{ "cells": [ { "cell_type": "markdown", "source": [ "# Finetuning of the specialized version of the language model BERTimbau on a token classification task (NER) with the dataset LeNER-Br" ], "metadata": { "id": "TLoRwNce-vzX" } }, { "cell_type": "markdown", "source": [ "- **Credit**: this notebook is copied/pasted with small changes from [PyTorch Examples](https://huggingface.co/docs/transformers/notebooks#pytorch-examples) of Hugging Face (notebook [token_classification.ipynb](https://github.com/huggingface/notebooks/blob/master/examples/token_classification.ipynb)).\n", "- **Author**: [Pierre GUILLOU](https://www.linkedin.com/in/pierreguillou/)\n", "- **Date**: 12/20/2021\n", "- **Blog post**: [NLP | Modelos e Web App para Reconhecimento de Entidade Nomeada (NER) no domínio jurídico brasileiro](https://medium.com/@pierre_guillou/nlp-modelos-e-web-app-para-reconhecimento-de-entidade-nomeada-ner-no-dom%C3%ADnio-jur%C3%ADdico-b658db55edfb)" ], "metadata": { "id": "5WT-HWVQ7VKe" } }, { "cell_type": "markdown", "metadata": { "id": "rEJBSTyZIrIb" }, "source": [ "## Overview" ] }, { "cell_type": "markdown", "metadata": { "id": "fTvCIuqgEj0A" }, "source": [ "In this notebook, we will see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) model to a token classification task, which is the task of predicting a label for each token.\n", "\n", "![Widget inference representing the NER task](https://github.com/huggingface/notebooks/raw/8044bbce25bed20a79e5488040a41d3c32575cec/examples/images/token_classification.png)\n", "\n", "The most common token classification tasks are:\n", "\n", "- NER (Named-entity recognition) Classify the entities in the text (person, organization, location...).\n", "- POS (Part-of-speech tagging) Grammatically classify the tokens (noun, verb, adjective...)\n", "- Chunk (Chunking) Grammatically classify the tokens and group them into \"chunks\" that go together\n", "\n", "We will see how to easily load a dataset for these kinds of tasks and use the `Trainer` API to fine-tune a model on it." ] }, { "cell_type": "markdown", "metadata": { "id": "4RRkXuteIrIh" }, "source": [ "This notebook is built to run on any token classification task, with any model checkpoint from the [Model Hub](https://huggingface.co/models) as long as that model has a version with a token classification head and a fast tokenizer (check on [this table](https://huggingface.co/transformers/index.html#bigtable) if this is the case). It might just need some small adjustments if you decide to use a different dataset than the one used here. Depending on you model and the GPU you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those three parameters, then the rest of the notebook should run smoothly:" ] }, { "cell_type": "markdown", "source": [ "## Configuration" ], "metadata": { "id": "tbVxQFOV8B21" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zVvslsfMIrIh" }, "outputs": [], "source": [ "task = \"ner\" # Should be one of \"ner\", \"pos\" or \"chunk\"\n", "\n", "# model_checkpoint = \"neuralmind/bert-base-portuguese-cased\"\n", "# model_checkpoint = \"neuralmind/bert-large-portuguese-cased\"\n", "model_checkpoint = \"pierreguillou/bert-base-cased-pt-lenerbr\"" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rFz2diizOhKz", "outputId": "6dcf614c-d5c5-4d8f-e5c9-4718f29166aa" }, "source": [ "from google.colab import drive \n", "drive.mount('/content/drive', force_remount=True)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "markdown", "source": [ "If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it." ], "metadata": { "id": "-EZW0-ff70vJ" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MOsHUjgdIrIW" }, "outputs": [], "source": [ "%%capture\n", "!pip install datasets transformers seqeval" ] }, { "cell_type": "markdown", "metadata": { "id": "19JACx0TEjz5" }, "source": [ "If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.\n", "\n", "To be able to share your model with the community and generate results like the one shown in the picture below via the inference API, there are a few more steps to follow.