc25d", | |
| "0351702d013543a4b76326231af3d692", | |
| "4deff942822e4369b7cad6ac280127c7", | |
| "0dce527d7a274a57957abcf0637094f9", | |
| "de58de36ed4c4f168655f8dd560d8d01", | |
| "c4403baba67f4d1e92a6a632afde891c", | |
| "4acce1759f8f49c6a827e6f5d43be8d5", | |
| "53f9b83fc2ac439eb6f506c835df5679", | |
| "1885340e16eb4648860e98b8c6bea393", | |
| "6598a4332ae44fcf9865b032645cf43a", | |
| "1f0f6a277a0d47b68bcb269039ba67e4", | |
| "3b28b2d8b9d9472c9c87c337098961b3", | |
| "7fdb3ff08a344a27bae5ce9880da4d3b", | |
| "59c12800e25c4466ae318b682350588a", | |
| "6c1be445fa404404b7329531de9d2ba2" | |
| ] | |
| }, | |
| "outputId": "f568af9a-0934-45b6-ffaf-77fc3a430688" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading (…)okenizer_config.json: 0%| | 0.00/29.0 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "b325b111ad2649f09687265b3fe68b6f" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading (…)lve/main/config.json: 0%| | 0.00/570 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "80c28853bcb24f038424bf1531680c36" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading (…)solve/main/vocab.txt: 0%| | 0.00/213k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "f6f56ea5f935484f8f001e7b941ed67b" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading (…)/main/tokenizer.json: 0%| | 0.00/436k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "de58de36ed4c4f168655f8dd560d8d01" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import AutoTokenizer\n", | |
| "\n", | |
| "model_checkpoint = \"bert-base-cased\"\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "id": "cA1DNvLbXyFe", | |
| "outputId": "209f50ee-1d46-43f5-cd37-60e7b64fb054", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "True" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 12 | |
| } | |
| ], | |
| "source": [ | |
| "tokenizer.is_fast" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "id": "J79Jeg1_XyFe", | |
| "outputId": "60d20eb9-3337-4489-ab8f-e55fdb0d1706", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['[CLS]',\n", | |
| " 'EU',\n", | |
| " 'rejects',\n", | |
| " 'German',\n", | |
| " 'call',\n", | |
| " 'to',\n", | |
| " 'boycott',\n", | |
| " 'British',\n", | |
| " 'la',\n", | |
| " '##mb',\n", | |
| " '.',\n", | |
| " '[SEP]']" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 13 | |
| } | |
| ], | |
| "source": [ | |
| "inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n", | |
| "inputs.tokens()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "id": "piMSC3GeXyFe", | |
| "outputId": "b8eabbce-322e-469b-f553-60bf75b90766", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[None, 0, 1, 2, 3, 4, 5, 6, 7, 7, 8, None]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 14 | |
| } | |
| ], | |
| "source": [ | |
| "inputs.word_ids()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "id": "2EGOX-qeXyFe" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def align_labels_with_tokens(labels, word_ids):\n", | |
| " new_labels = []\n", | |
| " current_word = None\n", | |
| " for word_id in word_ids:\n", | |
| " if word_id != current_word:\n", | |
| " # Start of a new word!\n", | |
| " current_word = word_id\n", | |
| " label = -100 if word_id is None else labels[word_id]\n", | |
| " new_labels.append(label)\n", | |
| " elif word_id is None:\n", | |
| " # Special token\n", | |
| " new_labels.append(-100)\n", | |
| " else:\n", | |
| " # Same word as previous token\n", | |
| " label = labels[word_id]\n", | |
| " # If the label is B-XXX we change it to I-XXX\n", | |
| " if label % 2 == 1:\n", | |
| " label += 1\n", | |
| " new_labels.append(label)\n", | |
| "\n", | |
| " return new_labels" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "id": "SbaT-8ysXyFf", | |
| "outputId": "d5c673b8-7553-42ca-b7c2-6e4ea3c9bdb0", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[3, 0, 7, 0, 0, 0, 7, 0, 0]\n", | |
| "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n", | |
| "word_ids = inputs.word_ids()\n", | |
| "print(labels)\n", | |
