7913 lines
288 KiB
Plaintext
7913 lines
288 KiB
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{
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"cells": [
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# T5 model classification training"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Links:\n",
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"- Tensorboard training: https://tensorboard.dev/experiment/CgaWd9pATZeuquRT7TZp7w/#scalars\n",
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"- Huggingface dataset edited: https://huggingface.co/datasets/Zombely/sst2-project-dataset\n",
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"- Huggingface Trained model: https://huggingface.co/Zombely/t5-model\n",
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"- Huggingface Tokenizer: https://huggingface.co/Zombely/t5-tokenizer"
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]
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},
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "moS_Sk1kPztc",
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"outputId": "de22adf0-80a7-47f8-9df4-b6ac3732ded6"
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},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Collecting datasets\n",
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"\u001b[?25hCollecting transformers\n",
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"\u001b[?25hCollecting sentencepiece\n",
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"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from datasets) (1.21.6)\n",
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"Collecting huggingface-hub<1.0.0,>=0.2.0\n",
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" Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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"\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
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" Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
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"Collecting urllib3<1.27,>=1.21.1\n",
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"Installing collected packages: tokenizers, sentencepiece, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets\n",
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" Attempting uninstall: urllib3\n",
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" Found existing installation: urllib3 1.24.3\n",
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" Uninstalling urllib3-1.24.3:\n",
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" Successfully uninstalled urllib3-1.24.3\n",
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"Successfully installed datasets-2.9.0 huggingface-hub-0.12.0 multiprocess-0.70.14 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0\n"
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]
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}
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],
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"source": [
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"!pip install datasets transformers sentencepiece"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "Itn0ce_3P-Cv"
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},
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"outputs": [],
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"source": [
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"from datasets import load_dataset\n",
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"import torch\n",
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"from transformers import T5ForConditionalGeneration, T5Tokenizer, TrainingArguments, Trainer\n",
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"from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
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"import tensorflow as tf\n",
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"from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset\n",
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"import random\n",
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"import time\n",
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"import numpy as np\n",
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"import datetime\n",
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"import sklearn\n",
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"from tqdm.notebook import tqdm\n",
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"import os"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load data and transform dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "GQmeuSdNQkB7"
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},
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"outputs": [],
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"source": [
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"def load_and_process_dataset():\n",
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" dataset = load_dataset(\"sst2\")\n",
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" dataset.remove_columns('idx')\n",
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" del dataset['test']\n",
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" dataset['test'] = dataset['validation']\n",
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" del dataset['validation']\n",
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" split_dataset = dataset['train'].train_test_split(test_size=1600)\n",
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" dataset['train'] = split_dataset['train']\n",
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" dataset['validation'] = split_dataset['test']\n",
