{ "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", "03b801de0a4346268e05a899a31f3839", "34b7ecd785204eebbd65626c925088f8", "903531c91439422da08e4a13e6cd8db7", "a1484eef417243089a08aa61be87242f", "c2fd654ded0841efafda13c29bdf1f5d", "2fd47cf8c984458eaeb52e67c5bf5d65", "56bf0d3eb7414cd7a007e4c5d79990d0", "72167a33a3f04f599d41cbf7ede2a1b5", "73a516c08f3846a89ba6f41a8ee01699", "666c358e45894bd0b55c65de9226285f", "8391515be61247cd866107376801ad63", "287703c286c04e2aa13314d86933b1f7", "39031a5af3454fa9a7da8685df6afe4c", "e7427b6526a14cd38d7c25c04f4e2fd4", "f5f474c79efa463a96b72bc0fd6fdd94", "1a3d8cce01784545bbb7856fdb71adb5", "87bd961892be4775b4b2ab23db4b9ae6", "288000ebc917408b849810bad5cb765a", "5e8797ff794044e8b241ea0df5ec97cb", "d18cb08076e0426facfd79e9b0565b90", "217d3c875d2a45739ac53f64deb1a7d6", "38c3fa57562b4f638fd416b392a364e3", "7d4bfb13af1a4158a7e688253dcda840", "85f8c3a9a07b419fa1fc185826b7f6ef", "5686e8e95dfd491b9098696fe0c4f913", "3aa3c0e7bbec441e9dbc27e1704bb6d8", "33f63cb2f5e941048540e669ab14cfa9", "93f28819830a4386adae7481d84b7d03", "fd99ae6bfd5944e5b9620c093cbe41a8", "4de8be4aa2bc47e094513926a5b1eac0", "825136f1d0284733878b9c58827783cf", "f93d1dcb9fd642268101bef44b98f5eb", "19eeb8f99ef744a1850dfa9addc99cb4", "21065e5df6ea488999134cfcf643a0b8", "ebe6286e742844b8803cf48bee62c5ec", "6ffd2d238bc54a859b6e95e58d315288", "e0bd2467b96648b7858438b9c13cc6ee", "0240f43312ce43bda42a734d402373a0", "cda53d2e0a9d498ca68c0dda56966d47", "be9d93436199452f806433a01b87fd4a", "0477cc951ba04f43b99da568e4c39ad6", "8220fed74f424e94b0fd3cb9d57a5791", "6a075b69fe75454b84bad4a892afb93a", "3dc3fff570cc4076bc70e8ef83cb1389", "8c2444856ba44c749c3973a3e08d1cf4", "d248221981ba490cb7964344cda9565d", "133d9218dd134394b6af80d1b9fe1429", "3289cee31011431994fc72ab0afa3ad3", "bf7aace8978f4088b2bb1791e70c92f6", "a328bcd1bcd24a77835da1b8b52136c9", "5da3ba777bd44aa783a47ccbf0e7c25f", "b83a0cd36da043d1a50c775bf01d305e", "44e74d5f1608418393949709f33bbb47", "66540fa034e646b1a07c87502576c7cd", "2efb3798adcd4231b61c790973277ba8", "be084d08cf914436b829bc106dad60e8", "ff52ab2bb48a40d6a4c1150597eadfce", "f86c62ed2f664cbb80617f1ea58630c7", "d985afa8af224e349eafcbc762c04194", "79903b04d132439f8e0a26469b00001b", "3fe264ec144b4fd29e218b81b8682ffe", "f625363f04b5445ba902a9af90a4a6ca", "9ddd735ff4b3499b91f98b3b9ae51f12", "2641b3dea40646d7bcb9514b561c56c6", "efb6b31d136042608684bccbcec4c2eb", "e1221f3525f545d2b33ccb72e1c9530e", "9d4f745881e3435fb5246e61c2900ca5", "7957156b925e4bfb9bd52be49d22261c", "27ae8dde6e2342d492c06596b2072a26", "81cfb1e25dd14dc999febb495d34f82e", "a09c08bea8594f8681be6517579d04c4", "051e26bc261a469a95ae0a0472f83ca8", "80f76eb07ade42d691098b413b52081b", "f205c0eff29042b8a09c475c7f2743a3", "87ff4146065e4d0eaf90b84da751b97d", "b14e7adc0e95459abc2544436523edfb", "3ea8ffbb556240788b8b594c7e3fc670", "92e3417289b84044932b543b8ee58dcb", "571c514c58514736ad33fbe31e53b374", "34ec6159f8d744d6bc716e11e5dc558a", "1eaf10310dcb48f4a4643a0b11b222ab", "26b5d971a87f4511adf551611eccc153", "e3685f582fbf44078dae51da82600481", "3a8a8323aebe4b8c86d6d9a32280645d", "9ea3ef24a7c4462cb474d724cd7ad880", "606fb4aa505146ec895dd4400596bc71", "edaabe4187c644158ba27d688b9a03eb", "da7197558bbc451c8eb4ec8610a74e9e", "ec57dcca24ff4532866a34fee987a204", "9b2f4adf4d8949d7a9566aece9e5e5c1", "6ca4861a42d241a59a