| "print(align_labels_with_tokens(labels, word_ids))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": { | |
| "id": "KYH-NIy_XyFf" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def tokenize_and_align_labels(examples):\n", | |
| " tokenized_inputs = tokenizer(\n", | |
| " examples[\"tokens\"], truncation=True, is_split_into_words=True\n", | |
| " )\n", | |
| " all_labels = examples[\"ner_tags\"]\n", | |
| " new_labels = []\n", | |
| " for i, labels in enumerate(all_labels):\n", | |
| " word_ids = tokenized_inputs.word_ids(i)\n", | |
| " new_labels.append(align_labels_with_tokens(labels, word_ids))\n", | |
| "\n", | |
| " tokenized_inputs[\"labels\"] = new_labels\n", | |
| " return tokenized_inputs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": { | |
| "id": "GwX1ZW5IXyFf", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 113, | |
| "referenced_widgets": [ | |
| "4bdf17e2a58b4782a8409309510160bb", | |
| "ceae894deb4d499c961bd6ab6cf46dd5", | |
| "3ed6bd30543a4a2c91a5e950468fe8ec", | |
| "09f7f3a5d11e47c592c3dcb44a430c8a", | |
| "a1fbbe4422b94863931abb534ba5fa83", | |
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| ] | |
| }, | |
| "outputId": "49a5ecd7-c3dc-4aa0-dfb0-f6e724045702" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Map: 0%| | 0/14041 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "4bdf17e2a58b4782a8409309510160bb" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Map: 0%| | 0/3250 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "00c2ed9286b440dc95edfce292a8c498" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Map: 0%| | 0/3453 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "116009e78aa04fb7888c492912ac147c" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "tokenized_datasets = raw_datasets.map(\n", | |
| " tokenize_and_align_labels,\n", | |
| " batched=True,\n", | |
| " remove_columns=raw_datasets[\"train\"].column_names,\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": { | |
| "id": "x0p5BfHrXyFf" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import DataCollatorForTokenClassification\n", | |
| "\n", | |
| "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": { | |
| "id": "mWpzCNQ1XyFf", | |
| "outputId": "d5e2ff22-425a-47fd-dd69-f843f4bcdd95", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "tensor([[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100],\n", | |
| " [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 20 | |
| } | |
| ], | |
| "source": [ | |
| "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n", | |
| "batch[\"labels\"]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": { | |
| "id": "m4t6MQFUXyFf", | |
| "outputId": "82f917d3-a21f-4e9d-a3d5-f898e904f508", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n", | |
| "[-100, 1, 2, -100]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for i in range(2):\n", | |
| " print(tokenized_datasets[\"train\"][i][\"labels\"])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": { | |
| "id": "crcj83GvXyFf", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "f6a917c9-deac-4910-8e7f-930be2b6568c" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting seqeval\n", | |
| " Downloading seqeval-1.2.2.tar.gz (43 kB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.23.5)\n", | |
| "Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.2.2)\n", | |
| "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.11.2)\n", | |
| "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.3.2)\n", | |
| "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.2.0)\n", | |
| "Building wheels for collected packages: seqeval\n", | |
| " Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16162 sha256=fb819495b0aa6c2ad8d86d2e78ffb51c526a56b108d17ed62dcff7c8c5e4393a\n", | |
| " Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n", | |
| "Successfully built seqeval\n", | |
| "Installing collected packages: seqeval\n", | |
| "Successfully installed seqeval-1.2.2\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!pip install seqeval" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": { | |