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" return dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 329,
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"referenced_widgets": [
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"1f0d8bc4c93048baa73f3a1cf00eae63",
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"c026ef4af9c145b9a8315e60e01c8db3",
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"17ccc44770fc407f9e2619b4080eced1",
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"502b22b7634a4862b3f083fe5dc5efaf",
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"9e4d054b2f5742d287837daecdf3c1d3",
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"c8fd183dd6694c5788b50922a326e3fa",
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"6857e7133d024d5f9e0a54528a06f164",
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"e6a081c879df438da42c384e665e606d",
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"b36171be00c54e718cc546f2f8b28250",
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"8d8167938ae14287b214f177b0a3d5be",
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"dd9e3eb27fc643d9ad20c35491afa209"
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]
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},
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"id": "BWonEzhAQmnF",
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"outputId": "3bfe3731-974e-4a77-d7b1-5f3bfb806a4b"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:datasets.builder:Found cached dataset sst2 (/root/.cache/huggingface/datasets/sst2/default/2.0.0/9896208a8d85db057ac50c72282bcb8fe755accc671a57dd8059d4e130961ed5)\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1f0d8bc4c93048baa73f3a1cf00eae63",
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"version_major": 2,
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"version_minor": 0
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" 0%| | 0/3 [00:00<?, ?it/s]"
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"DatasetDict({\n",
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" train: Dataset({\n",
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" features: ['idx', 'sentence', 'label'],\n",
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" num_rows: 65749\n",
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" })\n",
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" test: Dataset({\n",
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" features: ['idx', 'sentence', 'label'],\n",
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" num_rows: 872\n",
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" })\n",
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" validation: Dataset({\n",
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" features: ['idx', 'sentence', 'label'],\n",
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" num_rows: 1600\n",
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" })\n",
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"})"
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]
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},
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"execution_count": 29,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dataset = load_and_process_dataset()\n",
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}
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],
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"source": [
|
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"for key in dataset.keys():\n",
|
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" dataset[key] = dataset[key].map(lambda x: {'source_text': f\"sentiment-analysis: {x['sentence']}\", 'target_text': 'positive' if x['label'] else 'negative'}).remove_columns(['idx', 'sentence', 'label'])"
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]
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{
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"execution_count": 6,
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"id": "wgEXht_UQpNp"
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},
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"outputs": [],
|
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"source": [
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"train = dataset['train']\n",
|
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"validation = dataset['validation']\n",
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"test = dataset['test']"
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]
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"execution_count": 7,
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"base_uri": "https://localhost:8080/"
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"id": "l3aTDCjDbfo3",
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},
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"outputs": [
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{
|
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"data": {
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"text/plain": [
|
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"{'source_text': 'sentiment-analysis: smart , sassy and exceptionally charming ',\n",
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" 'target_text': 'positive'}"
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]
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"output_type": "execute_result"
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"version_minor": 0
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{
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|
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|
