wehub-resource-sync
1780 lines
74 KiB
Plaintext
1780 lines
74 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3c5d72f4",
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"metadata": {},
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"source": [
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"# Finetuning a DistilBERT Classifier in Lightning"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18a56df9-d19c-4dfa-afab-097e4a57f5a1",
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"metadata": {},
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"source": [
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"Here, we are finetuning the output layers of a pretrained transformer:\n",
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"\n",
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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": 1,
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"id": "6fd9cda8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install transformers"
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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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"id": "92ea5612",
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install datasets"
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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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"id": "fe7191cf-62ed-4793-8358-bee70b233d05",
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install lightning"
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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": 4,
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"id": "033b75c5",
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"metadata": {},
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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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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
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" if not hasattr(tensorboard, \"__version__\") or LooseVersion(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch : 1.12.1\n",
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"transformers: 4.23.1\n",
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"datasets : 2.6.1\n",
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"lightning : 2022.10.20\n",
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"\n",
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"conda environment: dl-fundamentals\n",
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"\n"
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]
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}
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark --conda -p torch,transformers,datasets,lightning"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4cfd724d",
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"metadata": {},
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"source": [
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"# 1 Loading the Dataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fd06d930",
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"metadata": {},
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"source": [
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"The IMDB movie review dataset consists of 50k movie reviews with sentiment label (0: negative, 1: positive)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "60fe0b76",
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"metadata": {},
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"source": [
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"## 1a) Load from `datasets` Hub"
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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": 5,
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"id": "447e24bb",
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"metadata": {},
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"outputs": [],
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"source": [
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"from datasets import list_datasets, load_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": 6,
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"id": "2baf2f16",
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"metadata": {},
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"outputs": [],
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"source": [
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"# list_datasets()"
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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": 7,
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"id": "6310d5bf",
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"metadata": {},
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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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"Found cached dataset imdb (/home/raschka/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1)\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": "70c772b75480491d8590da7e1e6cab0a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/3 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"DatasetDict({\n",
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" train: Dataset({\n",
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" features: ['text', 'label'],\n",
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" num_rows: 25000\n",
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" })\n",
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" test: Dataset({\n",
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" features: ['text', 'label'],\n",
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" num_rows: 25000\n",
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" })\n",
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" unsupervised: Dataset({\n",
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" features: ['text', 'label'],\n",
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" num_rows: 50000\n",
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" })\n",
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"})\n"
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]
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}
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],
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"source": [
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"imdb_data = load_dataset(\"imdb\")\n",
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"print(imdb_data)"
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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": 8,
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"id": "552bbb2e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'text': \"This film is terrible. You don't really need to read this review further. If you are planning on watching it, suffice to say - don't (unless you are studying how not to make a good movie).<br /><br />The acting is horrendous... serious amateur hour. Throughout the movie I thought that it was interesting that they found someone who speaks and looks like Michael Madsen, only to find out that it is actually him! A new low even for him!!<br /><br />The plot is terrible. People who claim that it is original or good have probably never seen a decent movie before. Even by the standard of Hollywood action flicks, this is a terrible movie.<br /><br />Don't watch it!!! Go for a jog instead - at least you won't feel like killing yourself.\",\n",
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" 'label': 0}"
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]
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},
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"execution_count": 8,
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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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"imdb_data[\"train\"][99]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "40bdb9c5",
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"metadata": {},
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"source": [
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"## 1b) Load from local directory"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9103ec2d",
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"metadata": {},
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"source": [
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"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\n",
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"\n",
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"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\n",
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"\n",
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" tar -zxf aclImdb_v1.tar.gz\n",
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"\n",
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"B) If you are working with Windows, download an archiver such as 7Zip to extract the files from the download archive."