| "id": "wU6JJM8zXyFh", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 49, | |
| "referenced_widgets": [ | |
| "460688da4b09439eb127deeff5048dcd", | |
| "a0890ef7170d4358882cc96903883a0f", | |
| "081bcab5892e4e188e9398325f5a28d4", | |
| "81448c5f24c7443fa3b95bfa6517f5f3", | |
| "7a282d2f678c40bfa8037feb4b091437", | |
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| "d75cc1d8b31e4bbabb70d0c799d6bf31", | |
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| ] | |
| }, | |
| "outputId": "edd6468c-80b6-4471-8ea2-2aa3d4d0ff13" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading builder script: 0%| | 0.00/6.34k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "460688da4b09439eb127deeff5048dcd" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "import evaluate\n", | |
| "\n", | |
| "metric = evaluate.load(\"seqeval\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": { | |
| "id": "Ece90tQ-XyFh", | |
| "outputId": "3bea3e79-a655-4287-9967-a8f000dc1694", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 24 | |
| } | |
| ], | |
| "source": [ | |
| "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n", | |
| "labels = [label_names[i] for i in labels]\n", | |
| "labels" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": { | |
| "id": "WkWE241fXyFh", | |
| "outputId": "7d6314fd-52de-45ed-af8b-9c311c46a929", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "{'MISC': {'precision': 1.0,\n", | |
| " 'recall': 0.5,\n", | |
| " 'f1': 0.6666666666666666,\n", | |
| " 'number': 2},\n", | |
| " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n", | |
| " 'overall_precision': 1.0,\n", | |
| " 'overall_recall': 0.6666666666666666,\n", | |
| " 'overall_f1': 0.8,\n", | |
| " 'overall_accuracy': 0.8888888888888888}" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 25 | |
| } | |
| ], | |
| "source": [ | |
| "predictions = labels.copy()\n", | |
| "predictions[2] = \"O\"\n", | |
| "metric.compute(predictions=[predictions], references=[labels])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": { | |
| "id": "w-vb68VDXyFh" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "\n", | |
| "\n", | |
| "def compute_metrics(eval_preds):\n", | |
| " logits, labels = eval_preds\n", | |
| " predictions = np.argmax(logits, axis=-1)\n", | |
| "\n", | |
| " # Remove ignored index (special tokens) and convert to labels\n", | |
| " true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n", | |
| " true_predictions = [\n", | |
| " [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n", | |
| " for prediction, label in zip(predictions, labels)\n", | |
| " ]\n", | |
| " all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n", | |
| " return {\n", | |
| " \"precision\": all_metrics[\"overall_precision\"],\n", | |
| " \"recall\": all_metrics[\"overall_recall\"],\n", | |
| " \"f1\": all_metrics[\"overall_f1\"],\n", | |
| " \"accuracy\": all_metrics[\"overall_accuracy\"],\n", | |
| " }" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": { | |
| "id": "BhSUlSywXyFi" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "id2label = {i: label for i, label in enumerate(label_names)}\n", | |
| "label2id = {v: k for k, v in id2label.items()}" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "metadata": { | |
| "id": "CheIevP0XyFi", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 104, | |
| "referenced_widgets": [ | |
| "0b684635a5e24fdc8b0197abf886e7b5", | |
| "a867b162a1fc45738cda8bd043191912", | |
| "1ebe4e5d08604d779550a067e238a726", | |
| "1675bdde97834bc4ace66322863833e9", | |
| "958c2e2174f04d77a6460c69ffe1034a", | |
| "94341e458acd4111a2f2ab8159480b64", | |
| "8560c224b4154ea88cca089643ee53f0", | |
| "87a5a7fbb9b94b449af5a0d305bb75ff", | |
| "31199d1d1f364246bfba92389255fa27", | |
| "d583e36e28074c94ba91fcf7885e3eb4", | |
| "e1b56b9e4e2e481e8611822e49ea5a55" | |
| ] | |
| }, | |
| "outputId": "e7fe8555-1dad-47bf-d980-1ec5ad1efb9a" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading model.safetensors: 0%| | 0.00/436M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "0b684635a5e24fdc8b0197abf886e7b5" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n", | |