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"text": [
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|||
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|
"/usr/local/lib/python3.8/dist-packages/transformers/models/t5/tokenization_t5.py:163: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
|
|||
|
|
"For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
|
|||
|
|
"- Be aware that you SHOULD NOT rely on t5-small automatically truncating your input to 512 when padding/encoding.\n",
|
|||
|
|
"- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
|
|||
|
|
"- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
|
|||
|
|
" warnings.warn(\n"
|
|||
|
|
]
|
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|
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"version_minor": 0
|
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|
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"text/plain": [
|
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"version_minor": 0
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|
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}
|
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],
|
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|
|
"source": [
|
|||
|
|
"tokenizer = T5Tokenizer.from_pretrained(\"t5-small\")\n",
|
|||
|
|
"model = T5ForConditionalGeneration.from_pretrained(\"t5-small\")"
|
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|
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]
|
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},
|
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{
|
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|
"cell_type": "code",
|
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"execution_count": 9,
|
|||
|
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"metadata": {
|
|||
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"id": "kNPSSfA-Ejn5"
|
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|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"model.cuda();"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
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{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
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"metadata": {
|
|||
|
|
"id": "2lbY87au4Ekf"
|
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|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def tokenize(dataset):\n",
|
|||
|
|
" input_ids = []\n",
|
|||
|
|
" target_ids = []\n",
|
|||
|
|
" attention_masks = []\n",
|
|||
|
|
" for row in dataset:\n",
|
|||
|
|
" encoding = tokenizer.encode_plus(\n",
|
|||
|
|
" row['source_text'],\n",
|
|||
|
|
" padding=\"max_length\",\n",
|
|||
|
|
" max_length=86,\n",
|
|||
|
|
" truncation=True,\n",
|
|||
|
|
" add_special_tokens = True,\n",
|
|||
|
|
" return_attention_mask = True,\n",
|
|||
|
|
" return_tensors=\"pt\"\n",
|
|||
|
|
" )\n",
|
|||
|
|
" target_encoding = tokenizer.encode_plus(\n",
|
|||
|
|
" row['target_text'],\n",
|
|||
|
|
" padding=\"max_length\",\n",
|
|||
|
|
" max_length=2,\n",
|
|||
|
|
" truncation=True,\n",
|
|||
|
|
" add_special_tokens = True,\n",
|
|||
|
|
" return_attention_mask = True,\n",
|
|||
|
|
" return_tensors=\"pt\",\n",
|
|||
|
|
" )\n",
|
|||
|
|
" input_ids.append(encoding['input_ids'])\n",
|
|||
|
|
" target_ids.append(target_encoding['input_ids'])\n",
|
|||
|
|
" attention_masks.append(encoding['attention_mask'])\n",
|
|||
|
|
" return TensorDataset(torch.cat(input_ids, dim=0), torch.cat(attention_masks, dim=0), torch.cat(target_ids, dim=0))\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "qKGhmdBM6o3G"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"train_tokenized = tokenize(train)\n",
|
|||
|
|
"validation_tokenized = tokenize(validation)\n",
|
|||
|
|
"test_tokenized = tokenize(test)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "wXkLoCQD_yIP"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"batch_size = 32\n",
|
|||
|
|
"\n",
|
|||
|
|
"train_dataloader = DataLoader(\n",
|
|||
|
|
" train_tokenized,\n",
|
|||
|
|
" sampler = RandomSampler(train_tokenized),\n",
|
|||
|
|
" batch_size = batch_size\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
"validation_dataloader = DataLoader(\n",
|
|||
|
|
" validation_tokenized,\n",
|
|||
|
|
" sampler = SequentialSampler(validation_tokenized),\n",
|
|||
|
|
" batch_size = batch_size\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
"test_dataloader = DataLoader(\n",
|
|||
|
|
" test_tokenized,\n",
|
|||
|
|
" sampler = SequentialSampler(test_tokenized),\n",
|
|||
|
|
" batch_size = batch_size\n",
|
|||
|
|
" )"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"attachments": {},
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Main model training class"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "kInmDIcg4IWF"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"class T5Model:\n",
|
|||
|
|
" def __init__(self, model, tokenizer, train_dataloader, val_dataloader, logs_dir_path, seed=42, epochs=3, lr=1e-4, eps=1e-8):\n",
|
|||
|
|
" self.model = model\n",
|
|||
|
|
" self.tokenizer = tokenizer\n",
|
|||
|
|
" self.train_dataloader = train_dataloader\n",
|
|||
|
|
" self.val_dataloader = val_dataloader\n",
|
|||
|
|
" self.seed=seed\n",
|
|||
|
|
" self.epochs = epochs\n",
|
|||
|
|
" self.learning_rate = lr\n",
|
|||
|
|
" self.eps = eps\n",
|
|||
|
|
" self.logs_dir_path = logs_dir_path\n",
|
|||
|
|
" self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
|||
|
|
" self.optimizer = torch.optim.AdamW(model.parameters(), lr = lr, eps = eps)\n",
|
|||
|
|
" self.tensorboard_writter()\n",
|
|||
|
|
" self.init_seed()\n",
|
|||
|
|
"\n",
|
|||
|
|
" def init_seed(self):\n",
|
|||
|
|
