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command above and uncomment the following one.\n", + "# ! pip install git+https://github.com/huggingface/transformers.git" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kRRjAH_ZAwxi" + }, + "source": [ + "# Text classification" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "hide_input": true, + "id": "mJcVlLqoAwxj", + "outputId": "0110dcb9-d1b2-4c73-dc83-a4d38de20c51" + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#@title\n", + "from IPython.display import HTML\n", + "\n", + "HTML('')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xi5wf8QxAwxk" + }, + "source": [ + "Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production for a wide range of practical applications. One of the most popular forms of text classification is sentiment analysis, which assigns a label like 🙂 positive, 🙁 negative, or 😐 neutral to a sequence of text.\n", + "\n", + "This guide will show you how to:\n", + "\n", + "1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [IMDb](https://huggingface.co/datasets/imdb) dataset to determine whether a movie review is positive or negative.\n", + "2. Use your finetuned model for inference.\n", + "\n", + "\n", + "The task illustrated in this tutorial is supported by the following model architectures:\n", + "\n", + "\n", + "\n", + "[ALBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/albert), [BART](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/bart), [BERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/bert), [BigBird](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/big_bird), [BigBird-Pegasus](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/bigbird_pegasus), [BioGpt](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/biogpt), [BLOOM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/bloom), [CamemBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/camembert), [CANINE](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/canine), [ConvBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/convbert), [CTRL](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/ctrl), [Data2VecText](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/data2vec-text), [DeBERTa](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/deberta), [DeBERTa-v2](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/deberta-v2), [DistilBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/distilbert), [ELECTRA](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/electra), [ERNIE](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/ernie), [ErnieM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/ernie_m), [ESM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/esm), [FlauBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/flaubert), [FNet](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/fnet), [Funnel Transformer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/funnel), [GPT-Sw3](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gpt-sw3), [OpenAI GPT-2](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gpt2), [GPTBigCode](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gpt_bigcode), [GPT Neo](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gpt_neo), [GPT NeoX](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gpt_neox), [GPT-J](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/gptj), [I-BERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/ibert), [LayoutLM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/layoutlm), [LayoutLMv2](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/layoutlmv2), [LayoutLMv3](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/layoutlmv3), [LED](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/led), [LiLT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/lilt), [LLaMA](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/llama), [Longformer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/longformer), [LUKE](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/luke), [MarkupLM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/markuplm), [mBART](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mbart), [MEGA](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mega), [Megatron-BERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/megatron-bert), [MobileBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mobilebert), [MPNet](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mpnet), [MVP](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mvp), [Nezha](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/nezha), [Nyströmformer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/nystromformer), [OpenLlama](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/open-llama), [OpenAI