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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "joTaXUsFAwxg",
+ "outputId": "244e05d4-687e-4833-ebad-77d68d482000",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "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[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
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+ "\u001b[?25hCollecting multiprocess (from datasets)\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.2.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\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": [
+ "VIDEO "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#@title\n",
+ "from IPython.display import HTML\n",
+ "\n",
+ "HTML('VIDEO ')"
+ ]
+ },
+ {
+ "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",
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+ "4efd27e3bd9140448fc8b352445515f4",
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+ "46a4dc93769d4c12beeaa49a446b29c5",
+ "d039054864604b329a931ac39ec8fb05",
+ "9d810e1343a64fabb3b3ce4e7310376d",
+ "455d8af6cf7145d49bc67f8ef0d74ad8",
+ "2903cd1c7c9544daa5b67f01dc0c48af",
+ "0c72803b8fc74ef49a1946f24aa55d0e",
+ "dac3ed5a0a6247abbc062f58a79b13ed",
+ "0f2e85c6524a4bf2bd7f3cf022c72f99",
+ "983f9900345c46728d829b8e2b1c889c"
+ ]
+ },
+ "outputId": "f631ae28-d264-4444-e987-6d9cc58c28bf"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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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"
+ ]
+ }
+ ]
+ },
+ {
+ "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, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1cad237c8dfa456fb80f696995f7f142"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "import evaluate\n",
+ "\n",
+ "accuracy = evaluate.load(\"accuracy\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "f5Fe2iHxAwxo"
+ },
+ "source": [
+ "Then create a function that passes your predictions and labels to [compute](https://huggingface.co/docs/evaluate/main/en/package_reference/main_classes#evaluate.EvaluationModule.compute) to calculate the accuracy:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "id": "molPHOqrAwxo"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "\n",
+ "def compute_metrics(eval_pred):\n",
+ " predictions, labels = eval_pred\n",
+ " predictions = np.argmax(predictions, axis=1)\n",
+ " return accuracy.compute(predictions=predictions, references=labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FR3-5XWtAwxo"
+ },
+ "source": [
+ "Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1FYcve-MAwxo"
+ },
+ "source": [
+ "## Train"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TrrL93_2Awxo"
+ },
+ "source": [
+ "Before you start training your model, create a map of the expected ids to their labels with `id2label` and `label2id`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "id": "J_F65yLBAwxo"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nw24AD-fAwxo"
+ },
+ "source": [
+ "\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, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f3e1e071fe834e56aca08e1e632de099"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFRobertaForSequenceClassification: ['roberta.embeddings.position_ids']\n",
+ "- This IS expected if you are initializing TFRobertaForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing TFRobertaForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "All the weights of TFRobertaForSequenceClassification were initialized from the PyTorch model.\n",
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaForSequenceClassification for predictions without further training.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import TFAutoModelForSequenceClassification\n",
+ "\n",
+ "label2int = {\n",
+ " \"sadness\": 0,\n",
+ " \"joy\": 1,\n",
+ " \"love\": 2,\n",
+ " \"anger\": 3,\n",
+ " \"fear\": 4,\n",
+ " \"surprise\": 5\n",
+ "}\n",
+ "\n",
+ "# Create id2label dictionary by reversing key-value pairs of label2int\n",
+ "id2label = {v: k for k, v in label2int.items()}\n",
+ "\n",
+ "# Create label2id dictionary from label2int\n",
+ "label2id = label2int\n",
+ "\n",
+ "model = TFAutoModelForSequenceClassification.from_pretrained(\n",
+ " \"gokuls/distilroberta-emotion-intent\", num_labels=6, id2label=id2label, label2id=label2id,from_pt=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "w294LOHxAwxp"
+ },
+ "source": [
+ "Convert your datasets to the `tf.data.Dataset` format with [prepare_tf_dataset()](https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "DKfFf5GBAwxp"
+ },
+ "outputs": [],
+ "source": [
+ "tf_train_set = model.prepare_tf_dataset(\n",
+ " tokenized_data[\"train\"],\n",
+ " shuffle=True,\n",
+ " batch_size=16,\n",
+ " collate_fn=data_collator,\n",
+ ")\n",
+ "\n",
+ "tf_validation_set = model.prepare_tf_dataset(\n",
+ " tokenized_data[\"test\"],\n",
+ " shuffle=False,\n",
+ " batch_size=16,\n",