| "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import AutoModelForTokenClassification\n", | |
| "\n", | |
| "model = AutoModelForTokenClassification.from_pretrained(\n", | |
| " model_checkpoint,\n", | |
| " id2label=id2label,\n", | |
| " label2id=label2id,\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "metadata": { | |
| "id": "31PvjAFqXyFi", | |
| "outputId": "6f9ad60d-4407-4827-90c4-7d0d41e42bb2", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "9" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 29 | |
| } | |
| ], | |
| "source": [ | |
| "model.config.num_labels" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "wf95qNHdXyFi" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from huggingface_hub import notebook_login\n", | |
| "\n", | |
| "notebook_login()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "metadata": { | |
| "id": "ldUuDB9oXyFi" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import TrainingArguments\n", | |
| "\n", | |
| "args = TrainingArguments(\n", | |
| " \"bert-finetuned-ner\",\n", | |
| " evaluation_strategy=\"epoch\",\n", | |
| " save_strategy=\"epoch\",\n", | |
| " learning_rate=2e-5,\n", | |
| " num_train_epochs=3,\n", | |
| " weight_decay=0.01,\n", | |
| " push_to_hub=True,\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "metadata": { | |
| "id": "i-NZCKCNXyFi", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 204 | |
| }, | |
| "outputId": "aa277206-eefe-4122-9de1-a2b5b0f2c32a" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div>\n", | |
| " \n", | |
| " <progress value='5268' max='5268' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
| " [5268/5268 10:26, Epoch 3/3]\n", | |
| " </div>\n", | |
| " <table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>Epoch</th>\n", | |
| " <th>Training Loss</th>\n", | |
| " <th>Validation Loss</th>\n", | |
| " <th>Precision</th>\n", | |
| " <th>Recall</th>\n", | |
| " <th>F1</th>\n", | |
| " <th>Accuracy</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>1</td>\n", | |
| " <td>0.076600</td>\n", | |
| " <td>0.076396</td>\n", | |
| " <td>0.908228</td>\n", | |
| " <td>0.934366</td>\n", | |
| " <td>0.921112</td>\n", | |
| " <td>0.979661</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>0.041700</td>\n", | |
| " <td>0.055622</td>\n", | |
| " <td>0.921581</td>\n", | |
| " <td>0.949344</td>\n", | |
| " <td>0.935257</td>\n", | |
| " <td>0.985798</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3</td>\n", | |
| " <td>0.024800</td>\n", | |
| " <td>0.055559</td>\n", | |
| " <td>0.939253</td>\n", | |
| " <td>0.952373</td>\n", | |
| " <td>0.945768</td>\n", | |
| " <td>0.987181</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table><p>" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "TrainOutput(global_step=5268, training_loss=0.06625506057826153, metrics={'train_runtime': 629.7752, 'train_samples_per_second': 66.886, 'train_steps_per_second': 8.365, 'total_flos': 921792849708600.0, 'train_loss': 0.06625506057826153, 'epoch': 3.0})" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 31 | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import Trainer\n", | |
| "\n", | |
| "trainer = Trainer(\n", | |
| " model=model,\n", | |
| " args=args,\n", | |
| " train_dataset=tokenized_datasets[\"train\"],\n", | |
| " eval_dataset=tokenized_datasets[\"validation\"],\n", | |
| " data_collator=data_collator,\n", | |
| " compute_metrics=compute_metrics,\n", | |
| " tokenizer=tokenizer,\n", | |
| ")\n", | |
| "trainer.train()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "metadata": { | |
| "id": "xRZR3f5SXyFi", | |
| "outputId": "95b1e42a-b6c0-420d-958c-100a66317ec4", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'https://huggingface.co/daethyra/bert-finetuned-ner/tree/main/'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 32 | |
| } | |
| ], | |
| "source": [ | |
| "trainer.push_to_hub(commit_message=\"Training complete\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "metadata": { | |