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac508bb8",
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"metadata": {},
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"source": [
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"C) Use the following code to download and unzip the dataset via Python"
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]
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},
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{
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"cell_type": "markdown",
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"id": "241ecc96",
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"metadata": {},
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"source": [
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"**Download the movie reviews**"
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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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"id": "02aeade4",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"import tarfile\n",
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"import time\n",
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"import urllib.request\n",
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"\n",
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"source = \"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\"\n",
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"target = \"aclImdb_v1.tar.gz\"\n",
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"\n",
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"if os.path.exists(target):\n",
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" os.remove(target)\n",
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"\n",
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"\n",
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"def reporthook(count, block_size, total_size):\n",
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" global start_time\n",
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" if count == 0:\n",
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" start_time = time.time()\n",
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" return\n",
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" duration = time.time() - start_time\n",
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" progress_size = int(count * block_size)\n",
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" speed = progress_size / (1024.0**2 * duration)\n",
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" percent = count * block_size * 100.0 / total_size\n",
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"\n",
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" sys.stdout.write(\n",
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" f\"\\r{int(percent)}% | {progress_size / (1024.**2):.2f} MB \"\n",
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" f\"| {speed:.2f} MB/s | {duration:.2f} sec elapsed\"\n",
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" )\n",
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" sys.stdout.flush()\n",
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"\n",
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"\n",
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"if not os.path.isdir(\"aclImdb\") and not os.path.isfile(\"aclImdb_v1.tar.gz\"):\n",
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" urllib.request.urlretrieve(source, target, reporthook)"
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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": 10,
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"id": "2a867dcc",
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"metadata": {},
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"outputs": [],
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"source": [
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"if not os.path.isdir(\"aclImdb\"):\n",
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"\n",
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" with tarfile.open(target, \"r:gz\") as tar:\n",
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" tar.extractall()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9318d4d0",
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"metadata": {},
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"source": [
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"**Convert them to a pandas DataFrame and save them as CSV**"
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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": 11,
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"id": "464e587c",
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"metadata": {},
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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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"100%|███████████████████████████████████████████████████████| 50000/50000 [00:54<00:00, 914.11it/s]\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import sys\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from packaging import version\n",
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"from tqdm import tqdm\n",
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"\n",
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"# change the `basepath` to the directory of the\n",
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"# unzipped movie dataset\n",
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"\n",
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"basepath = \"aclImdb\"\n",
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"\n",
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"labels = {\"pos\": 1, \"neg\": 0}\n",
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"\n",
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"df = pd.DataFrame()\n",
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"\n",
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"with tqdm(total=50000) as pbar:\n",
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" for s in (\"test\", \"train\"):\n",
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" for l in (\"pos\", \"neg\"):\n",
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" path = os.path.join(basepath, s, l)\n",
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" for file in sorted(os.listdir(path)):\n",
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" with open(os.path.join(path, file), \"r\", encoding=\"utf-8\") as infile:\n",
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" txt = infile.read()\n",
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"\n",
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" if version.parse(pd.__version__) >= version.parse(\"1.3.2\"):\n",
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" x = pd.DataFrame(\n",
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" [[txt, labels[l]]], columns=[\"review\", \"sentiment\"]\n",
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" )\n",
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" df = pd.concat([df, x], ignore_index=False)\n",
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"\n",
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" else:\n",
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" df = df.append([[txt, labels[l]]], ignore_index=True)\n",
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" pbar.update()\n",
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"df.columns = [\"text\", \"label\"]"
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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": 12,
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"id": "02649593",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"np.random.seed(0)\n",
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"df = df.reindex(np.random.permutation(df.index))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59ca0386",
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"metadata": {},
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"source": [