" random.seed(self.seed)\n",
|
|||
|
|
" np.random.seed(self.seed)\n",
|
|||
|
|
" torch.manual_seed(self.seed)\n",
|
|||
|
|
" torch.cuda.manual_seed_all(self.seed)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def tensorboard_writter(self):\n",
|
|||
|
|
" if not os.path.exists(self.logs_dir_path):\n",
|
|||
|
|
" os.mkdir(self.logs_dir_path)\n",
|
|||
|
|
" self.summary_writer = tf.summary.create_file_writer(self.logs_dir_path)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def compute_metrics(self, target, preds):\n",
|
|||
|
|
" return sklearn.metrics.accuracy_score(target, preds)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def _train(self, epoch_number):\n",
|
|||
|
|
" print(f\"Training epoch: {epoch_number}\")\n",
|
|||
|
|
" self.model.train()\n",
|
|||
|
|
" train_accuracy_total = 0\n",
|
|||
|
|
" pbar = tqdm(self.train_dataloader, total=len(self.train_dataloader))\n",
|
|||
|
|
" for step, batch in enumerate(pbar):\n",
|
|||
|
|
" b_input_ids = batch[0].to(self.device)\n",
|
|||
|
|
" b_input_mask = batch[1].to(self.device)\n",
|
|||
|
|
" b_labels = batch[2].to(self.device)\n",
|
|||
|
|
"\n",
|
|||
|
|
" outputs = self.model(\n",
|
|||
|
|
" input_ids=b_input_ids,\n",
|
|||
|
|
" attention_mask=b_input_mask,\n",
|
|||
|
|
" labels=b_labels\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" gen_output = self.model.generate(\n",
|
|||
|
|
" input_ids = b_input_ids,\n",
|
|||
|
|
" attention_mask = b_input_mask, \n",
|
|||
|
|
" max_length=3, \n",
|
|||
|
|
" num_beams=2,\n",
|
|||
|
|
" repetition_penalty=2.5, \n",
|
|||
|
|
" length_penalty=1.0, \n",
|
|||
|
|
" early_stopping=True\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" preds = [self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in gen_output]\n",
|
|||
|
|
" target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in b_labels]\n",
|
|||
|
|
"\n",
|
|||
|
|
" loss = outputs[0]\n",
|
|||
|
|
" self.optimizer.zero_grad()\n",
|
|||
|
|
" loss.backward()\n",
|
|||
|
|
" self.optimizer.step()\n",
|
|||
|
|
"\n",
|
|||
|
|
" accuracy = self.compute_metrics(target, preds)\n",
|
|||
|
|
" train_accuracy_total += accuracy\n",
|
|||
|
|
" pbar.set_description(f\"Epoch {epoch_number+1}, step {step}, train loss {loss}, accuracy: {accuracy}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" with self.summary_writer.as_default():\n",
|
|||
|
|
" tf.summary.scalar(name=f\"Epoch: {epoch_number+1}, step/train_accuracy\", data=accuracy ,step=step)\n",
|
|||
|
|
" tf.summary.scalar(name=f\"Epoch: {epoch_number+1}, step/train_loss\", data=loss.cpu().detach().numpy(), step=step)\n",
|
|||
|
|
" self.summary_writer.flush()\n",
|
|||
|
|
" print(f\"Epoch: {epoch_number+1}, Average train accuracy: {train_accuracy_total/len(self.train_dataloader)}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" def train(self):\n",
|
|||
|
|
" print(f\"Starting training, epochs: {self.epochs}\")\n",
|
|||
|
|
" for i in range(0, self.epochs):\n",
|
|||
|
|
" self._train(i)\n",
|
|||
|
|
" self.validate(i)\n",
|
|||
|
|
" \n",
|
|||
|
|
" def validate(self, epoch_number):\n",
|
|||
|
|
" print(f\"Validation epoch: {epoch_number}\")\n",
|
|||
|
|
" self.model.eval()\n",
|
|||
|
|
" val_accuracy_total = 0\n",
|
|||
|
|
" pbar_val = tqdm(self.val_dataloader, total=len(self.val_dataloader))\n",
|
|||
|
|
" for step, batch in enumerate(pbar_val):\n",
|
|||
|
|
" b_input_ids = batch[0].to(self.device)\n",
|
|||
|
|
" b_input_mask = batch[1].to(self.device)\n",
|
|||
|
|
" b_labels = batch[2].to(self.device)\n",
|
|||
|
|
"\n",
|
|||
|
|
" with torch.no_grad(): \n",
|
|||
|
|
"\n",
|
|||
|
|
" outputs = self.model(\n",
|
|||
|
|
" input_ids=b_input_ids,\n",
|
|||
|
|
" attention_mask=b_input_mask,\n",
|
|||
|
|
" labels=b_labels\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" loss = outputs[0]\n",
|
|||
|
|
"\n",
|
|||
|
|
" gen_output = self.model.generate(\n",
|
|||
|
|
" input_ids = b_input_ids,\n",
|
|||
|
|
" attention_mask = b_input_mask, \n",
|
|||
|
|
" max_length=3, \n",
|
|||
|
|
" num_beams=2,\n",
|
|||
|
|
" repetition_penalty=2.5, \n",
|
|||
|
|
" length_penalty=1.0, \n",
|
|||
|
|
" early_stopping=True\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" preds = [self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in gen_output]\n",
|
|||
|
|
" target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in b_labels]\n",
|
|||
|
|
"\n",
|
|||
|
|
" accuracy_val = self.compute_metrics(target, preds)\n",
|
|||
|
|
" val_accuracy_total += accuracy_val\n",
|
|||
|
|
" pbar_val.set_description(f\"Epoch {epoch_number+1}, val loss {loss}, accuracy: {accuracy_val}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" with self.summary_writer.as_default():\n",
|
|||
|
|
" tf.summary.scalar(name=f\"Epoch: {epoch_number+1}, step/val_accuracy\", data=accuracy_val ,step=step)\n",
|
|||
|
|
" tf.summary.scalar(name=f\"Epoch: {epoch_number+1}, step/val_loss\", data=loss.cpu().detach().numpy(), step=step)\n",
|
|||
|
|
" self.summary_writer.flush()\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(f\"Epoch: {epoch_number+1}, Average validation accuracy: {val_accuracy_total/len(self.val_dataloader)}\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" def evaluate(self, test_dataloader):\n",
|
|||
|
|
" self.model.eval()\n",
|
|||
|
|
" total_test_acc = 0\n",
|
|||
|
|
" for batch in tqdm(test_dataloader, total=len(test_dataloader)):\n",
|
|||
|
|
" b_input_ids = batch[0].to(self.device)\n",
|
|||
|
|
" b_input_mask = batch[1].to(self.device)\n",
|
|||
|
|
" b_labels = batch[2].to(self.device)\n",
|
|||
|
|
" \n",
|
|||
|
|
" with torch.no_grad(): \n",
|
|||
|
|
"\n",
|
|||
|
|
" gen_output = self.model.generate(\n",
|
|||
|
|
" input_ids = b_input_ids,\n",
|
|||
|
|
" attention_mask = b_input_mask, \n",