GPT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/openai-gpt), [OPT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/opt), [Perceiver](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/perceiver), [PLBart](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/plbart), [QDQBert](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/qdqbert), [Reformer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/reformer), [RemBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/rembert), [RoBERTa](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/roberta), [RoBERTa-PreLayerNorm](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/roberta-prelayernorm), [RoCBert](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/roc_bert), [RoFormer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/roformer), [SqueezeBERT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/squeezebert), [TAPAS](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/tapas), [Transformer-XL](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/transfo-xl), [XLM](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/xlm), [XLM-RoBERTa](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/xlm-roberta), [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/xlm-roberta-xl), [XLNet](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/xlnet), [X-MOD](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/xmod), [YOSO](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/yoso)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Before you begin, make sure you have all the necessary libraries installed:\n", + "\n", + "```bash\n", + "pip install transformers datasets evaluate\n", + "```\n", + "\n", + "We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "45ca9b09528043dda2319aad7daaa6fc", + "12a2b94c4b484408bfe25e53f82b03a2", + "c51ec2777b5a442691e6d321c01587a7", + "562706b999e64397a62dac2b1ecca2f4", + "fdfff33cd4b747d5b429c686f7698109", + "6e9ba47d02b74d79a051fd4f93003898", + "04a92d7f92404bd6b02c7cb3f9ad9ea2", + "e7ea0688809a4398a630625b2d55bf43", + "5106d26986504884b35be9e9c060ffbc", + "4f100334000443119a0b4f3c01eedb26", + "3b840a16f81647e890f63bc1c74fcfb7", + "e33d15b12c454bc6992432191cde43f3", + "30b432685bad4bc3840c27f3dc9a601e", + "d93d01bc2ac84bf2a3d2150a039b37aa", + "41eb20b10e6444689ee24a1c5d1ea05a", + "5e9768b2fbe74d069749f7f16ada624b", + "62b27bad20ef4caf9fcfe689bc36c898", + "1b8807553f544094a0766b8d0ef903ef", + "f778710c6025428faaa800b54153c482", + "a73001b7e4734e77a3053c5ed9cb3973", + "4c9d73349dfc4d0ea4f5df1a06d6439f", + "2e4d761451364692b6bef51eb73aa625", + "11535c67ade84a1683d0b0dd34b56eaf", + "5cd9b00387c84890ab7a80cd7191e119", + "574bd83e55a641dba44a0aacf976f725", + "7276bf4f09d94390a1eba8c2f59fd1fd", + "d1909a4c3cd04d5cac53f411e99055e9", + "aa002452216d4f819acf7e93eaa4eb8a", + "179777e9abec4859aacbaac1c2c8b142", + "aced0c3a2c2f4520bc278286ea97fac5", + "035e78173d184616a5731f4333f89247", + "46341b20b32a468f85a91a33dd7ef07c" + ] + }, + "id": "0zrBI8sBAwxl", + "outputId": "089ff3a0-c92e-4954-bd58-f71784b2204b" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
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satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->evaluate) (1.16.0)\n", + "Installing collected packages: responses, evaluate\n", + "Successfully installed evaluate-0.4.1 responses-0.18.0\n" + ] + } + ], + "source": [ + "pip install evaluate" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "8cb261a806ec4e11819c6524148d96f3", + "ae012b210d2846e3a4b149d40981d013", + "bfd041c9ebef482b9939e0361bd92f3c", + "b73090259ead49b2bbd870dc8a463203", + "f3ca415db5ad4ea8a32f4eff6fa521a8", + "44243b4f97ac42cd95867fc8580f5db6", + "8a784cafaef542a78a79a542503e219a", + "6d1f4656d10c44729805d48b322ea07e", + "738c72f878e2433e89edeebc0e01c274", + "761108aea8a3427a9c1a81b2b7713a39", + "de7845805e3a4ff08a45405649fe4d3d" + ] + }, + "id": "OlWJIwc6Awxo", + "outputId": "92332359-65cb-46c0-f290-723158ea917e" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading builder script: 0%| | 0.00/4.20k [00:00\n", + "\n", + "If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](https://huggingface.co/docs/transformers/main/en/tasks/../training#train-a-tensorflow-model-with-keras)!\n", + "\n", + "\n", + "To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "nGQyRjTuAwxo" + }, + "outputs": [], + "source": [ + "from transformers import create_optimizer\n", + "import tensorflow as tf\n", + "\n", + "batch_size = 16\n", + "num_epochs = 5\n", + "batches_per_epoch = len(tokenized_data[\"train\"]) // batch_size\n", + "total_train_steps = int(batches_per_epoch * num_epochs)\n", + "optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EjT2wNgaAwxo" + }, + "source": [ + "Then you can load DistilBERT with [TFAutoModelForSequenceClassification](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.TFAutoModelForSequenceClassification) along with the number of expected labels, and the label mappings:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 140, + "referenced_widgets": [ + "eeb7cb3226964e8cb6c9ab812e508d72", + "c787ee63a90646e0aca0e84622b2c205", + "79c224da2ec74aa5ad9c7de14438612f", + "939db04576a44ad9a48db8b81045bc9c", + "630da9b7ba0c4be0a7c88f4a96c17a9a", + "97261511cb4c4b27b30a0d2c65a12913", + "d76cc9b02e1c402e8a6879168aac28b6", + "f59cddd8264b4426bdb86906225fd5c0", + "d830e86804ac41749d96ae81a846c5fc", + "cb61dab0e460494680091550d42a1eeb", + "b351e6b493974789aed5baf031de24b0" + ] + }, + "id": "Czp_lnZRAwxp", + "outputId": "2b1cea42-c719-4583-fe98-f6c073a360f9" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/1.12G [00:00 and will run it as-is.\n", + "Cause: for/else statement not yet supported\n", + "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "WARNING: AutoGraph could not transform and will run it as-is.