| "id": "JzZCss5bXyFi" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from torch.utils.data import DataLoader\n", | |
| "\n", | |
| "train_dataloader = DataLoader(\n", | |
| " tokenized_datasets[\"train\"],\n", | |
| " shuffle=True,\n", | |
| " collate_fn=data_collator,\n", | |
| " batch_size=8,\n", | |
| ")\n", | |
| "eval_dataloader = DataLoader(\n", | |
| " tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=8\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "metadata": { | |
| "id": "2k2MOBvxXyFj", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "91c65a39-0178-46c4-9766-cdcccdab0d5f" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n", | |
| "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "model = AutoModelForTokenClassification.from_pretrained(\n", | |
| " model_checkpoint,\n", | |
| " id2label=id2label,\n", | |
| " label2id=label2id,\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 35, | |
| "metadata": { | |
| "id": "uhHgXgd8XyFn" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from torch.optim import AdamW\n", | |
| "\n", | |
| "optimizer = AdamW(model.parameters(), lr=2e-5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 36, | |
| "metadata": { | |
| "id": "zVr6pR5dXyFn" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from accelerate import Accelerator\n", | |
| "\n", | |
| "accelerator = Accelerator()\n", | |
| "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n", | |
| " model, optimizer, train_dataloader, eval_dataloader\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "metadata": { | |
| "id": "mk_LE5pkXyFn" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import get_scheduler\n", | |
| "\n", | |
| "num_train_epochs = 3\n", | |
| "num_update_steps_per_epoch = len(train_dataloader)\n", | |
| "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n", | |
| "\n", | |
| "lr_scheduler = get_scheduler(\n", | |
| " \"linear\",\n", | |
| " optimizer=optimizer,\n", | |
| " num_warmup_steps=0,\n", | |
| " num_training_steps=num_training_steps,\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 42, | |
| "metadata": { | |
| "id": "YjT4tPyQXyFn", | |
| "outputId": "eefccade-6ac6-45bc-cc13-131a9869e78d", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'daethyra/bert-finetuned-ner'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 42 | |
| } | |
| ], | |
| "source": [ | |
| "from huggingface_hub import Repository, get_full_repo_name\n", | |
| "\n", | |
| "model_name = \"bert-finetuned-ner\"\n", | |
| "repo_name = get_full_repo_name(model_name)\n", | |
| "repo_name" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 43, | |
| "metadata": { | |
| "id": "ckqZ5kICXyFn", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 180, | |
| "referenced_widgets": [ | |
| "beecc49f7ff949b08b477bede4575659", | |
| "4a9518d68c0f40e1829de7557b74ee55", | |
| "455fa90d2d0646fd8dc709bbae428b11", | |
| "ce4bce70fd7241e59c9ff641f3896771", | |
| "d9986607513844f5bc5f91b93a211686", | |
| "fb03291d98f248678a0cd0f9544e9f09", | |
| "28a8bd58e8274a798ff4909412461335", | |
| "8b601cdab1594fa89cfccff243dd8b5e", | |
| "4017addc92d041e0b0a9827e72339c45", | |
| "356996b8457c455c86c013d066e81d7c", | |
| "d999c3662e284d39852ce4c1a73dc608", | |
| "49c2216c799047d6af1c00963422f5df", | |
| "df60186a46e74b589a9e52799cb28a45", | |
| "797f14c77e4d41999406d6eeb83a5e7c", | |
| "9036498dbcbc4f3db19dbcc647ef07ec", | |
| "68c58f86fe584a45b79df68fc83a6cf1", | |
| "b93aec52299d465bab1dd7f83ca8e2cc", | |
| "b6c408a02d8e473e8b447e4398b065e3", | |
| "0c1c9f369f9a42e7aa65372e461beb38", | |
| "c98011ec1ad54a2d8a52228a11dc9ec6", | |
| "1b35b34540b84b75babfb4defe9d0b08", | |
| "dba3a1cd0783452f97101148022ea65d", | |
| "6158b89ea0a444538dc9f217eba31c4d", | |
| "11a78c17d08d474cbc088c0418cd1734", | |
| "03392a4b900042eab2aaa02c98f565aa", | |
| "5411d2ec750a4330be764dccabec36df", | |
| "39431067057f4a8480e146199977d048", | |
| "6e088d6f65604e3e86ee4733c63c40e1", | |
| "2490ec63176c4fd681435608fd2c07c6", | |