|
|||
|
|
" max_length=3, \n",
|
|||
|
|
" num_beams=2,\n",
|
|||
|
|
" repetition_penalty=2.5, \n",
|
|||
|
|
" length_penalty=1.0, \n",
|
|||
|
|
" early_stopping=True\n",
|
|||
|
|
" )\n",
|
|||
|
|
" \n",
|
|||
|
|
" preds = [self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in gen_output]\n",
|
|||
|
|
" target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in b_labels]\n",
|
|||
|
|
" total_test_acc += self.compute_metrics(preds, target) \n",
|
|||
|
|
" print(\"Accuracy: \",total_test_acc / len(test_dataloader))\n",
|
|||
|
|
" "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 519,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"f836ca46b8d64d718b4e3e39191cd91d",
|
|||
|
|
"31480d5282db474eb8377bb0a55d65e4",
|
|||
|
|
"23d20e43b3cf41cf879369a936ea6a45",
|
|||
|
|
"9cb18f242db04c5398a70ee49e784ae2",
|
|||
|
|
"900b4933de1d452c97a1ff422b663616",
|
|||
|
|
"7081fcb53f434874b026c1a184445cf7",
|
|||
|
|
"86de0111f730426cb48559604782b1ef",
|
|||
|
|
"d3efad5cbfa141e18293f9da59c7fc6e",
|
|||
|
|
"9c97c01642df4605af75fb41010bd853",
|
|||
|
|
"4635957c434a4b6c83cb1df64c2b6504",
|
|||
|
|
"65ab78a23c504af89298ecad7121115e",
|
|||
|
|
"c5602472d8f141d390479498071ee0e5",
|
|||
|
|
"ba920ec014754ad7aa7966ffeda05a07",
|
|||
|
|
"7cf526de9b794b00b62770941c88c7df",
|
|||
|
|
"cedfdc33ad4049a1a792262d58bf56e9",
|
|||
|
|
"76df2eb16da840d692cbc4c68c3327be",
|
|||
|
|
"029f7b5ac8f4495ca6ba01ccfd0239ca",
|
|||
|
|
"eb01e9d5c3c04e538cbdc09cfeda9469",
|
|||
|
|
"3fc12b9695774d3280edf9f4f7f3af5d",
|
|||
|
|
"f6554259b9f7478aa679206284f4da45",
|
|||
|
|
"af0c141fff434ab9ae3d9122a53c87e1",
|
|||
|
|
"1e5b6c9c99944acd8e7b5d6d30b9b395",
|
|||
|
|
"225d2a25d5874717820b1211d192c920",
|
|||
|
|
"581349ca45604485a42bbcd118ea8b7c",
|
|||
|
|
"30c066b2abcd412493b051552aba019f",
|
|||
|
|
"e229b3597cfa4eb1891d76ac7c5d48d6",
|
|||
|
|
"fa20291a20154d0396385f70335fee34",
|
|||
|
|
"e7654197e2a0455d941df67c26debe00",
|
|||
|
|
"2bde0a51664744e28f74dc54d75ac6c7",
|
|||
|
|
"b667ab4d68ce4e17b099171eed21c78d",
|
|||
|
|
"4d446a5d636c454f8736ecf4b75f08d5",
|
|||
|
|
"3d092417dc6e4004a582de62fb56da8d",
|
|||
|
|
"d30bbb55b5d34c86bf9a3dd6d6b30917",
|
|||
|
|
"09f57655e6834560bd6d4262ac1e07c3",
|
|||
|
|
"3fbe90c4bcf44fa286fb77e4efb760f4",
|
|||
|
|
"992c018e2a4b43a4b235aa16d907bfe7",
|
|||
|
|
"a6a4aed066aa4bd0aa7009b054689e19",
|
|||
|
|
"ae8d2e5d508a41829d69935a994046af",
|
|||
|
|
"612594d6d5ee423981e8a41902e522ba",
|
|||
|
|
"5087855d51d84df6974415ee200573ea",
|
|||
|
|
"8a11c3ca3099415aa35b831cc0e55a75",
|
|||
|
|
"a5eaa5e9a9be44a8836ea652cda0cb6e",
|
|||
|
|
"ab19d785b03f45639d62cf492684222d",
|
|||
|
|
"987f1a738cc04ccd850e0291b3212a24",
|
|||
|
|
"3cacafbfc6974fb88cebd4fd18c7ea76",
|
|||
|
|
"90d6e52c1cce4c0bb40cb318def6fdda",
|
|||
|
|
"8f2a9e141c454268ac1a5194fd223ef8",
|
|||
|
|
"d689204e330c44de9990cc58d3242618",
|
|||
|
|
"546731897ee8488b8a5825cb81d0e52a",
|
|||
|
|
"b30772d5d36d4bb3ac8082017a1acbeb",
|
|||
|
|
"028168d8cb2e46609c7063334b3f24e7",
|
|||
|
|
"f58e080bbea44251abb2ed52ee5c5c9c",
|
|||
|
|
"3e4be046323249b994d857f3f5ab78d8",
|
|||
|
|
"d48435bace1245c1b346966461402c45",
|
|||
|
|
"5aee2684f47b4c048ff641dfb1b56ac1",
|
|||
|
|
"fbe579ef91614cdc856adc4b95f1a2af",
|
|||
|
|
"63cfe60559df43cb89ce7f79a05c4659",
|
|||
|
|
"d20c258cc5314ecf87c39ee9e7e16227",
|
|||
|
|
"bbeae4afbced426b97486430acf7ef8e",
|
|||
|
|
"4eb70b766d2c442fbe889d6c264ec415",
|
|||
|
|
"0006f7eafe4441caa8941a0574506128",
|
|||
|
|
"511d63f9236e48499a38e4d0b4d28d88",
|
|||
|
|
"998e47e8d2f6449ab57905d5960f5d87",
|
|||
|
|
"4df5a65bb4ee48d1b48d5506b0d7ae94",
|
|||
|
|
"2f5706cb62ee4e94891fd71a0d3e4ccc",
|
|||
|
|
"a980069f31984ea29678d5eae419793c"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "Xo1vuaaw7JA7",
|
|||
|
|
"outputId": "5e3e83f7-2c46-47cf-bc4f-af8a697dab6b"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Starting training, epochs: 3\n",
|
|||
|
|
"Training epoch: 0\n"
|
|||
|
|
]
|
|||
|
|
},
|
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{
|
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|
"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "f836ca46b8d64d718b4e3e39191cd91d",
|
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"version_major": 2,
|
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"version_minor": 0
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|
"output_type": "display_data"
|
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},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Epoch: 1, Average train accuracy: 0.8825947167188043\n",
|
|||
|
|
"Validation epoch: 0\n"
|
|||
|
|
]
|
|||
|
|
},
|
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|
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|
"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "c5602472d8f141d390479498071ee0e5",
|
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|
"version_major": 2,
|
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|
|
"version_minor": 0
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|
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|
|
"output_type": "display_data"
|
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|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Epoch: 1, Average validation accuracy: 0.926875\n",
|
|||
|
|
"Training epoch: 1\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
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|
"model_id": "225d2a25d5874717820b1211d192c920",
|
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|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
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|
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|
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|
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|
