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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": { + "id": "0zrBI8sBAwxl", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "38a4bdd86da54b1696affaa0ef3a3ab6", + "d4316538d622421b888971ed0f57d30f", + "488f0f9b6cab438387859a480572ba03", + "c4caa0387e574b3b925f56739fa1340e", + "8a92f5e5699f48b29cead39954616b4e", + "ff89057a397344fe97db33a4d0be5756", + "c228dfad62e84b34af9da819126da1fa", + "b3a8ca1c389a4c4db29660e7657500b2", + "95c1fba864e9470987c33eef6a16131e", + "2a228ff67ca9416296b0139567764a63", + "c5a281440e9a47229a259f306416a856", + "122f6b9f48d048a289d2769818591175", + "4f49a70c56f94d749ebfbd739e4090f2", + "8dbe1e78934d44b38807d9b8bc667c79", + "153bdc927f944d8b88024fb52f445c12", + "e1bfb813b3534cb88f2818befce5ede1", + "8434e19c49584477a93331895c6e4263", + "15fddc292ad24b0da0d168e58c537d50", + "e6111357e7254b3592891f1d4a2e6619", + "56dff133dad04a6f85c862e3b43e7251", + "57bd4b32d7cb404d9f7353dce53f9f50", + "4efd27e3bd9140448fc8b352445515f4", + "ac563bfef13a4f828863a50da60bae47", + "46a4dc93769d4c12beeaa49a446b29c5", + "d039054864604b329a931ac39ec8fb05", + "9d810e1343a64fabb3b3ce4e7310376d", + "455d8af6cf7145d49bc67f8ef0d74ad8", + "2903cd1c7c9544daa5b67f01dc0c48af", + "0c72803b8fc74ef49a1946f24aa55d0e", + "dac3ed5a0a6247abbc062f58a79b13ed", + "0f2e85c6524a4bf2bd7f3cf022c72f99", + "983f9900345c46728d829b8e2b1c889c" + ] + }, + "outputId": "f631ae28-d264-4444-e987-6d9cc58c28bf" + }, + "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" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "OlWJIwc6Awxo", + "outputId": "51b27c8e-d8c6-42dd-a7c9-bdaa6b23f764", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "1cad237c8dfa456fb80f696995f7f142", + "673ab160ce3d4ddeac31e4936828a5d4", + "892ce934e02a4b84afca3741eef032cb", + "dde5480cb6ec48e38bd84ddb0d35c262", + "50d7abbb1bbc4476906001db152527c6", + "02aa18bf37e444ed8660b688d5a6c64c", + "77fa461991274e4a84ec3ea58a1b8a82", + "89e9df06577b4f63bbca4d085027c355", + "e974259855f04fa3921ad3d9a7ef8db3", + "7ac046e51bd84ddfbd24b735192b7ab8", + "9328c95b65a044598f3fddcc4b212053" + ] + } + }, + "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": 18, + "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": 21, + "metadata": { + "id": "Czp_lnZRAwxp", + "outputId": "a53e515d-f649-4ad0-8304-b6a50cfa9ce7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 158, + "referenced_widgets": [ + "f3e1e071fe834e56aca08e1e632de099", + "a309dcc2352c4d5a82399e7499ed7181", + "11c375a2060341f787e328824bf51547", + "860a2427f2f44d638b072d74d0dc3eac", + "9ce5af82a4a94ca09a56b6baab435784", + "4801ca7739da47a3a95163e0f8e31abb", + "8399f10684d34eb6a325ed301aac7a53", + "3cd68c5f42634efcaea448449ff280b1", + "0e95a930687449088bcf2f4b9c4910e9", + "62cecc4eb0da46e3ba1a61d4e010c73e", + "ae41e7a0cc8340039df3c4f2579bb7ad" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "pytorch_model.bin: 0%| | 0.00/329M [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 [==============================] - 203s 149ms/step - loss: 0.1519 - val_loss: 0.1257 - accuracy: 0.9495\n", + "Epoch 2/3\n", + "1000/1000 [==============================] - 143s 143ms/step - loss: 0.1226 - val_loss: 0.1140 - accuracy: 