\n", "\n", "First you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!) then execute the following cell and input your username and password:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jFZyzs2aEjz6", "colab": { "base_uri": "https://localhost:8080/", "height": 387, "referenced_widgets": [ "2d49a0ce504a4205aaad5a6459901411", "131cf3574c2647f3bef4ca1af53e9e2c", "7d11f7958d9c4640a864628bafaa5ea0", "eaf48c7c7be0400aa9741d35248801a9", "3f342dfbe44b4f0b931e581995a1aba2", "2a639392b8ca4ce5ab2aa468ee1c62a7", "8bc65bb4498b4a419d9ae2cedf6659f2", "7b522cb9b5624dc787866fc009c2778b", "0e57e94ae0ae4c619919724ac6a8e51a", "16cc466e62464b6ebfe64f5c3c5616b7", "8693803d3ae5416fb2adb6fb15f34aca", "cb669c6f05454801acfee663e5e53ab7", "347ad6c051ff427bb4720d93de343f33", "f43c0712df12470999f0881eab479102", "d300b8b0ace1499f8d9ed16908810f0a", "19e1a4ae15be46d0b9455f858c9187d2", "02ec97730290454faf2988eb7709618c" ] }, "outputId": "a7e45fee-9485-48ca-c18d-1659eea716d2" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ], "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ] }, { "cell_type": "markdown", "metadata": { "id": "aCR-Kws7Ejz7" }, "source": [ "Then you need to install Git-LFS. Uncomment the following instructions:" ] }, { "cell_type": "code", "source": [ "%%capture\n", "!apt install git-lfs" ], "metadata": { "id": "Gipmah9QeWjY" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "SK7bIVJ-Ejz8" }, "source": [ "Make sure your version of Transformers is at least 4.11.0 since the functionality was introduced in that version:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XrDA_lWtEjz8", "outputId": "efcc3fd4-0415-4481-bfa0-2c671b23c92c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "4.13.0\n" ] } ], "source": [ "import transformers\n", "\n", "print(transformers.__version__)\n", "# 4.13.0" ] }, { "cell_type": "code", "source": [ "import datasets\n", "\n", "print(datasets.__version__)\n", "# 1.17.0" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lqlk0kFyRRZY", "outputId": "6861a58e-c132-462e-da05-b35ec84e4199" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1.17.0\n" ] } ] }, { "cell_type": "code", "source": [ "import pathlib\n", "from pathlib import Path\n", "\n", "import pandas as pd" ], "metadata": { "id": "X6L3zDsM8OnI" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from datasets import Dataset, DatasetDict" ], "metadata": { "id": "f3fNgz678QiE" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "HFASsisvIrIb" }, "source": [ "You can find a script version of this notebook to fine-tune your model in a distributed fashion using multiple GPUs or TPUs [here](https://github.com/huggingface/transformers/tree/master/examples/token-classification)." ] }, { "cell_type": "markdown", "metadata": { "id": "whPRbBNbIrIl" }, "source": [ "## Loading the dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "W7QYTpxXIrIl" }, "source": [ "We will use the [🤗 Datasets](https://github.com/huggingface/datasets) library to download the data and get the metric we need to use for evaluation (to compare our model to the benchmark). This can be easily done with the functions `load_dataset` and `load_metric`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IreSlFmlIrIm" }, "outputs": [], "source": [ "from datasets import load_dataset, load_metric" ] }, { "cell_type": "markdown", "metadata": { "id": "CKx2zKs5IrIq" }, "source": [ "For our example here, we'll use the [LeNER-Br dataset](https://huggingface.co/datasets/lener_br). The notebook should work with any token classification dataset provided by the 🤗 Datasets library. If you're using your own dataset defined from a JSON or csv file (see the [Datasets documentation](https://huggingface.co/docs/datasets/loading_datasets.html#from-local-files) on how to load them), it might need some adjustments in the names of the columns used." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 327, "referenced_widgets": [ "8e5f5cd5469648a3a75c541529538bea", "b509b617e1f84d75aa8942ab4db1b74c", "537d91fed7a9474d86a662bdd7af5225", "497959189d3144d9b8fabdfd45828538", "690bf3c462bb4d8caed2482556af85b3", "086fd56a7c4a4072972ee3f56b3314b7", "120c6cc9980b487892b199277df8b799", "a361a1f20cb2436aa83979917e6929b5", "1f6571502d3043639731afcbb6fe5df3", "9d866d5cdcdc4f4c9a34c5610073cd3f", "8299e78398ff4f04a7ad891119787f7c", "7ed5fef22b1e49979dd00b22bcee6ff8", "8abb2d58745843c4a63cd200957746b7", "05d6d55c3d9141619d0630d540574993", "33e3bef5e37646a29104ba3a7e8658c0", "b3602b3977924b81871a888f9e42aa93", 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"application/vnd.jupyter.widget-view+json": { "model_id": "8e5f5cd5469648a3a75c541529538bea", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/2.20k [00:00\n", " \n", " \n", " \n", " id\n", " tokens\n", " ner_tags\n", " \n", " \n", " \n", " \n", " 0\n", " 1567\n", " [POSSIBILIDADE, .]\n", " [O, O]\n", " \n", " \n", " 1\n", " 4517\n", " [Relator, :, Ministro, Napoleão, Nunes, Maia, Filho, .]\n", " [O, O, O, B-PESSOA, I-PESSOA, I-PESSOA, I-PESSOA, O]\n", " \n", " \n", " 2\n", " 4072\n", " [Publique-se, e, encaminhe-se, ao, Instituto, de, Defesa, do, Consumidor, -, PROCON, ,, para, as, providências, pertinentes, .]\n", " [O, O, O, O, B-ORGANIZACAO, I-ORGANIZACAO, I-ORGANIZACAO, I-ORGANIZACAO, I-ORGANIZACAO, O, B-ORGANIZACAO, O, O, O, O, O, O]\n", " \n", " \n", " 3\n", " 52\n", " [AÇÃO, DECLARATÓRIA, .]\n", " [O, O, O]\n", " \n", " \n", " 4\n", " 4307\n", " [AGRAVO, INTERNO, NO, RECURSO, ESPECIAL, .]\n", " [O, O, O, O, O, O]\n", " \n", " \n", " 5\n", " 5238\n", " [Aceita, a, denúncia, à, fls, ., 39, e, interrogado, o, réu, a, fls, ., 174, ,, sua, Advogada, apresentou, defesa, prévia, a, fls, ., 93/98, .]\n", " [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O]\n", " \n", " \n", " 6\n", " 958\n", " [PROJETO, DE, AUTORIA, PARLAMENTAR, .]\n", " [O, O, O, O, O]\n", " \n", " \n", " 7\n", " 3353\n", " [O, presente, feito, carece, de, condição, de, procedibilidade, ,, devendo, ser, reconhecida, ,, preliminarmente, e, de, ofício, ,, a, ausência, de, formalidade, essencial, à, sua, continuidade, .]\n", " [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O]\n", " \n", " \n", " 8\n", " 2085\n", " [Também, incompleta, a, documentação, relativa, a, despesas, com, táxi, ,, consultores, ,, alimentação, ,, advogados, e, prestadores, de, serviços, diversos, .]\n", " [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O]\n", " \n", " \n", " 9\n", " 7633\n", " [Inicialmente, ,, não, há, que, se, falar, em, contradição, ,, pois, o, caso, era, mesmo, de, extinção, da, execução, ,, ainda, que, por, fundamento, diverso, ,, devendo, ser, mantida, a, decisão, de, origem, e, ,, consequentemente, ,, negado, provimento, ao, Agravo, da, União, Federal, .]\n", " [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-ORGANIZACAO, O, O]\n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {} } ], "source": [ "show_random_elements(datasets[\"train\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "n9qywopnIrJH" }, "source": [ "## Preprocessing the data" ] }, { "cell_type": "markdown", "metadata": { "id": "YVx71GdAIrJH" }, "source": [ "Before we can feed those texts to our model, we need to preprocess them. This is done by a 🤗 Transformers `Tokenizer` which will (as the name indicates) tokenize the inputs (including converting the tokens to their corresponding IDs in the pretrained vocabulary) and put it in a format the model expects, as well as generate the other inputs that model requires.\n", "\n", "To do all of this, we instantiate our tokenizer with the `AutoTokenizer.from_pretrained` method, which will ensure:\n", "\n", "- we get a tokenizer that corresponds to the model architecture we want to use,\n", "- we download the vocabulary used when pretraining this specific checkpoint.