{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fine-tuning for multi-label text classification\n", "**Note: This notebook was run in Google Colab**\n", "\n", "This notebook demonstrates how to fine-tune a `bert-base-uncased` model using this Kaggle [dataset](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)\n", "\n", "The Colab link is here: https://colab.research.google.com/drive/1_tOvmArkigdQpxhZhzVIhR58InDHrxPz\n", "\n", "## Setup Environment\n", "We first install and import all the necessary libraries and modules." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wybe5jQM1NLf", "outputId": "2f86abba-83b8-4d89-dcf8-3dec710886e0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting transformers\n", " Downloading 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"execution_count": null, "metadata": { "id": "hQN-HmXXW6SA" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import torch\n", "from sklearn.model_selection import train_test_split\n", "from torch.utils.data import Dataset\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n", "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Dataset\n", "Here we extract training and validation datasets from `train.csv`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WtsAFyrzWuCr" }, "outputs": [], "source": [ "# Read dataset and extract all texts and labels\n", "df = pd.read_csv(\"/content/drive/MyDrive/AI_project/data/train.csv\")\n", "\n", "train_texts = df[\"comment_text\"].values\n", "labels = df.columns[2:]\n", "id2label = {idx:label for idx, label in enumerate(labels)}\n", "label2id = {label:idx for idx, label in enumerate(labels)}\n", "train_labels = df[labels].values\n", "\n", "# Randomly select training texts and respective labels\n", "np.random.seed(18)\n", "small_train_texts = np.random.choice(train_texts, size=25000, replace=False)\n", "\n", "np.random.seed(18)\n", "small_train_labels_idx = np.random.choice(train_labels.shape[0], size=25000, replace=False)\n", "small_train_labels = train_labels[small_train_labels_idx, :]\n", "\n", "# Split data into training data and validation data with a percentage of 80% vs 20%\n", "train_texts, val_texts, train_labels, val_labels = train_test_split(small_train_texts, small_train_labels, test_size=.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Preprocessing\n", "As models like BERT don't expect text as direct input, but rather `input_ids`, etc., we tokenize the text using the tokenizer. The `AutoTokenizer` will automatically load the appropriate tokenizer based on the checkpoint on the hub. We can now merge the labels and texts to datasets as a class we defined." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "6e18d8e88c4c455f8c8acc8cf83ef38d", "669265aa86c74a35b257548449092362", "c191a7b285724d5ab7d962f36faebad6", "0785b592287643beb0546cc1ec7edd53", "14b87475585e46f48f33d967bd092fb1", "18382b77a8b949069144899ce4df2b7f", "c369d1887b8f480fb7281e7ac212537a", "d87c9eec619843e58160cf5ec79032d6", "c1629d36516647efa3e98e1cd71c6a74", "2b408fc48eae4330b2452da6c952ef08", "5bc464a714184d1586d0ae93e6044a34", "5d869754c9e94f999c55af68012caa22", "f7e2d957d5f941d1918fbf30754350d5", "3296da063e504b86b9e67e7d8baa4f96", "150d64b514bd49d99124ee45296de676", "4022514810d5404f93f8a90d683a6b84", "9eb1adc164a442b999d7b965ee3d3398", "1daff565766948a2ac40a9700eefd668", "a0d9a35c2f4b4f37bf5c9d06fb44d0f0", "1b1a0b6b1118444eafd071e7429d8a8e", "9c248ee9e4c74e48b881909532b71cf0", "98bf983675de497897154e358bdfa8ac", "0ec8ea8c9b02489295985bd6767cda5b", "25967142b0d841c4be3704affde1f3a0", "e0451cc5f6f74ddf94017bdd48817e05", "396b691570cf460181d46b3eb36a3b82", "17acfb2b88064fc683eb5bcb64389a04", "48336cf934eb45968604e9bb40e04e05", "37435accef354428b8b9e45a10a4b103", "aede7eeb0a714033