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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "joTaXUsFAwxg",
+ "outputId": "58b4978a-fc71-4b78-f002-f7c0c2e56ebb"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.2)\n",
+ "Collecting datasets\n",
+ " Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25hCollecting multiprocess (from datasets)\n",
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+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
+ "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
+ "Successfully installed datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16 xxhash-3.4.1\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Transformers installation\n",
+ "! pip install transformers datasets\n",
+ "# To install from source instead of the last release, comment the 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",
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+ "fdfff33cd4b747d5b429c686f7698109",
+ "6e9ba47d02b74d79a051fd4f93003898",
+ "04a92d7f92404bd6b02c7cb3f9ad9ea2",
+ "e7ea0688809a4398a630625b2d55bf43",
+ "5106d26986504884b35be9e9c060ffbc",
+ "4f100334000443119a0b4f3c01eedb26",
+ "3b840a16f81647e890f63bc1c74fcfb7",
+ "e33d15b12c454bc6992432191cde43f3",
+ "30b432685bad4bc3840c27f3dc9a601e",
+ "d93d01bc2ac84bf2a3d2150a039b37aa",
+ "41eb20b10e6444689ee24a1c5d1ea05a",
+ "5e9768b2fbe74d069749f7f16ada624b",
+ "62b27bad20ef4caf9fcfe689bc36c898",
+ "1b8807553f544094a0766b8d0ef903ef",
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+ "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=' =2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.18.0)\n",
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+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2023.4)\n",
+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.1)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (23.2.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.4.1)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (6.0.5)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.9.4)\n",
+ "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.3)\n",
+ "Requirement already 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": [
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+ },
+ "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": [
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+ "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",
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+ "config.json: 0%| | 0.00/538 [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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