diff --git "a/milestone3/finetune.ipynb" "b/milestone3/finetune.ipynb" new file mode 100644--- /dev/null +++ "b/milestone3/finetune.ipynb" @@ -0,0 +1,18278 @@ +{ + "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 transformers-4.28.1-py3-none-any.whl (7.0 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m51.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n", + "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n", + " Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n", + "\u001b[2K 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transformers-4.28.1\n" + ] + } + ], + "source": [ + "!pip install transformers" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AYvuPa35Wq9C" + }, + "source": [ + "---------------------------------------------------------" + ] + }, + { + "cell_type": "code", + "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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