| "3c64d8ac189a4828918b4d5a9d00c13d", | |
| "2806d450b07d4f578df8f5323d72e8b5", | |
| "b3d60e3e27204053a638b71b7b2b022f", | |
| "a05f504bf6e74e6986ca429864ead9bd", | |
| "74e5b6ee40b64fcab38ded0dcbe7e551", | |
| "1e43dca19f8d4cc1ab1e21c4723db981", | |
| "89c679f91e2848c08d80ab7ab0a477a9", | |
| "efc4e2be22fe49ad87545b5c6f305ff8", | |
| "5e7c0c63237f474aabc0e6662e59e384", | |
| "c87a3fa2f6d74ab7915defb5e9d3e3f5", | |
| "ca1fce57e90344d190bb1ad637228b70", | |
| "35c94755177e44628bdcaf3078a8e1c5", | |
| "ce7da8277e674dca9871db086486ef54", | |
| "bc0ac0e24cf9462ea5bbcaec607c3c9f", | |
| "1a9b06f137b342618377ffe677ca9f9c" | |
| ] | |
| }, | |
| "outputId": "b99b85f5-5486-497e-f396-425150d277fb" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Cloning https://huggingface.co/daethyra/bert-finetuned-ner into local empty directory.\n", | |
| "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/daethyra/bert-finetuned-ner into local empty directory.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Download file pytorch_model.bin: 0%| | 15.4k/411M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "beecc49f7ff949b08b477bede4575659" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Download file training_args.bin: 100%|##########| 3.93k/3.93k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "49c2216c799047d6af1c00963422f5df" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Clean file training_args.bin: 25%|##5 | 1.00k/3.93k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "6158b89ea0a444538dc9f217eba31c4d" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Clean file pytorch_model.bin: 0%| | 1.00k/411M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "74e5b6ee40b64fcab38ded0dcbe7e551" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "output_dir = \"bert-finetuned-ner-accelerate\"\n", | |
| "repo = Repository(output_dir, clone_from=repo_name)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 44, | |
| "metadata": { | |
| "id": "IESM5jLJXyFn" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def postprocess(predictions, labels):\n", | |
| " predictions = predictions.detach().cpu().clone().numpy()\n", | |
| " labels = labels.detach().cpu().clone().numpy()\n", | |
| "\n", | |
| " # Remove ignored index (special tokens) and convert to labels\n", | |
| " true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n", | |
| " true_predictions = [\n", | |
| " [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n", | |
| " for prediction, label in zip(predictions, labels)\n", | |
| " ]\n", | |
| " return true_labels, true_predictions" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 45, | |
| "metadata": { | |
| "id": "AKVaNgibXyFo", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 101, | |
| "referenced_widgets": [ | |
| "f5ad799bd4294a58b2ae1f9afba94b04", | |
| "4b0bf7133926470293f22b2cb007b478", | |
| "1eca421d10224328b60101b3d9590bcc", | |
| "2d12237718854c37be198af7aee0bcb9", | |
| "ce9cb04b85004774ae5ca1a74cbc120b", | |
| "74ead699406746668b8fded40f99f71f", | |
| "f5a69636ba014875b711e1edd045c9fc", | |
| "5707be745c084996a7f0043d5d88136f", | |
| "339b4a921369495ba2e49f17c4acf191", | |
| "5d1fa50fc7e641ca9ed7f13b004c1543", | |
| "7c9c81424990442b8215cf2dd0888266" | |
| ] | |
| }, | |
| "outputId": "92942b26-9402-47b4-9d5f-42cd18239ea6" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| " 0%| | 0/5268 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "f5ad799bd4294a58b2ae1f9afba94b04" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "epoch 0: {'precision': 0.9348704140020195, 'recall': 0.9056080860776002, 'f1': 0.9200066247101689, 'accuracy': 0.9818537705304056}\n", | |
| "epoch 1: {'precision': 0.9434533826994278, 'recall': 0.9256935270805813, 'f1': 0.9344890815135856, 'accuracy': 0.9846058750809442}\n", | |
| "epoch 2: {'precision': 0.9498485358465163, 'recall': 0.9275267050123254, 'f1': 0.9385549181009396, 'accuracy': 0.9864308000235474}\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from tqdm.auto import tqdm\n", | |
| "import torch\n", | |
| "\n", | |