\n", + "Cause: for/else statement not yet supported\n", + "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n", + "1000/1000 [==============================] - ETA: 0s - loss: 0.8384 - accuracy: 0.6678" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Adding files tracked by Git LFS: ['tokenizer.json']. This may take a bit of time if the files are large.\n", + "WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['tokenizer.json']. This may take a bit of time if the files are large.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1000/1000 [==============================] - 433s 336ms/step - loss: 0.8384 - accuracy: 0.9230 - val_loss: 0.2463 - val_accuracy: 0.9230 - precision: 0.9256 - recall: 0.9230 - f1: 0.9221\n", + "Epoch 2/3\n", + "1000/1000 [==============================] - 333s 333ms/step - loss: 0.2072 - accuracy: 0.9360 - val_loss: 0.1811 - val_accuracy: 0.9360 - precision: 0.9379 - recall: 0.9360 - f1: 0.9356\n", + "Epoch 3/3\n", + "1000/1000 [==============================] - 351s 351ms/step - loss: 0.1468 - accuracy: 0.9440 - val_loss: 0.1545 - val_accuracy: 0.9440 - precision: 0.9458 - recall: 0.9440 - f1: 0.9436\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Evaluate your model on the test set\n", + "evaluation_result = model.evaluate(tf_test_set)\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WbTW9AF-Lze3", + "outputId": "d4ea5fc9-88f4-442b-a323-19ddd7430b58" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "125/125 [==============================] - 9s 69ms/step - loss: 0.1694 - accuracy: 0.9410\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9fqIYMvwAwxt" + }, + "source": [ + "Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!\n", + "\n", + "\n", + "\n", + "For a more in-depth example of how to finetune a model for text classification, take a look at the corresponding\n", + "[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)\n", + "or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb).\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X1VBMsD3Awxu" + }, + "source": [ + "## Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q1tsnVT4Awxu" + }, + "source": [ + "Great, now that you've finetuned a model, you can use it for inference!\n", + "\n", + "Grab some text you'd like to run inference on:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "2wadXJm5Awxu" + }, + "outputs": [], + "source": [ + "text = \"This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kfv3frDSAwxu" + }, + "source": [ + "The simplest way to try out your finetuned model for inference is to use it in a [pipeline()](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.pipeline). Instantiate a `pipeline` for sentiment analysis with your model, and pass your text to it:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 227, + "referenced_widgets": [ + "32e224c94d2b4c0eb914c2dc38362183", + "3507d1e7cb634af69342e6fb62c14ea5", + "c3f690db42a5483ca39712054d9bf35f", + "06791024f97f43f49b82d52e641bf6ab", + "f4f4edd966504407aeef2efbc483976c", + "8dd64da214c54b64877c40228a6db7b9", + "21f9d125541943fbb26c613e352f9e8f", + "10dcc98aa47d435c923032f78c00c770", + "b5ff2580d21c47ff95e966b99f5ecc50", + "2eaa693da433466fbe76eed0e06309c6", + "499ce9a03d8a4722ac070932b8abffbf", + "039e00cc037449b08b9d18c252562676", + "f379f19196304c3eb01cedaf3a341364", + "b3d3d56066cc40a5919aa84daf2a28b8", + "ef3ea0fcd4084b1a833da13336560830", + "4d9e276dc8504b13b11e1e80500d9a1b", + "c3f7747bdf9e42b3a7fbab496f67dcca", + "c1961458fb5a411ea58a1a039a2f015a", + "93376e35b3c84345a74631b00e9028cc", + "32e574b3467d488685fa28fa82417911", + "a13694fa8254466fab7c08feb214738c", + "9293e7ebb8184340b6948f8916cd3386", + "006698694f7a4580b4d63d8f85540c85", + "bff451bbaa9c4320878de34c76d02ea1", + "d8b49b85a4964b9b8eac2f78d2d5a0c3", + "32841c91e63c49f08d8e4fdba6c0726f", + "918ae71c46a14d5eaf43c94a9997a9cf", + "31bc467ca1c14c11aefb292e65c0df0c", + "930d99bbbca446f7ad6267d223e9de99", + "59fcc98a8ada44bcb51af068ff8e853d", + "fd33074464ff448e927a8b1db26d8004", + "3d0381e4e45b4bfd80f566dc4485d5dd", + "a48b6669bb094c44b57a33a20ce36f2f", + "bb16dbac96cc4d2183b97903a25f63c2", + "dfa01a95f765486eb4c47f3f077ec9a2", + "34142d7b612a4b01ab2238ee3d6a7d31", + "fd49396447a04b2193046bc54d1dce9e", + "c30e65061f9b471dac14a8b87e83dfc6", + "a8f40c04f8b7474dba645c2638c34479", + "a8a066742e3646d59f43b7a6d01c7f90", + "0f200191c5694151be898282bec944d9", + "26171a04243b424f87fb504ecc388236", + "cd493689f4ef4a53a72fecba5198fbfe", + "ce2f763ab9eb4823b2a1702d547a24a4", + "dd235ee865d64f938b88d3f1edf64743", + "6546c29797a543258066b726d21a3902", + "63969fbc9d2048c5bdde5bc8a6a40ffe", + "bc6fef5f35df473497a386ad46f6e91a", + "be1a78b62e3f497c84373746101def37", + "dbfd105e2b914557aca3cff986ac7687", + "1348c523ced74aab976a95b27d03538f", + "6563b138cf0643dab881f933a05f7e8c", + "e93b48e2cd9044888d87626f33c41b65", + "ff801d7eb3344de78f8678fe641af55d", + "9692b54e2a3348699019285ea6a10ef6", + "a4d82f54024a412aaa22a0d4d025fb41", + "c4327208d2e542ffbb528001bc3da8f3", + "4dcf7748a70a4bc2b6951253cd647706", + "11d7d7813da840f9820b434353d4297a", + "90ecaeb7906a4036b5c2a9adb7326148", + "36c44b47aa394d19950c88260d45abd8", + "6ce4243e524447418bf01ec318323b6b", + "9c32cd6b4e5c43ac80bf0c99993d26d2", + "b702c7b510c141ffa246d7e5648ad5f0", + "ef22a1e097ea4cf89628d45e6824077a", + "f7bedee4ee014ddfb9a39f7b80596822" + ] + }, + "id": "cmV1hPffAwxu", + "outputId": "60510fcf-191a-431e-e309-cd5390c92732" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/538 [00:00
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