\n", "\n", "That vocabulary will be cached, so it's not downloaded again the next time we run the cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eXNLu_-nIrJI", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "d69ce383bfa941a3aacff4c20543597b", "718220d4d2404452ad94561cd7c14455", "bc9efe2495e54820ba3a8ff8e9f5a202", "f9a02b0ca8364782b666395065729834", "7c17045f6e2d4e4d8f3cd5964e63f6ca", "3765ac69a8624073a0f4fbcfe67340c8", "2a1a3511b13d4f21b02ed14a674e0ae7", "8a024d946f6a401980cce78884233d80", "65f6203b99584e7b9e0ea5e9e5cc9184", "e921c3e2a5a54570a4770946b9375985", "2849d60cc7a34dc19ae44d3127c2bf16", "d65628a4664344be9fc8390c2de40575", "9e2ff2967b71465ba5d923a470691706", "b5785ef2ab0c4af4b8019f182ce441fe", "65c92def396a4a0286d18a11d1b10342", "603272a6c83c4c6a846409cdaef6f289", "a3794b74f3954eeca21b9a896edc9e12", "0edfd72f462040709cd16b0ca7b23116", "a0ff1c76dce14f459e9f805296e348b4", "a0e50876cbc84f939993f39606cbd3d4", "bcaa18c74d2e4646ab365da2dfb17287", "aa5b921f187d448c8a3ed8012da5b6b4", "41f697dacd0f49f781d6fa7a3e4f9003", "aed111ccdecc4c92ae30a3a49dedb491", "b1dbb46040864821addf5ee0782f1799", "9420b6b4b747449ca46772d3ce48229e", "1747b188a7d64cca9da45021a2bfc784", "365932023aa74e2989ab6ad1c6cdcaf5", "97bf39a8e7f149d1b0b57915c6702da7", "2b728b3a5ffe4d7c88902cbf60e9d9be", "9f3e6acc08c64d93bfbda459f44bedfe", "ed68866a1c5541709c36a97e49b5bc21", "01f4c9a541ab40c98132c6df4db2dd92", "ace16a59593147f2a043c0ae743d0192", "3f810be8fb974303a1ab01eb7e98ff27", "36fd77917c764854bccdbf680fe2ff7f", "4a03205517c2447cb231a053891856cd", "1d023fa39aeb42c48aaeae93e8ad3d95", "4fd1462370a34e409fa9921a720d7e50", "c061e8d28a6a47618865fad03aa6fd88", "6766c82f60504816b72046b781c8678b", "29bd60940a0d4bf481fff0447f8d0340", "9140e8b41e784a86af2ca587f1db8cc7", "3b7a2c0a76a844aba53c7a250ae0eb4a" ] }, "outputId": "66578393-f160-411f-ed3b-6ab08a1e001b" }, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d69ce383bfa941a3aacff4c20543597b", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/530 [00:00\n", " \n", " \n", " [2320/2934 37:10 < 09:50, 1.04 it/s, Epoch 2/3]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining LossValidation LossPrecisionRecallF1Accuracy
2900.3146000.1630420.7358280.6978490.7163360.949198
5800.0869000.1234950.7795400.8243010.8012960.965807
8700.0728000.1067850.7984810.8589250.8276000.968626
11600.0463000.1099210.8245760.8774190.8501770.973243
14500.0366000.1024950.8487920.8993550.8733420.975940
17400.0334000.1215140.8216810.8997850.8589610.967071
20300.0347000.1155680.8468490.8870970.8665060.970607
23200.0180000.1086000.8402580.8959140.8671940.973730

" ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags.\n", "***** Running Evaluation *****\n", " Num examples = 1177\n", " Batch size = 8\n", "Saving model checkpoint to /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-290\n", "Configuration saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-290/config.json\n", "Model weights saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-290/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-290/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-290/special_tokens_map.json\n", "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags.\n", "***** Running Evaluation *****\n", " Num examples = 1177\n", " Batch size = 8\n", "Saving model checkpoint to /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-580\n", "Configuration saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-580/config.json\n", "Model weights saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-580/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-580/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-580/special_tokens_map.json\n", "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags.\n", "***** Running Evaluation *****\n", " Num examples = 1177\n", " Batch size = 8\n", "Saving model checkpoint to /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-870\n", "Configuration saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-870/config.json\n", "Model weights saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-870/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-870/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-870/special_tokens_map.json\n", "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags.\n", "***** Running Evaluation *****\n", " Num examples = 1177\n", " Batch size = 8\n", "Saving model checkpoint to /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1160\n", "Configuration saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1160/config.json\n", "Model weights saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1160/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1160/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1160/special_tokens_map.json\n", "Deleting older checkpoint [/content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-290] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags.\n", "***** Running Evaluation *****\n", " Num examples = 1177\n", " Batch size = 8\n", "Saving model checkpoint to /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1450\n", "Configuration saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1450/config.json\n", "Model weights saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1450/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1450/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1450/special_tokens_map.json\n", "Deleting older checkpoint [/content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-580] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags.\n", "***** Running Evaluation *****\n", " Num examples = 1177\n", " Batch size = 8\n", "Saving model checkpoint to /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1740\n", "Configuration saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1740/config.json\n", "Model weights saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1740/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1740/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1740/special_tokens_map.json\n", "Deleting older checkpoint [/content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-870] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags.\n", "***** Running Evaluation *****\n", " Num examples = 1177\n", " Batch size = 8\n", "Saving model checkpoint to /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-2030\n", "Configuration saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-2030/config.json\n", "Model weights saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-2030/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-2030/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-2030/special_tokens_map.json\n", "Deleting older checkpoint [/content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1160] due to args.save_total_limit\n", "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags.\n", "***** Running Evaluation *****\n", " Num examples = 1177\n", " Batch size = 8\n", "Saving model checkpoint to /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-2320\n", "Configuration saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-2320/config.json\n", "Model weights saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-2320/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-2320/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-2320/special_tokens_map.json\n", "Deleting older checkpoint [/content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1740] due to args.save_total_limit\n", "\n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n", "Loading best model from /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/checkpoints/e3_lr2e-05/checkpoint-1450 (score: 0.8733423827921062).\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=2320, training_loss=0.0804201787915723, metrics={'train_runtime': 2231.4621, 'train_samples_per_second': 10.524, 'train_steps_per_second': 1.315, 'total_flos': 901649209129776.0, 'train_loss': 0.0804201787915723, 'epoch': 2.37})" ] }, "metadata": {}, "execution_count": 36 } ] }, { "cell_type": "markdown", "metadata": { "id": "CKASz-2vIrJi" }, "source": [ "The `evaluate` method allows you to evaluate again on the evaluation dataset or on another dataset:" ] }, { "cell_type": "code", "source": [ "trainer.evaluate()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 262 }, "id": "HljwI_DBSNBn", "outputId": "82ca267b-47b5-4108-c1fe-9e5d040416b3" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags.\n", "***** Running Evaluation *****\n", " Num examples = 1177\n", " Batch size = 8\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "

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\n", " " ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "{'epoch': 2.37,\n", " 'eval_accuracy': 0.9759397808828684,\n", " 'eval_f1': 0.8733423827921062,\n", " 'eval_loss': 0.10249536484479904,\n", " 'eval_precision': 0.8487923685812868,\n", " 'eval_recall': 0.8993548387096775,\n", " 'eval_runtime': 50.5934,\n", " 'eval_samples_per_second': 23.264,\n", " 'eval_steps_per_second': 2.925}" ] }, "metadata": {}, "execution_count": 37 } ] }, { "cell_type": "markdown", "metadata": { "id": "YwGBhU-lEj0U" }, "source": [ "To get the precision/recall/f1 computed for each category now that we have finished training, we can apply the same function as before on the result of the `predict` method:" ] }, { "cell_type": "code", "source": [ "predictions, labels, _ = trainer.predict(tokenized_datasets[\"validation\"])\n", "predictions = np.argmax(predictions, axis=2)\n", "\n", "# Remove ignored index (special tokens)\n", "true_predictions = [\n", " [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n", " for prediction, label in zip(predictions, labels)\n", "]\n", "true_labels = [\n", " [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n", " for prediction, label in zip(predictions, labels)\n", "]\n", "\n", "results = metric.compute(predictions=true_predictions, references=true_labels)\n", "results" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 592 }, "id": "QhGFZQShSTlu", "outputId": "9b403be4-43a9-4120-ca20-01938a01f89e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags.\n", "***** Running Prediction *****\n", " Num examples = 1177\n", " Batch size = 8\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "
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\n", " " ], "text/plain": [ "" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "{'JURISPRUDENCIA': {'f1': 0.7069834413246942,\n", " 'number': 657,\n", " 'precision': 0.6707650273224044,\n", " 'recall': 0.7473363774733638},\n", " 'LEGISLACAO': {'f1': 0.8256227758007118,\n", " 'number': 571,\n", " 'precision': 0.8390596745027125,\n", " 'recall': 0.8126094570928196},\n", " 'LOCAL': {'f1': 0.7688564476885645,\n", " 'number': 194,\n", " 'precision': 0.728110599078341,\n", " 'recall': 0.8144329896907216},\n", " 'ORGANIZACAO': {'f1': 0.8548387096774193,\n", " 'number': 1340,\n", " 'precision': 0.8062169312169312,\n", " 'recall': 0.9097014925373135},\n", " 'PESSOA': {'f1': 0.9826697892271662,\n", " 'number': 1072,\n", " 'precision': 0.9868297271872061,\n", " 'recall': 0.9785447761194029},\n", " 'TEMPO': {'f1': 0.9615846338535414,\n", " 'number': 816,\n", " 'precision': 0.9423529411764706,\n", " 'recall': 0.9816176470588235},\n", " 'overall_accuracy': 0.9759397808828684,\n", " 'overall_f1': 0.8733423827921062,\n", " 'overall_precision': 0.8487923685812868,\n", " 'overall_recall': 0.8993548387096775}" ] }, "metadata": {}, "execution_count": 38 } ] }, { "cell_type": "code", "source": [ "model_dir = '/content/drive/MyDrive/' + 'ner-lenerbr-' + str(model_name) + '/model/'\n", "trainer.save_model(model_dir)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wm0QAfx7Q-GT", "outputId": "35bedeeb-9c06-468a-bb76-88effd4cb74e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Saving model checkpoint to /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/\n", "Configuration saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/config.json\n", "Model weights saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/pytorch_model.bin\n", "tokenizer config file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/tokenizer_config.json\n", "Special tokens file saved in /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/special_tokens_map.json\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Push model to HF model hub" ], "metadata": { "id": "98yTUXGT9UKl" } }, { "cell_type": "markdown", "metadata": { "id": "CKcJJdZ_Ej0U" }, "source": [ "You can now upload the result of the training to the Hub, just execute this