{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "j9x-5xxtgJYA" }, "source": [ "# [Token classification (PyTorch)](https://huggingface.co/learn/nlp-course/en/chapter7/2?fw=pt)\n", "\n", "* The Dataset structure contains rows of dictionary where each dictionary correspond to a data point consisting of features and coresponding values\n", "* `Dataset` object also has a number of methods and attributes\n", "* `DataCollatorForTokenClassification` pads with `-100`\n", "* The traditional framework used to evaluate token classification prediction is [`seqeval`](https://huggingface.co/spaces/evaluate-metric/seqeval)" ] }, { "cell_type": "markdown", "source": [ "#### Main steps to prepare custom training loop\n", "1. Prepare the dataloaders\n", "2. Prepare the model -> model initialisation and passing in 1d2label and label2id\n", "3. Prepare the optimizer\n", "4. Pass all to accelerate.prepare()" ], "metadata": { "id": "4xzr2_FURfQP" } }, { "cell_type": "markdown", "metadata": { "id": "53wt9QqZgJYC" }, "source": [ "Install the Transformers, Datasets, and Evaluate libraries to run this notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EO3w1a41gJYC", "outputId": "3e1b07df-ceb4-4e4a-8aa4-dfc74fc81530" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting datasets\n", " Downloading datasets-2.16.0-py3-none-any.whl (507 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting evaluate\n", " Downloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta 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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 24 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": "uyOjlt8igJYD" }, "source": [ "You will need to setup git, adapt your email and name in the following cell." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "GS9z71W1gJYD" }, "outputs": [], "source": [ "!git config --global user.email \"koakande@gmail.com\"\n", "!git config --global user.name \"koakande\"" ] }, { "cell_type": "markdown", "metadata": { "id": "Tk9JVa10gJYE" }, "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": { "colab": { "base_uri": "https://localhost:8080/", "height": 331, "referenced_widgets": [ "b09ce087c3134d64839edbc4a568f742", "43aa4e04178e4b5fa50837e4e0db4d55", "ebd1f7f7f4154bca9f0594a2f3351472", "768816c3f54c4e6cbca05c47b1ec34ad", "f18137c6c2f04783a50580de5e5caf3b", "c10791b3c8b3483d8a97007797392ab4", "b17ee1d8d8374223b2bb160758cf2355", "847822b699924086a19f2b9e73e613a2", "d068c6a78af848ddaf388fba271e28f7", "7fee36f9922c49c083d109fb49f48737", "d499f0acba1842e0892b1e0766e6c6c0", "b93985358ab046cd942e6249ca460539", "7d67a4d67b494ceeb6f7d2c7791b26ef", "368077ddf45840a89447a2f8ee5af94e", "2df808b55601428c96e693a6f0d70e52", "c154e37f61324f69b7f99dfaded33e96", "8830863aa8da4402b6ae48678c23a8c8" ] }, "id": "2_b6ALK3gJYE", "outputId": "3adce3a2-adb4-4a5b-9b8d-2927776678fd" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
multiple labels due from multple examples\n", " all_labels = examples[\"ner_tags\"]\n", " new_labels = []\n", " # iterate over each example and expand the label\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", " if i < 2:\n", " print(new_labels)\n", "\n", " tokenized_inputs[\"labels\"] = new_labels\n", " return tokenized_inputs" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 955, "referenced_widgets": [ "0536cfafaccf4ec18bb12950e72bfa76", "f91123520deb4b079f75d976bbc7a4cc", "0238463e07ac44fa8c446a31ca3c7b8e", "a4c715aecb3e4fd7a2a9a0f672bf1165", "094927f6de7c481a9fce81ff3edcc4c5", "ed80deef918b446bb19cc8134084a47c", "983d1135c66d4d93b2921678e4f856b8", "c06374868d38444387daeea9b71664b9", "c50804718a61487ab241017def181836", "032c14014a1f40b4afb9d58ed336b1ab", "2a81dac192644f0c9aa950b775c7da48", "5b61bcf5884943f2b4a048795f3e45a5", "87c74db18e144c198a16a214db8216a1", "78d1e24fbb2c4197aafbc904c0f7779f", "abc49c82348949ce92caf8e577a146a4", "206f50ec2cb049d3a176cb82afe98acd", "272593454194452d97159cdb9072ca94", "0bf1978fcd7b43d8b8207f1985170afd", "8549b416478c4c3c836d6b26892e0cc0", "83e3ee347b4a4ccf9b6295b56c04370d", "246c95adf0184097a8edd4f746d7f915", "f7bea156362345df9cf0f01b2a4d8210", "e4b92641775a4d56aaa75cc19e3bdee0", "7d5870d66dce4c288f748785c5e932eb", "8c0a114db5b64a5aa6f0271a8301607c", "26d6045644b8430a9ece9389e13617a6", "4358efc842eb495ab1525aee1379ea6b", "1e3b742a4ec5405b8e35c2e3a6158ac6", "d7842316357048289a55e8f0868c7b1e", "1aadb4ea3d884a95ae3d1904555321cc", "898dc78148f347e0b6654f5e26a727eb", "71e64fcdb2e94f709a070684658923f7", "adc0664aa9504a648cf17b79cc89ee60" ] }, "id": "N4YFu7EkgJYH", "outputId": "936fbc15-73b7-4abc-cb8b-9528101589ba" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/14041 [00:00=1.14.0 in 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/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": 28, "metadata": { "id": "xZbAih33gJYH", "outputId": "322d4c78-fe56-409a-a520-f148dd5b5521", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "1a81a79dc52641a4ab1b57890c97e85a", "ea937b7c1edb47ecbe70c9df27a3c3a5", "f4563797c3cf4e1db6063bcdcc5a4335", "42ff9ba7fca24986adbecca6ed17ba78", "a4f697f1d4a6454fb076a19165e399fc", "43138242ad4a4c1897130b8852e4ded9", "0abac9af4caa44ef87a59f3b7b2a4879", "8aac758dc81c4f228c35a467d1458ca4", "d28f69cc0d064fd1a5c7dd679a438ee8", "5b8adfe7fc704e84aa8c6939f218318a", "963af7b5f84c41039fb0f971b8468581" ] } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/6.34k [00:00 \u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTrainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m trainer = Trainer(\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, args, data_collator, train_dataset, eval_dataset, tokenizer, model_init, compute_metrics, callbacks, optimizers, preprocess_logits_for_metrics)\u001b[0m\n\u001b[1;32m 534\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_model_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 536\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_hf_repo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 537\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_save\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 538\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36m_build_hf_headers\u001b[0;34m(self, token, is_write_action, library_name, library_version, user_agent)\u001b[0m\n\u001b[1;32m 6941\u001b[0m \u001b[0;31m# Cannot do `token = token or self.token` as token can be `False`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6942\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6943\u001b[0;31m return build_hf_headers(\n\u001b[0m\u001b[1;32m 6944\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6945\u001b[0m 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\u001b[0mis_write_action\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 173\u001b[0m \u001b[0;34m\"Token is required (write-access action) but no token found. You need\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;34m\" to provide a token or be logged in to Hugging Face with\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Token is required (write-access action) but no token found. You need to provide a token or be logged in to Hugging Face with `huggingface-cli login` or `huggingface_hub.login`. See https://huggingface.co/settings/tokens." ] } ], "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": null, "metadata": { "id": "YkpIZ9Z0gJYI" }, "outputs": [], "source": [ "trainer.push_to_hub(commit_message=\"Training complete\")" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "id": "LjYQCQeigJYI" }, "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": 41, "metadata": { "id": "R687bCK5gJYI", "outputId": "896f7ed1-7179-4ef5-fedf-3b571a67d6ea", "colab": { "base_uri": "https://localhost:8080/" } }, "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": 42, "metadata": { "id": "YW2dXicYgJYN" }, "outputs": [], "source": [ "from torch.optim import AdamW\n", "\n", "optimizer = AdamW(model.parameters(), lr=2e-5)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "id": "3dpCXwGPgJYO" }, "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": 44, "metadata": { "id": "iS_haGsHgJYO" }, "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": 45, "metadata": { "id": "fEYIaqhNgJYO", "outputId": "7bbf6ea8-5a40-4bd4-d565-b90ddf182166", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'koakande/bert-finetuned-ner-accelerate'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 45 } ], "source": [ "from huggingface_hub import Repository, get_full_repo_name\n", "\n", "model_name = \"bert-finetuned-ner-accelerate\"\n", "repo_name = get_full_repo_name(model_name)\n", "repo_name" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "id": "Qd8YB-6ygJYO", "outputId": "f25a3b5f-da7a-4d89-bc1e-981da2a1da6c", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:127: FutureWarning: 'Repository' (from 'huggingface_hub.repository') is deprecated and will be removed from version '1.0'. Please prefer the http-based alternatives instead. Given its large adoption in legacy code, the complete removal is only planned on next major release.\n", "For more details, please read https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http.\n", " warnings.warn(warning_message, FutureWarning)\n", "Cloning https://huggingface.co/koakande/bert-finetuned-ner-accelerate into local empty directory.\n", "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/koakande/bert-finetuned-ner-accelerate into local empty directory.\n" ] } ], "source": [ "output_dir = \"bert-finetuned-ner-accelerate\"\n", "repo = Repository(output_dir, clone_from=repo_name)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "id": "iQEV4cF3gJYO" }, "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": 48, "metadata": { "id": "qgmZhuaNgJYO", "outputId": "c27ec276-7215-450f-8560-2302475e992a", "colab": { "base_uri": "https://localhost:8080/", "height": 103, "referenced_widgets": [ "dd5e2c234b5d4cd8b5b79c9adf789799", "7a88edbae055466d981133ae27d2606f", "60b81ee439c64f0185bd0ac5a38962a5", "af034b33b77648b0ab4079d6444c2f1b", "6ef4021dc34b4079b6306b2ade742b09", "f18509bed31045bfb9848a71cb134206", "845692520c5c4525a59c01e2d387b8bc", "adb2383205074ae3b5e069cb6e0b61f6", "0229692a2dc74c24b920dbde42b9bde4", "f7201507eb1a49a88eb0ba8c2505b6f7", "590c7302977245ca8449a0258fdf12be" ] } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/5268 [00:00
Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
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