4f6ed202f9e4db", "bcb3a63ac666416ea21cce28f7483d41", "456b5bb573ec44ceb10cc2407527dfc8", "56d5931307e84e41a2e8bff16ab2d1f5", "1cccdf96723d4288a983e2059c9192e3", "5e8e1e323ed1425e8d3eaa3fb81338df", "139036f9cb0f4057bd5f69b8ba067e56", "38fc1dbe9c18468d88e5dc21958430a2", "d4d5521787d8455c8c02d877deb954f6", "6f9c76d032f0496b9df963450b704284", "4a214d281da944aea31d106271672d3a", "e6f57b8e4cec459e88312e578ea971a3", "1b92c590a2cf4045820cd1ae1fc80924", "7e402b8f699f4facb713b99b74a2686e", "e9784355b05647d6ab1919188d3f5a55" ] }, "id": "s_AY1ATSIrIq", "outputId": "6ae3fbc7-58c5-4e0b-f2a9-684e2fe15921" }, "outputs": [ { "output_type": "display_data", "data": { "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, ?B/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7ed5fef22b1e49979dd00b22bcee6ff8", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/1.51k [00:00, ?B/s]" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Downloading and preparing dataset lener_br/lener_br (download: 2.84 MiB, generated: 5.27 MiB, post-processed: Unknown size, total: 8.12 MiB) to /root/.cache/huggingface/datasets/lener_br/lener_br/1.0.0/4a8c97e6813b5c2d85a50faf0a3e6c24ea82f4a9044e6e9e8b24997d27399382...\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "56bf0d3eb7414cd7a007e4c5d79990d0", "version_minor": 0, "version_major": 2 }, 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"version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/3 [00:00, ?it/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f625363f04b5445ba902a9af90a4a6ca", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "80f76eb07ade42d691098b413b52081b", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3a8a8323aebe4b8c86d6d9a32280645d", "version_minor": 0, "version_major": 2 }, "text/plain": [ "0 examples [00:00, ? examples/s]" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Dataset lener_br downloaded and prepared to /root/.cache/huggingface/datasets/lener_br/lener_br/1.0.0/4a8c97e6813b5c2d85a50faf0a3e6c24ea82f4a9044e6e9e8b24997d27399382. Subsequent calls will reuse this data.\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1cccdf96723d4288a983e2059c9192e3", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/3 [00:00, ?it/s]" ] }, "metadata": {} } ], "source": [ "datasets = load_dataset(\"lener_br\")" ] }, { "cell_type": "markdown", "metadata": { "id": "RzfPtOMoIrIu" }, "source": [ "The `datasets` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation and test set." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DCW9RM6XEj0E", "outputId": "8218ef0b-3064-411e-bd7e-4ead37961767" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['id', 'tokens', 'ner_tags'],\n", " num_rows: 7828\n", " })\n", " validation: Dataset({\n", " features: ['id', 'tokens', 'ner_tags'],\n", " num_rows: 1177\n", " })\n", " test: Dataset({\n", " features: ['id', 'tokens', 'ner_tags'],\n", " num_rows: 1390\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 9 } ], "source": [ "datasets" ] }, { "cell_type": "markdown", "metadata": { "id": "1EFq3z-ZEj0H" }, "source": [ "We can see the training, validation and test sets all have a column for the tokens (the input texts split into words) and one column of labels for each kind of task we introduced before." ] }, { "cell_type": "markdown", "metadata": { "id": "u3EtYfeHIrIz" }, "source": [ "To access an actual element, you need to select a split first, then give an index:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "X6HrpprwIrIz", "outputId": "dbf3c608-f370-4e73-f4ff-62406d47aef7", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'id': '0',\n", " 'ner_tags': [0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 1,\n", " 2,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0,\n", " 0],\n", " 'tokens': ['EMENTA',\n", " ':',\n", " 'APELAÇÃO',\n", " 'CÍVEL',\n", " '-',\n", " 'AÇÃO',\n", " 'DE',\n", " 'INDENIZAÇÃO',\n", " 'POR',\n", " 'DANOS',\n", " 'MORAIS',\n", " '-',\n", " 'PRELIMINAR',\n", " '-',\n", " 'ARGUIDA',\n", " 'PELO',\n", " 'MINISTÉRIO',\n", " 'PÚBLICO',\n", " 'EM',\n", " 'GRAU',\n", " 'RECURSAL',\n", " '-',\n", " 'NULIDADE',\n", " '-',\n", " 'AUSÊNCIA',\n", " 'DE',\n", " 'INTERVENÇÃO',\n", " 'DO',\n", " 'PARQUET',\n", " 'NA',\n", " 'INSTÂNCIA',\n", " 'A',\n", " 'QUO',\n", " '-',\n", " 'PRESENÇA',\n", " 'DE',\n", " 'INCAPAZ',\n", " '-',\n", " 'PREJUÍZO',\n", " 'EXISTENTE',\n", " '-',\n", " 'PRELIMINAR',\n", " 'ACOLHIDA',\n", " '-',\n", " 'NULIDADE',\n", " 'RECONHECIDA',\n", " '.']}" ] }, "metadata": {}, "execution_count": 10 } ], "source": [ "datasets[\"train\"][0]" ] }, { "cell_type": "markdown", "metadata": { "id": "ahSl4rFpEj0H" }, "source": [ "The labels are already coded as integer ids to be easily usable by our model, but the correspondence with the actual categories is stored in the `features` of the dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oedB8Q--Ej0H", "outputId": "ac2ffa89-5f9f-4a6f-c8a6-906dd6d5df53" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Sequence(feature=ClassLabel(num_classes=13, names=['O', 'B-ORGANIZACAO', 'I-ORGANIZACAO', 'B-PESSOA', 'I-PESSOA', 'B-TEMPO', 'I-TEMPO', 'B-LOCAL', 'I-LOCAL', 'B-LEGISLACAO', 'I-LEGISLACAO', 'B-JURISPRUDENCIA', 'I-JURISPRUDENCIA'], names_file=None, id=None), length=-1, id=None)" ] }, "metadata": {}, "execution_count": 11 } ], "source": [ "datasets[\"train\"].features[f\"ner_tags\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "1udRoR7CEj0I" }, "source": [ "So for the NER tags, 0 corresponds to 'O', 1 to 'B-PER' etc... On top of the 'O' (which means no special entity), there are four labels for NER here, each prefixed with 'B-' (for beginning) or 'I-' (for intermediate), that indicate if the token is the first one for the current group with the label or not:\n", "- 'PESSOA' for person\n", "- 'ORGANIZACAO' for organization\n", "- 'LOCAL' for location\n", "- ...." ] }, { "cell_type": "markdown", "metadata": { "id": "typxzo_DEj0I" }, "source": [ "Since the labels are lists of `ClassLabel`, the actual names of the labels are nested in the `feature` attribute of the object above:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ali52SGLEj0I", "outputId": "20110aca-4a13-42e8-d468-97aafa77e3a6" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['O',\n", " 'B-ORGANIZACAO',\n", " 'I-ORGANIZACAO',\n", " 'B-PESSOA',\n", " 'I-PESSOA',\n", " 'B-TEMPO',\n", " 'I-TEMPO',\n", " 'B-LOCAL',\n", " 'I-LOCAL',\n", " 'B-LEGISLACAO',\n", " 'I-LEGISLACAO',\n", " 'B-JURISPRUDENCIA',\n", " 'I-JURISPRUDENCIA']" ] }, "metadata": {}, "execution_count": 12 } ], "source": [ "label_list = datasets[\"train\"].features[f\"{task}_tags\"].feature.names\n", "label_list" ] }, { "cell_type": "markdown", "metadata": { "id": "WHUmphG3IrI3" }, "source": [ "To get a sense of what the data looks like, the following function will show some examples picked randomly in the dataset (automatically decoding the labels in passing)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "i3j8APAoIrI3" }, "outputs": [], "source": [ "from datasets import ClassLabel, Sequence\n", "import random\n", "import pandas as pd\n", "from IPython.display import display, HTML\n", "\n", "def show_random_elements(dataset, num_examples=10):\n", " assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n", " picks = []\n", " for _ in range(num_examples):\n", " pick = random.randint(0, len(dataset)-1)\n", " while pick in picks:\n", " pick = random.randint(0, len(dataset)-1)\n", " picks.append(pick)\n", " \n", " df = pd.DataFrame(dataset[picks])\n", " for column, typ in dataset.features.items():\n", " if isinstance(typ, ClassLabel):\n", " df[column] = df[column].transform(lambda i: typ.names[i])\n", " elif isinstance(typ, Sequence) and isinstance(typ.feature, ClassLabel):\n", " df[column] = df[column].transform(lambda x: [typ.feature.names[i] for i in x])\n", " display(HTML(df.to_html()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SZy5tRB_IrI7", "outputId": "ec4eebe4-3540-413c-f92d-bd4d348d8f9d", "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 415 } }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "
\n", " | id | \n", "tokens | \n", "ner_tags | \n", "
---|---|---|---|
0 | \n", "1567 | \n", "[POSSIBILIDADE, .] | \n", "[O, O] | \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", "
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", "
3 | \n", "52 | \n", "[AÇÃO, DECLARATÓRIA, .] | \n", "[O, O, O] | \n", "
4 | \n", "4307 | \n", "[AGRAVO, INTERNO, NO, RECURSO, ESPECIAL, .] | \n", "[O, O, O, O, O, O] | \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", "
6 | \n", "958 | \n", "[PROJETO, DE, AUTORIA, PARLAMENTAR, .] | \n", "[O, O, O, O, O] | \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", "
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", "
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", "
Step | \n", "Training Loss | \n", "Validation Loss | \n", "Precision | \n", "Recall | \n", "F1 | \n", "Accuracy | \n", "
---|---|---|---|---|---|---|
290 | \n", "0.314600 | \n", "0.163042 | \n", "0.735828 | \n", "0.697849 | \n", "0.716336 | \n", "0.949198 | \n", "
580 | \n", "0.086900 | \n", "0.123495 | \n", "0.779540 | \n", "0.824301 | \n", "0.801296 | \n", "0.965807 | \n", "
870 | \n", "0.072800 | \n", "0.106785 | \n", "0.798481 | \n", "0.858925 | \n", "0.827600 | \n", "0.968626 | \n", "
1160 | \n", "0.046300 | \n", "0.109921 | \n", "0.824576 | \n", "0.877419 | \n", "0.850177 | \n", "0.973243 | \n", "
1450 | \n", "0.036600 | \n", "0.102495 | \n", "0.848792 | \n", "0.899355 | \n", "0.873342 | \n", "0.975940 | \n", "
1740 | \n", "0.033400 | \n", "0.121514 | \n", "0.821681 | \n", "0.899785 | \n", "0.858961 | \n", "0.967071 | \n", "
2030 | \n", "0.034700 | \n", "0.115568 | \n", "0.846849 | \n", "0.887097 | \n", "0.866506 | \n", "0.970607 | \n", "
2320 | \n", "0.018000 | \n", "0.108600 | \n", "0.840258 | \n", "0.895914 | \n", "0.867194 | \n", "0.973730 | \n", "
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