b64105e3db96816c", "d50189d73a6543128e5a2d6134b8ab96", "0502a391770f4549b30801d241d0c7e9", "ce1dd99060824d3e95be42e89735965e", "09fee717abef4084bc9b1e1c765527a4", "ad300d0d30ce43a58788c79ff6a363d8", "95fdfe9dc41c4ca28ece249529b4ae3a", "de918c551d334f80a2db5077ccc9fd1c", "eb316534775846b5a755f62d48ff19aa", "7f789bfea7094505bf4f05a720dee7d8", "7ccd0fa35c324a1e8acb9f2bc2b672f9", "aa05e364db114294be272653c0b83e46", "e795934f49f2446cabc78be8c33dd8e6", "dfc93871fa744bdebff63b293e6cda52", "b6e07a8d5059495ca0b35446ebe0d923" ] }, "id": "pPgvgOaYXb2f", "outputId": "338106c8-da47-4d6f-d1bd-f21e383d8aab" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6e18d8e88c4c455f8c8acc8cf83ef38d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading (…)okenizer_config.json: 0%| | 0.00/28.0 [00:00 \n", " \n", " \n", " [6250/6250 2:51:28, Epoch 5/5]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "TrainOutput(global_step=6250, training_loss=0.03428032752990723, metrics={'train_runtime': 10293.1001, 'train_samples_per_second': 9.715, 'train_steps_per_second': 0.607, 'total_flos': 2.63120504832e+16, 'train_loss': 0.03428032752990723, 'epoch': 5.0})" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\", \n", " problem_type=\"multi_label_classification\", \n", " num_labels=len(labels),\n", " id2label=id2label,\n", " label2id=label2id)\n", "model.to(device)\n", "\n", "training_args = TrainingArguments(\n", " output_dir=\"finetuned-bert-uncased\",\n", " evaluation_strategy = \"epoch\",\n", " save_strategy = \"epoch\",\n", " learning_rate=2e-5,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " num_train_epochs=5,\n", " load_best_model_at_end=True,\n", " push_to_hub=True,\n", ")\n", "\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_dataset,\n", " eval_dataset=val_dataset,\n", " tokenizer=tokenizer\n", ")\n", "\n", "trainer.train()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Post-train\n", "Now we can push the trained model to HuggingFa" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 276, "referenced_widgets": [ "c24206cd958c41c983d0c8dbaa9fb491", "508ac1895700407a9094534c049efa70", "d2b7a9b1ae3e400ea2e6995f4240a98d", "b1dcaf8843ea495fb6d9ecbbb78ac41a", "e295b3acc61d455c95bcc3673eb42110", "40800ac32a7e4adea537af1bc2761526", "a1856d09f291407290d06aede3a3ce40", "433114b59b0241cf938b300a53405ffc", "dadcf66ee7f948468821377dd6c440c5", "938b3f4e42fe46d49c2376eef010a7a7", "e24eecbe187f4e17a417017055d9f409" ] }, "id": "T-VyJbD_gMkx", "outputId": "6373a877-48b8-43f4-c797-6908abc3b778" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c24206cd958c41c983d0c8dbaa9fb491", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload file runs/Apr30_02-24-35_5690a136cd0d/events.out.tfevents.1682821590.5690a136cd0d.712.0: 0%| …" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "To https://huggingface.co/andyqin18/finetuned-bert-uncased\n", " fe51685..cf13f54 main -> main\n", "\n", "WARNING:huggingface_hub.repository:To https://huggingface.co/andyqin18/finetuned-bert-uncased\n", " fe51685..cf13f54 main -> main\n", "\n", "To https://huggingface.co/andyqin18/finetuned-bert-uncased\n", " cf13f54..ceb65db main -> main\n", "\n", "WARNING:huggingface_hub.repository:To https://huggingface.co/andyqin18/finetuned-bert-uncased\n", " cf13f54..ceb65db main -> main\n", "\n" ] }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/andyqin18/finetuned-bert-uncased/commit/cf13f54cbb5ba93b74ad449c875efa268f647504'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.push_to_hub()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 124 }, "id": "IZgRksP7ng8m", "outputId": "b1e55e4a-1db6-4f26-d742-2ce399193c16" }, "outputs": [ { "data": { "text/html": [ "\n", "

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