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"**Basic datasets analysis and sanity checks**"
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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": 13,
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"id": "c2db547a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Class distribution:\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array([25000, 25000])"
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]
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},
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"execution_count": 13,
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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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"print(\"Class distribution:\")\n",
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"np.bincount(df[\"label\"].values)"
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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": 14,
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"id": "a007e612",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(4, 173.0, 2470)"
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]
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},
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"execution_count": 14,
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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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"text_len = df[\"text\"].apply(lambda x: len(x.split()))\n",
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"text_len.min(), text_len.median(), text_len.max() "
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]
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},
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{
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"cell_type": "markdown",
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"id": "00f4b04d",
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"metadata": {},
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"source": [
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"**Split data into training, validation, and test sets**"
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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": 15,
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"id": "ff703901",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_shuffled = df.sample(frac=1, random_state=1).reset_index()\n",
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"\n",
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"df_train = df_shuffled.iloc[:35_000]\n",
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"df_val = df_shuffled.iloc[35_000:40_000]\n",
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"df_test = df_shuffled.iloc[40_000:]\n",
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"\n",
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"df_train.to_csv(\"train.csv\", index=False, encoding=\"utf-8\")\n",
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"df_val.to_csv(\"validation.csv\", index=False, encoding=\"utf-8\")\n",
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"df_test.to_csv(\"test.csv\", index=False, encoding=\"utf-8\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "846d83b1",
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"metadata": {},
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"source": [
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"# 2 Tokenization and Numericalization"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2bd5f770",
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"metadata": {},
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"source": [
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"**Load the dataset via `load_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": 16,
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"id": "a1aa66c7",
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"metadata": {},
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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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"Using custom data configuration default-b388e5fa43c4de1a\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading and preparing dataset csv/default to /home/raschka/.cache/huggingface/datasets/csv/default-b388e5fa43c4de1a/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"
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]
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},
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"version_minor": 0
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"text/plain": [
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"Downloading data files: 0%| | 0/3 [00:00<?, ?it/s]"
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]
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"metadata": {},
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"output_type": "display_data"
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"version_minor": 0
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},
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"text/plain": [
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"Extracting data files: 0%| | 0/3 [00:00<?, ?it/s]"
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]
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"metadata": {},
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
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" return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch-lightning_ipynb/transformer/train.csv'>\n",
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" self.handle.detach()\n",
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"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
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"data": {
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
|
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" return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch-lightning_ipynb/transformer/validation.csv'>\n",
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" self.handle.detach()\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
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" return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
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]
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},
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{
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"text": [
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"Dataset csv downloaded and prepared to /home/raschka/.cache/huggingface/datasets/csv/default-b388e5fa43c4de1a/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"
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"output_type": "stream",
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" self.handle.detach()\n",
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"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
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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": "33ff4316fd5e492496d6751af7373330",
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"DatasetDict({\n",
|
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" train: Dataset({\n",
|
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" features: ['index', 'text', 'label'],\n",
|
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" num_rows: 35000\n",
|
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" })\n",
|
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" validation: Dataset({\n",
|
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" features: ['index', 'text', 'label'],\n",
|
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" num_rows: 5000\n",
|
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" })\n",
|
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" test: Dataset({\n",
|
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" features: ['index', 'text', 'label'],\n",