instruction:" ] }, { "cell_type": "code", "source": [ "ner_tags = datasets[\"train\"].features[f\"ner_tags\"].feature.names" ], "metadata": { "id": "GlZp_NpBjYrF" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import AutoModelForTokenClassification, AutoTokenizer, AutoConfig\n", "\n", "id2label = {str(i): label for i, label in enumerate(ner_tags)}\n", "label2id = {v: k for k, v in id2label.items()}\n", "\n", "model = AutoModelForTokenClassification.from_pretrained(\n", " str(model_dir),\n", " id2label=id2label,\n", " label2id=label2id,\n", ")\n", "tokenizer = AutoTokenizer.from_pretrained(str(model_dir))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HBzsvyjrjR9C", "outputId": "9a85f148-6e5d-44ad-d786-34a33eb79140" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "loading configuration file /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/config.json\n", "Model config BertConfig {\n", " \"_name_or_path\": \"/content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/\",\n", " \"architectures\": [\n", " \"BertForTokenClassification\"\n", " ],\n", " \"attention_probs_dropout_prob\": 0.1,\n", " \"classifier_dropout\": null,\n", " \"directionality\": \"bidi\",\n", " \"hidden_act\": \"gelu\",\n", " \"hidden_dropout_prob\": 0.1,\n", " \"hidden_size\": 768,\n", " \"id2label\": {\n", " \"0\": \"O\",\n", " \"1\": \"B-ORGANIZACAO\",\n", " \"10\": \"I-LEGISLACAO\",\n", " \"11\": \"B-JURISPRUDENCIA\",\n", " \"12\": \"I-JURISPRUDENCIA\",\n", " \"2\": \"I-ORGANIZACAO\",\n", " \"3\": \"B-PESSOA\",\n", " \"4\": \"I-PESSOA\",\n", " \"5\": \"B-TEMPO\",\n", " \"6\": \"I-TEMPO\",\n", " \"7\": \"B-LOCAL\",\n", " \"8\": \"I-LOCAL\",\n", " \"9\": \"B-LEGISLACAO\"\n", " },\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 3072,\n", " \"label2id\": {\n", " \"B-JURISPRUDENCIA\": \"11\",\n", " \"B-LEGISLACAO\": \"9\",\n", " \"B-LOCAL\": \"7\",\n", " \"B-ORGANIZACAO\": \"1\",\n", " \"B-PESSOA\": \"3\",\n", " \"B-TEMPO\": \"5\",\n", " \"I-JURISPRUDENCIA\": \"12\",\n", " \"I-LEGISLACAO\": \"10\",\n", " \"I-LOCAL\": \"8\",\n", " \"I-ORGANIZACAO\": \"2\",\n", " \"I-PESSOA\": \"4\",\n", " \"I-TEMPO\": \"6\",\n", " \"O\": \"0\"\n", " },\n", " \"layer_norm_eps\": 1e-12,\n", " \"max_position_embeddings\": 512,\n", " \"model_type\": \"bert\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 12,\n", " \"output_past\": true,\n", " \"pad_token_id\": 0,\n", " \"pooler_fc_size\": 768,\n", " \"pooler_num_attention_heads\": 12,\n", " \"pooler_num_fc_layers\": 3,\n", " \"pooler_size_per_head\": 128,\n", " \"pooler_type\": \"first_token_transform\",\n", " \"position_embedding_type\": \"absolute\",\n", " \"torch_dtype\": \"float32\",\n", " \"transformers_version\": \"4.15.0\",\n", " \"type_vocab_size\": 2,\n", " \"use_cache\": true,\n", " \"vocab_size\": 29794\n", "}\n", "\n", "loading weights file /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/pytorch_model.bin\n", "All model checkpoint weights were used when initializing BertForTokenClassification.\n", "\n", "All the weights of BertForTokenClassification were initialized from the model checkpoint at /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForTokenClassification for predictions without further training.\n", "Didn't find file /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/added_tokens.json. We won't load it.\n", "loading file /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/vocab.txt\n", "loading file /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/tokenizer.json\n", "loading file None\n", "loading file /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/special_tokens_map.json\n", "loading file /content/drive/MyDrive/ner-lenerbr-bert-base-cased-pt-lenerbr/model/tokenizer_config.json\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qD5ZoTgOEj0V", "colab": { "base_uri": "https://localhost:8080/", "height": 274, "referenced_widgets": [ "d096c962fe714263a8eba9763bdfeead", "19dd0d293c0341eaa7ce8f069e2c7cbd", "b695834a29684f4693502224f94b76bc", "aec3d2a1a4a24fc3bb8e27513d4e459f", "8b543b5e06f84f94a5bfa6f7deb023ce", "2fa65eac20754548a5cd1760378855ec", "d4bdc9f706d447e1a8837415fca01189", "468c54e8b07a436a8aa533b9b39c0d96", "6eba5e7d023e4243aa0c304a3d20a444", "47f5fd5d5bf544c4ba3b25737e9705c7", "7113eac39fb74f498345113a6c847477" ] }, "outputId": "8d98a689-1d28-4fc4-ab82-2adebc8f001b" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:726: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n", " FutureWarning,\n", "Cloning https://huggingface.co/pierreguillou/ner-bert-base-cased-pt-lenerbr into local empty directory.