0.9520\n", + "Epoch 3/3\n", + "1000/1000 [==============================] - 136s 136ms/step - loss: 0.1092 - val_loss: 0.1145 - accuracy: 0.9520\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 28 + } + ], + "source": [ + "model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)" + ] + }, + { + "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": 29, + "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": 30, + "metadata": { + "id": "cmV1hPffAwxu", + "outputId": "9c6cf427-ddee-4ed8-d176-c4231211e7a8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 227, + "referenced_widgets": [ + "c9a3f511474342d895d4cc2bc24126cf", + "b68631ce4fed4527aedc04170aaae080", + "a36d2982917440d585463979614376bd", + "9b8cd57e69f64e87b7918def81fd49c2", + "45da32490a43498d87c9992050d61e6c", + "64faf61958ba48b99c34c2217cf11e75", + "afdfedc9e2dd490d8d6d381aded4fb78", + "2279b416553e47e586b0ce4f37b64531", + "5704d7b1119e43bcb19d1c2e13f67919", + "2bac194722ba425c9fdc482477a8c3b4", + "a90c53ef8f804be9acab14ee017a195e", + "e2905e75cbb3448ba80296ee85e5b52c", + "1cf8bef08a544503b616e93b7abcdaac", + "6798eef34be8452099dfab7551ad92a1", + "309d93af844c423693381eadab745178", + "035335b7c9d640788ea6564adf2d3c40", + "e809289431d844a7968c0cb1a5ea7d49", + "e41aca0848144b759306b34224801838", + "8a90f15791f34d52a323d8d0dbebcd9a", + "5a0e08ee92684f4d8ba9a7a0c0bde8cf", + "453c15114cad424a99bf3f56d112cd77", + "25d103bd761a4abfba608a1e4ab59124", + "62bae2f5fd5641a78a2b4bff3a936d22", + "339cf28ad9d44be89793f75bbd514aa8", + "7d05f3f291e64e49af2d9c0e512ec770", + "b1db54c5282d44038f717d51b618719b", + "8e809b46db33493183ca44f9adb6dd61", + "2afb65ae1acc4afba61a0c58f0727948", + "316fa99aa56d46e1a151d22781846c13", + "7a67aad49abb40deb6d4416b0e072eca", + "097868f04fb34fe4952412c2a40eb44b", + "d472c95400fc4c648377b5efc641c6ad", + "4420492219874254b59b21d313f9d5f3", + "85d7ddcbdeb748f18385de340d2e7b9e", + "85a0b4d479674141a52b65cb4126b88f", + "fcc57767df6f48d9a7cbae68ba421ae3", + "41c96a66dbbf40629e4b0c4dd183288e", + "6c088e6ba3534016810fd1f7e504b467", + "06c21b2a832d4085b0a55b8907703616", + "2068082624834a87a39ce4a9810b6a11", + "22f061feb30948e88872e200675ac446", + "358a9c6cd763475d8eb4be4f66afca27", + "5000e718ae0e4093bbb89620ff2c8232", + "681389d10d75469aa1efcd2e1ad4e11d", + "ba2cfd5ce0954306bb3ebfb014827470", + "9d709cff28324abca19c69e43a35e5f9", + "323c02af2a664b0c8b5d620ec4059d5e", + "e9773a4a4fd44d7da2168ea128d78914", + "9035638627f143029ca2f66e07397dd9", + "c3c8e7be72e540ad8cee0222f46a979b", + "b12d1541711c46c1b8dc993f6f9b4271", + "b6df444887834af48b26c140f45ec3ac", + "364f77d2cf27414baab2aecce54a2fcd", + "48199cec542545d2aee629746a0afe9b", + "274dc46705a842249a295f9a93384af8", + "91ea8feef4274884b060146b2ec0006d", + "b61589de18b54393aab7dddb6e2c7d96", + "386d8cbf2a60405ebf4bd16152b897e8", + "3c534c0c8afd4ff89aa18fb839d305c5", + "5e6d56eef5e5476282ee71784ef15e61", + "92d72e098876444bb9d2b3693088cff4", + "305c9af800004e2b81768d346340df69", + "b15e127d10e34f45a327138ef78c01c0", + "c3100e1559eb46d8af3469fa538d1a8e", + "805fb05f249b47f2a8ed916faf842bca", + "a8b157a28c464677b0bb732795a6df2d" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/538 [00:00
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