|
"output_type": "display_data"
|
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|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Epoch: 2, Average train accuracy: 0.9252577916811494\n",
|
|||
|
|
"Validation epoch: 1\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
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|
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|
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|
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|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
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|
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|
|
"output_type": "display_data"
|
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|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Epoch: 2, Average validation accuracy: 0.936875\n",
|
|||
|
|
"Training epoch: 2\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
|
"application/vnd.jupyter.widget-view+json": {
|
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|
|
"model_id": "3cacafbfc6974fb88cebd4fd18c7ea76",
|
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|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
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|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Epoch: 3, Average train accuracy: 0.9391720252577916\n",
|
|||
|
|
"Validation epoch: 2\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "fbe579ef91614cdc856adc4b95f1a2af",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
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|
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|
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|
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|
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|
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|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Epoch: 3, Average validation accuracy: 0.93875\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"t5model = T5Model(model, tokenizer, train_dataloader, validation_dataloader, \"./logs\")\n",
|
|||
|
|
"t5model.train()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 66,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"2c1910a4fc30451f8112f8ef9139ec96",
|
|||
|
|
"f9b30a6541e64808afaad39bc34c7545",
|
|||
|
|
"ce570046c07d4f9f97a715dc32a0cfdf",
|
|||
|
|
"b5c8f57e112249e0b5186c4ab2452d17",
|
|||
|
|
"ba8964c541e84a3fa562d0083ef5832b",
|
|||
|
|
"ece14b9b056445259cfb1bd8b8edf46a",
|
|||
|
|
"161ce1c991754ac0b2c7a20abb2ac653",
|
|||
|
|
"d272e67085d84532a1cfcec2f35199c5",
|
|||
|
|
"092b2a86efe947b39f5076c99938c8d0",
|
|||
|
|
"42441c4de5dd4fe8ad90be8e549addce",
|
|||
|
|
"990fb606bc40427a82320d79d9459a00"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "pmmQgj1lUjNN",
|
|||
|
|
"outputId": "ee815579-3725-4b02-ffa0-abed2acb1788"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "2c1910a4fc30451f8112f8ef9139ec96",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
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|
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|
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|
|||
|
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|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Accuracy: 0.9129464285714286\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"t5model.evaluate(test_dataloader)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "3JIPplO9NELv",
|
|||
|
|
"outputId": "95d2aa60-5b6d-4102-d3a1-522a50f115df"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"2023-02-13 11:49:46.367397: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
|
|||
|
|
"2023-02-13 11:49:46.367509: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
|
|||
|
|
"2023-02-13 11:49:46.367529: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
|
|||
|
|
"\n",
|
|||
|
|
"***** TensorBoard Uploader *****\n",
|
|||
|
|
"\n",
|
|||
|
|
"This will upload your TensorBoard logs to https://tensorboard.dev/ from\n",
|
|||
|
|
"the following directory:\n",
|
|||
|
|
"\n",
|
|||
|
|
"logs\n",
|
|||
|
|
"\n",
|
|||
|
|
"This TensorBoard will be visible to everyone. Do not upload sensitive\n",
|
|||
|
|
"data.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Your use of this service is subject to Google's Terms of Service\n",
|
|||
|
|
"<https://policies.google.com/terms> and Privacy Policy\n",
|
|||
|
|
"<https://policies.google.com/privacy>, and TensorBoard.dev's Terms of Service\n",
|
|||
|
|
"<https://tensorboard.dev/policy/terms/>.\n",
|
|||
|
|
"\n",
|
|||
|
|
"This notice will not be shown again while you are logged into the uploader.\n",
|
|||
|
|
"To log out, run `tensorboard dev auth revoke`.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Continue? (yes/NO) yes\n",
|
|||
|
|
"\n",
|
|||
|
|
"To sign in with the TensorBoard uploader:\n",
|
|||
|
|
"\n",
|
|||
|
|
"1. On your computer or phone, visit:\n",
|
|||
|
|
"\n",
|
|||
|
|
" https://www.google.com/device\n",
|
|||
|
|
"\n",
|
|||
|
|
"2. Sign in with your Google account, then enter:\n",
|
|||
|
|
"\n",
|
|||
|
|
" ZBRH-SMMW\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"Upload started and will continue reading any new data as it's added to the logdir.\n",
|
|||
|
|
"\n",
|
|||
|
|
"To stop uploading, press Ctrl-C.\n",
|
|||
|
|
"\n",
|
|||
|
|
"New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/CgaWd9pATZeuquRT7TZp7w/\n",
|
|||
|
|
"\n",
|
|||
|
|
"\u001b[1m[2023-02-13T11:50:01]\u001b[0m Started scanning logdir.\n",
|
|||
|
|
"\u001b[1m[2023-02-13T11:50:04]\u001b[0m Total uploaded: 12630 scalars, 0 tensors, 0 binary objects\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"Interrupted. View your TensorBoard at https://tensorboard.dev/experiment/CgaWd9pATZeuquRT7TZp7w/\n",