\n", "Configuration saved in pierreguillou/ner-bert-base-cased-pt-lenerbr/config.json\n", "Model weights saved in pierreguillou/ner-bert-base-cased-pt-lenerbr/pytorch_model.bin\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d096c962fe714263a8eba9763bdfeead", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.37k/413M [00:00 main\n", "\n", "tokenizer config file saved in pierreguillou/ner-bert-base-cased-pt-lenerbr/tokenizer_config.json\n", "Special tokens file saved in pierreguillou/ner-bert-base-cased-pt-lenerbr/special_tokens_map.json\n", "To https://huggingface.co/pierreguillou/ner-bert-base-cased-pt-lenerbr\n", " 181dfa1..aca0f80 main -> main\n", "\n" ] } ], "source": [ "if model_checkpoint == \"pierreguillou/bert-base-cased-pt-lenerbr\":\n", " model.push_to_hub('pierreguillou/ner-bert-base-cased-pt-lenerbr')\n", " tokenizer.push_to_hub('pierreguillou/ner-bert-base-cased-pt-lenerbr')\n", "else:\n", " model.push_to_hub('pierreguillou/ner-bert-large-cased-pt-lenerbr')\n", " tokenizer.push_to_hub('pierreguillou/ner-bert-large-cased-pt-lenerbr')\n", " " ] }, { "cell_type": "markdown", "metadata": { "id": "G-4pjcTOEj0V" }, "source": [ "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier `\"your-username/the-name-you-picked\"` so for instance:\n", "\n", "```python\n", "from transformers import AutoModelForTokenClassification\n", "\n", "model = AutoModelForTokenClassification.from_pretrained(\"sgugger/my-awesome-model\")\n", "```" ] }, { "cell_type": "markdown", "source": [ "# END" ], "metadata": { "id": "UbFmH8RvHdro" } } ], "metadata": { "colab": { "name": "HuggingFace_Notebook_token_classification_NER_LeNER_Br.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "2d49a0ce504a4205aaad5a6459901411": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_view_name": "VBoxView", "_dom_classes": [], "_model_name": "VBoxModel", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_view_count": null, "_view_module_version": "1.5.0", "box_style": "", "layout": "IPY_MODEL_131cf3574c2647f3bef4ca1af53e9e2c", "_model_module": "@jupyter-widgets/controls", "children": [ "IPY_MODEL_7d11f7958d9c4640a864628bafaa5ea0", "IPY_MODEL_eaf48c7c7be0400aa9741d35248801a9", "IPY_MODEL_3f342dfbe44b4f0b931e581995a1aba2", "IPY_MODEL_2a639392b8ca4ce5ab2aa468ee1c62a7", "IPY_MODEL_8bc65bb4498b4a419d9ae2cedf6659f2" ] } }, "131cf3574c2647f3bef4ca1af53e9e2c": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_view_name": "LayoutView", "grid_template_rows": null, "right": null, "justify_content": null, "_view_module": "@jupyter-widgets/base", "overflow": null, "_model_module_version": "1.2.0", "_view_count": null, "flex_flow": "column", "width": "50%", "min_width": null, "border": null, "align_items": "center", "bottom": null, "_model_module": "@jupyter-widgets/base", "top": null, "grid_column": null, "overflow_y": null, "overflow_x": null, "grid_auto_flow": null, "grid_area": null, "grid_template_columns": null, "flex": null, "_model_name": "LayoutModel", "justify_items": null, "grid_row": null, "max_height": null, "align_content": null, "visibility": null, "align_self": null, "height": null, "min_height": null, "padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": "flex", "left": null } }, "7d11f7958d9c4640a864628bafaa5ea0": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_view_name": "HTMLView", "style": "IPY_MODEL_7b522cb9b5624dc787866fc009c2778b", "_dom_classes": [], "description": "", "_model_name": "HTMLModel", "placeholder": "​", "_view_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "value": "
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