diff --git "a/milestone3/.ipynb_checkpoints/finetune-checkpoint.ipynb" "b/milestone3/.ipynb_checkpoints/finetune-checkpoint.ipynb"
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+{
+ "cells": [
+ {
+ "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 \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m84.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
+ "Collecting huggingface-hub<1.0,>=0.11.0\n",
+ " Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m24.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (2023.4.0)\n",
+ "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
+ "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
+ "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
+ "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 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')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WtsAFyrzWuCr"
+ },
+ "outputs": [],
+ "source": [
+ "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",
+ "\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",
+ "\n",
+ "train_texts, val_texts, train_labels, val_labels = train_test_split(small_train_texts, small_train_labels, test_size=.2)\n",
+ "# train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 145,
+ "referenced_widgets": [
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+ ]
+ },
+ "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, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5d869754c9e94f999c55af68012caa22",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/570 [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "0ec8ea8c9b02489295985bd6767cda5b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading (…)solve/main/vocab.txt: 0%| | 0.00/232k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "09fee717abef4084bc9b1e1c765527a4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading (…)/main/tokenizer.json: 0%| | 0.00/466k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
+ "#Set up the dataset\n",
+ "# train_encodings = tokenizer(train_texts, truncation=True, padding=True)\n",
+ "# val_encodings = tokenizer(val_texts, truncation=True, padding=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "aysAKCYoXBoz"
+ },
+ "outputs": [],
+ "source": [
+ "class TextDataset(Dataset):\n",
+ " def __init__(self,texts,labels):\n",
+ " self.texts = texts\n",
+ " self.labels = labels\n",
+ "\n",
+ " def __getitem__(self,idx):\n",
+ " encodings = tokenizer(self.texts[idx], truncation=True, padding=\"max_length\")\n",
+ " item = {key: torch.tensor(val) for key, val in encodings.items()}\n",
+ " item['labels'] = torch.tensor(self.labels[idx],dtype=torch.float32)\n",
+ " del encodings\n",
+ " return item\n",
+ "\n",
+ " def __len__(self):\n",
+ " return len(self.labels)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 113,
+ "referenced_widgets": [
+ "1cfd2e5c43634f95a7c683d77e702e4e",
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+ ]
+ },
+ "id": "BcZnYYII3Nxo",
+ "outputId": "4096dccf-8d0c-4892-9435-86c4be09cb75"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1cfd2e5c43634f95a7c683d77e702e4e",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "VBox(children=(HTML(value='
\n",
+ " \n",
+ " \n",
+ " [6250/6250 2:51:28, Epoch 5/5]\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Epoch | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 | \n",
+ " 0.052500 | \n",
+ " 0.048165 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 0.037000 | \n",
+ " 0.044507 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 0.027500 | \n",
+ " 0.048948 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 0.018800 | \n",
+ " 0.049080 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 0.014600 | \n",
+ " 0.050677 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ],
+ "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": [
+ "train_dataset = TextDataset(train_texts,train_labels)\n",
+ "val_dataset = TextDataset(val_texts, val_labels)\n",
+ "# small_train_dataset = train_dataset.shuffle(seed=42).select(range(1000))\n",
+ "# small_val_dataset = val_dataset.shuffle(seed=42).select(range(1000))\n",
+ "\n",
+ "\n",
+ "\n",
+ "# model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=6, problem_type=\"multi_label_classification\")\n",
+ "\n",
+ "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": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GH702kPdbbjs"
+ },
+ "outputs": [],
+ "source": [
+ "# print(device)"
+ ]
+ },
+ {
+ "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": {
+ "id": "iIHPfQZfhQpN"
+ },
+ "outputs": [],
+ "source": [
+ "# tokenizer.push_to_hub(\"andyqin18/test-finetuned\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
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+ "nbformat_minor": 1
+}