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" num_rows: 10000\n",
|
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" })\n",
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"})\n"
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]
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}
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],
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"source": [
|
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"imdb_dataset = load_dataset(\n",
|
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" \"csv\",\n",
|
|
" data_files={\n",
|
|
" \"train\": \"train.csv\",\n",
|
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" \"validation\": \"validation.csv\",\n",
|
|
" \"test\": \"test.csv\",\n",
|
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" },\n",
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")\n",
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"\n",
|
|
"print(imdb_dataset)"
|
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]
|
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},
|
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{
|
|
"cell_type": "markdown",
|
|
"id": "029adc8f-cdfe-4386-9552-a1120f49adee",
|
|
"metadata": {},
|
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"source": [
|
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"**Tokenize the dataset**"
|
|
]
|
|
},
|
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{
|
|
"cell_type": "code",
|
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"execution_count": 17,
|
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"id": "5ea762ba",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
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"Tokenizer input max length: 512\n",
|
|
"Tokenizer vocabulary size: 30522\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from transformers import AutoTokenizer\n",
|
|
"\n",
|
|
"tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
|
|
"print(\"Tokenizer input max length:\", tokenizer.model_max_length)\n",
|
|
"print(\"Tokenizer vocabulary size:\", tokenizer.vocab_size)"
|
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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": 18,
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"id": "8432c15c",
|
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"metadata": {},
|
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"outputs": [],
|
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"source": [
|
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"def tokenize_text(batch):\n",
|
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" return tokenizer(batch[\"text\"], truncation=True, padding=True)"
|
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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": 19,
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"id": "0bb392cf",
|
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"metadata": {},
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"outputs": [
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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": "fba13816cb154594aae6ea9562743a43",
|
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"version_major": 2,
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"version_minor": 0
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"metadata": {},
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"output_type": "display_data"
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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": "010c518cd2e84d158031017dfe5c34ff",
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"version_major": 2,
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"version_minor": 0
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},
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"metadata": {},
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"output_type": "display_data"
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},
|
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{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "7edb2a0be5844ed899ad895b806439b7",
|
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"version_major": 2,
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"version_minor": 0
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},
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},
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"metadata": {},
|
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"output_type": "display_data"
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}
|
|
],
|
|
"source": [
|
|
"imdb_tokenized = imdb_dataset.map(tokenize_text, batched=True, batch_size=None)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "6d4103c3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"del imdb_dataset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "89ef894c-978f-47f2-9d61-cb6a9f38e745",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"imdb_tokenized.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"label\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "0ea67091-aeb7-46c1-871f-638ce58d8a0e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import os\n",
|
|
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4c4f8cd8-e641-45fb-9893-70677631917a",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 3 Set Up DataLoaders"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "0807b068-7d8f-4055-a26a-177e07dea4c7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from torch.utils.data import DataLoader, Dataset\n",
|
|
"\n",
|
|
"\n",
|
|
"class IMDBDataset(Dataset):\n",
|
|
" def __init__(self, dataset_dict, partition_key=\"train\"):\n",
|
|
" self.partition = dataset_dict[partition_key]\n",
|
|
"\n",
|
|
" def __getitem__(self, index):\n",
|
|
" return self.partition[index]\n",
|
|
"\n",
|
|
" def __len__(self):\n",
|
|
" return self.partition.num_rows"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "90cb08f3-ef77-4351-8b19-42d99dd24f98",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"train_dataset = IMDBDataset(imdb_tokenized, partition_key=\"train\")\n",
|
|
"val_dataset = IMDBDataset(imdb_tokenized, partition_key=\"validation\")\n",
|
|
"test_dataset = IMDBDataset(imdb_tokenized, partition_key=\"test\")\n",
|
|
"\n",
|
|
"train_loader = DataLoader(\n",
|
|
" dataset=train_dataset,\n",
|
|
" batch_size=12,\n",
|
|
" shuffle=True, \n",
|
|
" num_workers=4\n",
|
|
")\n",
|
|
"\n",
|
|
"val_loader = DataLoader(\n",
|
|
" dataset=val_dataset,\n",
|
|
" batch_size=12,\n",
|
|
" num_workers=4\n",
|
|
")\n",
|
|
"\n",
|
|
"test_loader = DataLoader(\n",
|
|
" dataset=test_dataset,\n",
|
|
" batch_size=12,\n",
|
|
" num_workers=4\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6c977b07-2559-4ff7-85c4-d62785e81558",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 4 Initializing DistilBERT"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "10b1e449-7e00-4965-9883-f97374503996",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias']\n",
|
|
"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
|
|
"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
|
|
"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
|
|
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from transformers import AutoModelForSequenceClassification\n",
|
|
"\n",
|
|
"\n",
|
|
"model = AutoModelForSequenceClassification.from_pretrained(\n",
|
|
" \"distilbert-base-uncased\", num_labels=2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e9854e27-fc4f-418e-996f-a8ef04a295ac",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Freeze all layers**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"id": "c14e61f3-3279-4725-ad7a-3c77f85aa39d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for param in model.parameters():\n",
|
|
" param.requires_grad = False"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a6d3e532-3684-4dbf-a8ce-60b6c6675a00",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Unfreeze last layer**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "1bca22b5-8a48-4006-929f-66734a745a50",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"DistilBertForSequenceClassification(\n",