| "progress_bar = tqdm(range(num_training_steps))\n", | |
| "\n", | |
| "for epoch in range(num_train_epochs):\n", | |
| " # Training\n", | |
| " model.train()\n", | |
| " for batch in train_dataloader:\n", | |
| " outputs = model(**batch)\n", | |
| " loss = outputs.loss\n", | |
| " accelerator.backward(loss)\n", | |
| "\n", | |
| " optimizer.step()\n", | |
| " lr_scheduler.step()\n", | |
| " optimizer.zero_grad()\n", | |
| " progress_bar.update(1)\n", | |
| "\n", | |
| " # Evaluation\n", | |
| " model.eval()\n", | |
| " for batch in eval_dataloader:\n", | |
| " with torch.no_grad():\n", | |
| " outputs = model(**batch)\n", | |
| "\n", | |
| " predictions = outputs.logits.argmax(dim=-1)\n", | |
| " labels = batch[\"labels\"]\n", | |
| "\n", | |
| " # Necessary to pad predictions and labels for being gathered\n", | |
| " predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100)\n", | |
| " labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n", | |
| "\n", | |
| " predictions_gathered = accelerator.gather(predictions)\n", | |
| " labels_gathered = accelerator.gather(labels)\n", | |
| "\n", | |
| " true_predictions, true_labels = postprocess(predictions_gathered, labels_gathered)\n", | |
| " metric.add_batch(predictions=true_predictions, references=true_labels)\n", | |
| "\n", | |
| " results = metric.compute()\n", | |
| " print(\n", | |
| " f\"epoch {epoch}:\",\n", | |
| " {\n", | |
| " key: results[f\"overall_{key}\"]\n", | |
| " for key in [\"precision\", \"recall\", \"f1\", \"accuracy\"]\n", | |
| " },\n", | |
| " )\n", | |
| "\n", | |
| " # Save and upload\n", | |
| " accelerator.wait_for_everyone()\n", | |
| " unwrapped_model = accelerator.unwrap_model(model)\n", | |
| " unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n", | |
| " if accelerator.is_main_process:\n", | |
| " tokenizer.save_pretrained(output_dir)\n", | |
| " repo.push_to_hub(\n", | |
| " commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n", | |
| " )" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 46, | |
| "metadata": { | |
| "id": "tqI1OixNXyFo" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "accelerator.wait_for_everyone()\n", | |
| "unwrapped_model = accelerator.unwrap_model(model)\n", | |
| "unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 47, | |
| "metadata": { | |
| "id": "32tObPI5XyFo", | |
| "outputId": "31cface5-51a1-47d3-be1b-f6f3eb597c30", | |
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| } | |
| }, | |
| "outputs": [ | |
| { | |
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| }, | |
| { | |
| "output_type": "display_data", | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
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| "version_minor": 0, | |
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| } | |
| }, | |
| "metadata": {} | |
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| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[{'entity_group': 'PER',\n", | |
| " 'score': 0.99445945,\n", | |
| " 'word': 'Daethyra',\n", | |
| " 'start': 11,\n", | |
| " 'end': 19}]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 47 | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import pipeline\n", | |
| "\n", | |
| "# Replace this with your own checkpoint\n", | |
| "model_checkpoint = \"daethyra/bert-finetuned-ner\"\n", | |
| "token_classifier = pipeline(\n", | |
| " \"token-classification\", model=model_checkpoint, aggregation_strategy=\"simple\"\n", | |
| ")\n", | |
| "token_classifier(\"My name is Daethyra and I am passionate about creating programs that improve peoples' lives.\")" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
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| "value": "<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.svg\nalt='Hugging Face'> <br> Copy a token from <a\nhref=\"https://huggingface.co/settings/tokens\" target=\"_blank\">your Hugging Face\ntokens page</a> and paste it below. <br> Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. </center>" | |
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