+ " collate_fn=data_collator,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "P8VInog4Awxp"
+ },
+ "source": [
+ "Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "sMKZ-zqEAwxp"
+ },
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "\n",
+ "model.compile(optimizer=optimizer) # No loss argument!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gpNo6NokAwxp"
+ },
+ "source": [
+ "The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](https://huggingface.co/docs/transformers/main/en/tasks/../main_classes/keras_callbacks).\n",
+ "\n",
+ "Pass your `compute_metrics` function to [KerasMetricCallback](https://huggingface.co/docs/transformers/main/en/main_classes/keras_callbacks#transformers.KerasMetricCallback):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "ntlSYaNGAwxp"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers.keras_callbacks import KerasMetricCallback\n",
+ "\n",
+ "metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "K2wDYgolAwxt"
+ },
+ "source": [
+ "Specify where to push your model and tokenizer in the [PushToHubCallback](https://huggingface.co/docs/transformers/main/en/main_classes/keras_callbacks#transformers.PushToHubCallback):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "id": "8h2Vi6NYAwxt",
+ "outputId": "864e3815-a9e5-4150-ddd7-6114ef4e3bbb",
+ "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:131: 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/ruba2ksa/emo1 into local empty directory.\n",
+ "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/ruba2ksa/emo1 into local empty directory.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers.keras_callbacks import PushToHubCallback\n",
+ "\n",
+ "push_to_hub_callback = PushToHubCallback(\n",
+ " output_dir=\"./emo1\",\n",
+ " tokenizer=tokenizer,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "X8594Kl5Awxt"
+ },
+ "source": [
+ "Then bundle your callbacks together:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "id": "1Nl892o8Awxt"
+ },
+ "outputs": [],
+ "source": [
+ "callbacks = [metric_callback, push_to_hub_callback]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qS0SgLuRAwxt"
+ },
+ "source": [
+ "Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "id": "3-lU1kXIAwxt",
+ "outputId": "d95af6e9-facc-4571-e334-92e301a035dc",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/3\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "WARNING:tensorflow: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"
+ ]
+ },
+ {
+ "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": [
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+ "[{'label': 'LABEL_1', 'score': 0.9994940757751465}]"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 30
+ }
+ ],
+ "source": [
+ "from transformers import pipeline\n",
+ "\n",
+ "classifier = pipeline(\"sentiment-analysis\", model=\"stevhliu/my_awesome_model\")\n",
+ "classifier(text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pUIAd73mAwxu"
+ },
+ "source": [
+ "You can also manually replicate the results of the `pipeline` if you'd like:\n",
+ "\n",
+ "Tokenize the text and return TensorFlow tensors:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "id": "MnjMuYlLAwxu"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import AutoTokenizer\n",
+ "\n",
+ "tokenizer = AutoTokenizer.from_pretrained(\"stevhliu/my_awesome_model\")\n",
+ "inputs = tokenizer(text, return_tensors=\"tf\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ghPFR3W3Awxu"
+ },
+ "source": [
+ "Pass your inputs to the model and return the `logits`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "id": "2ls931AvAwxu",
+ "outputId": "b0b74785-5e2c-47fd-a351-346fc6c52e05",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "All PyTorch model weights were used when initializing TFDistilBertForSequenceClassification.\n",
+ "\n",
+ "All the weights of TFDistilBertForSequenceClassification were initialized from the PyTorch model.\n",
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForSequenceClassification for predictions without further training.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import TFAutoModelForSequenceClassification\n",
+ "\n",
+ "model = TFAutoModelForSequenceClassification.from_pretrained(\"stevhliu/my_awesome_model\")\n",
+ "logits = model(**inputs).logits"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ne4I6WwwAwxu"
+ },
+ "source": [
+ "Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a text label:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
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+ "data": {
+ "text/plain": [
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+ ],
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+ "metadata": {},
+ "execution_count": 33
+ }
+ ],
+ "source": [
+ "predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n",
+ "model.config.id2label[predicted_class_id]"
+ ]
+ }
+ ],
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