|
|
" (distilbert): DistilBertModel(\n",
|
|
" (embeddings): Embeddings(\n",
|
|
" (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
|
|
" (position_embeddings): Embedding(512, 768)\n",
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|
" )\n",
|
|
" (transformer): Transformer(\n",
|
|
" (layer): ModuleList(\n",
|
|
" (0): TransformerBlock(\n",
|
|
" (attention): MultiHeadSelfAttention(\n",
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|
" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" )\n",
|
|
" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|
" (ffn): FFN(\n",
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|
" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|
" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|
" (activation): GELUActivation()\n",
|
|
" )\n",
|
|
" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|
" )\n",
|
|
" (1): TransformerBlock(\n",
|
|
" (attention): MultiHeadSelfAttention(\n",
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|
" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" )\n",
|
|
" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|
" (ffn): FFN(\n",
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|
" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|
" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|
" (activation): GELUActivation()\n",
|
|
" )\n",
|
|
" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|
" )\n",
|
|
" (2): TransformerBlock(\n",
|
|
" (attention): MultiHeadSelfAttention(\n",
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|
" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
|
" )\n",
|
|
" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|
" (ffn): FFN(\n",
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
|
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" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
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" (activation): GELUActivation()\n",
|
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" )\n",
|
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" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
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" )\n",
|
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" (3): TransformerBlock(\n",
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" (attention): MultiHeadSelfAttention(\n",
|
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" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" )\n",
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" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
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" (ffn): FFN(\n",
|
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" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
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" (activation): GELUActivation()\n",
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" )\n",
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" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
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" )\n",
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" (4): TransformerBlock(\n",
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" (attention): MultiHeadSelfAttention(\n",
|
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" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
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" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" )\n",
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" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
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" (ffn): FFN(\n",
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" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
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" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
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" (activation): GELUActivation()\n",
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" )\n",
|
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" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
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" )\n",
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" (5): TransformerBlock(\n",
|
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" (attention): MultiHeadSelfAttention(\n",
|
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" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" )\n",
|
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" (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
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" (ffn): FFN(\n",
|
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" (dropout): Dropout(p=0.1, inplace=False)\n",
|
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" (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
|
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" (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
|
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" (activation): GELUActivation()\n",
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" )\n",
|
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" (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
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" )\n",
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" )\n",
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" )\n",
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" )\n",
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" (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
|
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" (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
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" (dropout): Dropout(p=0.2, inplace=False)\n",
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")"
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]
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},
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"execution_count": 27,
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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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"model"
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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": 28,
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"id": "4f852fc6-3b00-4831-a973-e03b539a6cd0",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"for param in model.pre_classifier.parameters():\n",
|
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" param.requires_grad = True\n",
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"\n",
|
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"for param in model.classifier.parameters():\n",
|
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" param.requires_grad = True"
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]
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},
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{
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"cell_type": "markdown",
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"id": "83971bc4-143d-4d82-b5cb-8774f9112f53",
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"metadata": {},
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"source": [
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"# 5 Finetuning"
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]
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},
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{
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"cell_type": "markdown",
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"id": "730b9a87-709e-4f3d-9c9f-475ccbb56908",
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"metadata": {},
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"source": [
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"**Wrap in LightningModule for Training**"
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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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"id": "9f2c474d",