|
|||
|
|
"Traceback (most recent call last):\n",
|
|||
|
|
" File \"/usr/local/bin/tensorboard\", line 8, in <module>\n",
|
|||
|
|
" sys.exit(run_main())\n",
|
|||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/main.py\", line 46, in run_main\n",
|
|||
|
|
" app.run(tensorboard.main, flags_parser=tensorboard.configure)\n",
|
|||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/absl/app.py\", line 308, in run\n",
|
|||
|
|
" _run_main(main, args)\n",
|
|||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/absl/app.py\", line 254, in _run_main\n",
|
|||
|
|
" sys.exit(main(argv))\n",
|
|||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/program.py\", line 276, in main\n",
|
|||
|
|
" return runner(self.flags) or 0\n",
|
|||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py\", line 691, in run\n",
|
|||
|
|
" return _run(flags, self._experiment_url_callback)\n",
|
|||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py\", line 124, in _run\n",
|
|||
|
|
" intent.execute(server_info, channel)\n",
|
|||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/grpc/_channel.py\", line 1564, in __exit__\n",
|
|||
|
|
" self._close()\n",
|
|||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/grpc/_channel.py\", line 1550, in _close\n",
|
|||
|
|
" self._channel.close(cygrpc.StatusCode.cancelled, 'Channel closed!')\n",
|
|||
|
|
" File \"src/python/grpcio/grpc/_cython/_cygrpc/channel.pyx.pxi\", line 513, in grpc._cython.cygrpc.Channel.close\n",
|
|||
|
|
" File \"src/python/grpcio/grpc/_cython/_cygrpc/channel.pyx.pxi\", line 399, in grpc._cython.cygrpc._close\n",
|
|||
|
|
" File \"src/python/grpcio/grpc/_cython/_cygrpc/channel.pyx.pxi\", line 429, in grpc._cython.cygrpc._close\n",
|
|||
|
|
" File \"/usr/lib/python3.8/threading.py\", line 364, in notify_all\n",
|
|||
|
|
" def notify_all(self):\n",
|
|||
|
|
"KeyboardInterrupt\n",
|
|||
|
|
"^C\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"!tensorboard dev upload --logdir logs --name t5-sst2"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "CHSu41ZvnaBB",
|
|||
|
|
"outputId": "2948e2b2-54a1-43aa-9d29-27a7b40538ed"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"('./model/tokenizer_config.json',\n",
|
|||
|
|
" './model/special_tokens_map.json',\n",
|
|||
|
|
" './model/spiece.model',\n",
|
|||
|
|
" './model/added_tokens.json')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"t5model.model.save_pretrained(\"./model\")\n",
|
|||
|
|
"t5model.tokenizer.save_pretrained(\"./model\")\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "Xja8cK6yoHcM",
|
|||
|
|
"outputId": "df987295-d175-4d17-928e-d9b03207169c"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
" adding: model/ (stored 0%)\n",
|
|||
|
|
" adding: model/tokenizer_config.json (deflated 82%)\n",
|
|||
|
|
" adding: model/config.json (deflated 62%)\n",
|
|||
|
|
" adding: model/generation_config.json (deflated 29%)\n",
|
|||
|
|
" adding: model/pytorch_model.bin (deflated 8%)\n",
|
|||
|
|
" adding: model/special_tokens_map.json (deflated 86%)\n",
|
|||
|
|
" adding: model/spiece.model (deflated 48%)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"!zip -r /content/model model"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "16jB6CpRoslW",
|
|||
|
|
"outputId": "15561efe-747b-4f21-aefc-84fb913c3037"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
" _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
|
|||
|
|
" _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
|
|||
|
|
" _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
|
|||
|
|
" _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
|
|||
|
|
" _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
|
|||
|
|
" \n",
|
|||
|
|
" To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
|
|||
|
|
"Token: \n",
|
|||
|
|
"Add token as git credential? (Y/n) y\n",
|
|||
|
|
"Token is valid.\n",
|
|||
|
|
"\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
|
|||
|
|
"You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
|
|||
|
|
"Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
|
|||
|
|
"\n",
|
|||
|
|
"git config --global credential.helper store\n",
|
|||
|
|
"\n",
|
|||
|
|
"Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
|
|||
|
|
"Token has not been saved to git credential helper.\n",
|
|||
|
|
"Your token has been saved to /root/.cache/huggingface/token\n",
|
|||
|
|
"Login successful\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"!huggingface-cli login"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 133,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"7e8cfa36fed049e2a7b7d8666bd57cdf",
|
|||
|
|
"3c3d6d206d4844b199743baffb14d6c8",
|
|||
|
|
"7e4c949d6ed246359c2e683f7697886d",
|
|||
|
|
"52a2d3aa6abc4ac0a74ec9a2fa400f2c",
|
|||
|
|
"2b964660bb9f41f599c6282547e23bc2",
|
|||
|
|
"a0a534ea53ba41c2bab6ca1e99fee542",
|
|||
|
|
"5170299b7a664c34b5aad7ea5d0f85c0",
|
|||
|
|
"27cd138062d94582b0323d477c2df364",
|
|||
|
|
"9d1f7fa6d41e46ba8a3a4d330d73d05b",
|
|||
|
|
"992bd52b3e474ce297dfa09fb0007a01",
|
|||
|
|
"6a17c291ee0547f2a31e430db846397c",
|
|||
|
|
"5a2c81a908b94bb8b2a4422effbce25c",
|
|||
|
|
"c22999770c184330a75f5efa68e61cab",
|
|||
|
|
"2f9ccc2d649f4b45a30f3851beff2629",
|
|||
|
|
"b6f2f601496c4018ba14e5615e1e300f",
|
|||
|
|
"1e9fb6e057014cc78f64006ef96d9cb1",
|
|||
|
|
"0f244fbff6de4511883e953c494f0a22",
|
|||
|
|
"5266c0225cc246eab466d882a47e60dd",
|
|||
|
|
"fda5959a3987462b8e75ec01c0eb2fda",
|
|||
|
|
"256c1b04cfd94ec984e6a844c60e881c",
|
|||
|
|
"500de4e8a3c5444aa8395a6bb3fa90f3",
|
|||
|
|
"25b3a226518f44f983cd12cdbde7e434"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "DptjUZgypVN9",
|
|||
|
|
"outputId": "339f2bc3-0ccc-4e72-9bb9-f90d056a6044"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "7e8cfa36fed049e2a7b7d8666bd57cdf",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "5a2c81a908b94bb8b2a4422effbce25c",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"pytorch_model.bin: 0%| | 0.00/242M [00:00<?, ?B/s]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"CommitInfo(commit_url='https://huggingface.co/Zombely/t5-model/commit/caab3e8d5523bc7309774f13f522fd6f06666b29', commit_message='Upload T5ForConditionalGeneration', commit_description='', oid='caab3e8d5523bc7309774f13f522fd6f06666b29', pr_url=None, pr_revision=None, pr_num=None)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"t5model.model.push_to_hub(\"Zombely/t5-model\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 133,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"d90b46344c204bb7af2bedaf880dfd19",
|
|||
|
|
"af25e324536c4d8b8f30985361796bda",
|
|||
|
|
"277a6f93cab44dc791a55ce0f0c06e55",
|
|||
|
|
"1f2ad89c131d47d7a942c7502131932a",
|
|||
|
|
"346977e99476484daf34a2fa0d3c6a24",
|
|||
|
|
"f23a46e5ba694cd5a9b36ba5dfb00d50",
|
|||
|
|
"b09ef5185e74451eb5b3213175fabb4b",
|
|||
|
|
"292b9e48c69d4d1d9591b93206cde6e7",
|
|||
|
|
"e696f4c920f74047b8265742390b3d24",
|
|||
|
|
"6654a814f3454658a35b5e2fc8263b34",
|
|||
|
|
"a7a9a14d84c947d29884b8a5b0a1e463",
|
|||
|
|
"9ac2a58b6d8e4291b11a46ac79b00c46",
|
|||
|
|
"8929a49925ca4472a5d45ea99060cbd9",
|
|||
|
|
"8cf283841f444b05bc645a5d0d67271d",
|
|||
|
|
"8e29f7cbc79d46378f6df94e81846ee2",
|
|||
|
|
"fe4ae5822dfc410f8d796f95d57fb179",
|
|||
|
|
"684dc6451e0e4cb0911869209b5aed60",
|
|||
|
|
"d5f5f65f1fa64cb2a8fa3bf7a61b6933",
|
|||
|
|
"3e9c76a851ad4b7cab1f804479955974",
|
|||
|
|
"cae8b82cd81547448c0a25333357e1b8",
|
|||
|
|
"99f13de48542440093e936bd1c34bd15",
|
|||
|
|
"f8d311c8feee4a14a8cdfbc7e0409dfb"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "OyhjXaK2pd3S",
|
|||
|
|
"outputId": "4aaa2128-d6c9-4c96-e38c-018b4a42dcda"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "d90b46344c204bb7af2bedaf880dfd19",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"spiece.model: 0%| | 0.00/792k [00:00<?, ?B/s]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "9ac2a58b6d8e4291b11a46ac79b00c46",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"CommitInfo(commit_url='https://huggingface.co/Zombely/t5-tokenizer/commit/640998bd3d496cbd5266140fbeff873d07c0158e', commit_message='Upload tokenizer', commit_description='', oid='640998bd3d496cbd5266140fbeff873d07c0158e', pr_url=None, pr_revision=None, pr_num=None)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 34,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"t5model.tokenizer.push_to_hub(\"Zombely/t5-tokenizer\")\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"accelerator": "GPU",
|
|||
|
|
"colab": {
|
|||
|
|
"machine_shape": "hm",
|
|||
|
|
"provenance": []
|
|||
|
|
},
|
|||
|
|
"gpuClass": "standard",
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"name": "python",
|
|||
|
|
"version": "3.9.0"
|
|||
|
|
},
|
|||
|
|
"vscode": {
|
|||
|
|
"interpreter": {
|
|||
|
|
"hash": "81794d4967e6c3204c66dcd87b604927b115b27c00565d3d43f05ba2f3a2cb0d"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"widgets": {
|
|||
|
|
"application/vnd.jupyter.widget-state+json": {
|
|||
|
|
"0006f7eafe4441caa8941a0574506128": {
|
|||
|
|
"model_module": "@jupyter-widgets/base",
|
|||
|
|
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|
|||
|
|
"model_name": "LayoutModel",
|
|||
|
|
"state": {
|
|||
|
|
"_model_module": "@jupyter-widgets/base",
|
|||
|
|
"_model_module_version": "1.2.0",
|
|||
|
|
"_model_name": "LayoutModel",
|
|||
|
|
"_view_count": null,
|
|||
|
|
"_view_module": "@jupyter-widgets/base",
|
|||
|
|
"_view_module_version": "1.2.0",
|
|||
|
|
"_view_name": "LayoutView",
|
|||
|
|
"align_content": null,
|
|||
|
|
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|
|||
|
|
"align_self": null,
|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
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|
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|
|||
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|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
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|
|||
|
|
"grid_template_rows": null,
|
|||
|
|
"height": null,
|
|||
|
|
"justify_content": null,
|
|||
|
|
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|
|||
|
|
"left": null,
|
|||
|
|
"margin": null,
|
|||
|
|
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|
|||
|
|
"max_width": null,
|
|||
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"height": null,
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"left": null,
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"margin": null,
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"max_height": null,
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"order": null,
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"overflow": null,
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"padding": null,
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"right": null,
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"top": null,
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"visibility": null,
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"width": null
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}
|
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}
|
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}
|
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}
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},
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"nbformat_minor": 0
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}
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