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"metadata": {},
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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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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/torchmetrics/utilities/prints.py:36: DeprecationWarning: From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification metric. Moving forward we recommend using these versions. This base metric will still work as it did prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required and the general order of arguments may change, such that this metric will just function as an single entrypoint to calling the three specialized versions.\n",
|
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" warnings.warn(*args, **kwargs)\n"
|
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]
|
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}
|
|
],
|
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"source": [
|
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"import lightning as L\n",
|
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"import torch\n",
|
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"import torchmetrics\n",
|
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"\n",
|
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"\n",
|
|
"class LightningModel(L.LightningModule):\n",
|
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" def __init__(self, model, learning_rate=5e-5):\n",
|
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" super().__init__()\n",
|
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"\n",
|
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" self.learning_rate = learning_rate\n",
|
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" self.model = model\n",
|
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"\n",
|
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" self.val_acc = torchmetrics.Accuracy()\n",
|
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" self.test_acc = torchmetrics.Accuracy()\n",
|
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"\n",
|
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" def forward(self, input_ids, attention_mask, labels):\n",
|
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" return self.model(input_ids, attention_mask=attention_mask, labels=labels)\n",
|
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" \n",
|
|
" def training_step(self, batch, batch_idx):\n",
|
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" outputs = self(batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"],\n",
|
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" labels=batch[\"label\"]) \n",
|
|
" self.log(\"train_loss\", outputs[\"loss\"])\n",
|
|
" return outputs[\"loss\"] # this is passed to the optimizer for training\n",
|
|
"\n",
|
|
" def validation_step(self, batch, batch_idx):\n",
|
|
" outputs = self(batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"],\n",
|
|
" labels=batch[\"label\"]) \n",
|
|
" self.log(\"val_loss\", outputs[\"loss\"], prog_bar=True)\n",
|
|
" \n",
|
|
" logits = outputs[\"logits\"]\n",
|
|
" predicted_labels = torch.argmax(logits, 1)\n",
|
|
" self.val_acc(predicted_labels, batch[\"label\"])\n",
|
|
" self.log(\"val_acc\", self.val_acc, prog_bar=True)\n",
|
|
" \n",
|
|
" def test_step(self, batch, batch_idx):\n",
|
|
" outputs = self(batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"],\n",
|
|
" labels=batch[\"label\"]) \n",
|
|
" \n",
|
|
" logits = outputs[\"logits\"]\n",
|
|
" predicted_labels = torch.argmax(logits, 1)\n",
|
|
" self.test_acc(predicted_labels, batch[\"label\"])\n",
|
|
" self.log(\"accuracy\", self.test_acc, prog_bar=True)\n",
|
|
"\n",
|
|
" def configure_optimizers(self):\n",
|
|
" optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
|
|
" return optimizer\n",
|
|
" \n",
|
|
"\n",
|
|
"lightning_model = LightningModel(model)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
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"id": "e6dab813-e1fc-47cd-87a1-5eb8070699c6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from lightning.pytorch.callbacks import ModelCheckpoint\n",
|
|
"from lightning.pytorch.loggers import CSVLogger\n",
|
|
"\n",
|
|
"\n",
|
|
"callbacks = [\n",
|
|
" ModelCheckpoint(\n",
|
|
" save_top_k=1, mode=\"max\", monitor=\"val_acc\"\n",
|
|
" ) # save top 1 model\n",
|
|
"]\n",
|
|
"logger = CSVLogger(save_dir=\"logs/\", name=\"my-model\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": 31,
|
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"id": "492aa043-02da-459e-a266-091b34254ac6",
|
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"metadata": {},
|
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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": [
|
|
"GPU available: True (cuda), used: True\n",
|
|
"TPU available: False, using: 0 TPU cores\n",
|
|
"IPU available: False, using: 0 IPUs\n",
|
|
"HPU available: False, using: 0 HPUs\n",
|
|
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
|
|
"\n",
|
|
" | Name | Type | Params\n",
|
|
"-----------------------------------------------------------------\n",
|
|
"0 | model | DistilBertForSequenceClassification | 67.0 M\n",
|
|
"1 | val_acc | Accuracy | 0 \n",
|
|
"2 | test_acc | Accuracy | 0 \n",
|
|
"-----------------------------------------------------------------\n",
|
|
"592 K Trainable params\n",
|
|
"66.4 M Non-trainable params\n",
|
|
"67.0 M Total params\n",
|
|
"267.820 Total estimated model params size (MB)\n"
|
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]
|
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},
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"data": {
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"text/plain": [
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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" self.pid = os.fork()\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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" self.pid = os.fork()\n",
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"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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" self.pid = os.fork()\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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" self.pid = os.fork()\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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" self.pid = os.fork()\n",
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"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"source": [
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"Restoring states from the checkpoint path at logs/my-model/version_2/checkpoints/epoch=1-step=5834.ckpt\n",
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"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:489: PossibleUserWarning: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test/predict dataloaders.\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\n",
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"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\n",
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"[{'accuracy': 0.8672000169754028}]"
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"source": [
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"trainer.test(lightning_model, dataloaders=train_loader, ckpt_path=\"best\")"
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},
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{
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"cell_type": "code",
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Restoring states from the checkpoint path at logs/my-model/version_2/checkpoints/epoch=1-step=5834.ckpt\n",
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"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
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"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\n",
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