784 lines
18 KiB
Plaintext
784 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Ekstrakcja informacji </h1>\n",
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"<h2> 11. <i>NER RNN</i> [ćwiczenia]</h2> \n",
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"<h3> Jakub Pokrywka (2021)</h3>\n",
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"</div>\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": "markdown",
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"metadata": {},
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"source": [
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"### Podejście softmax z embeddingami na przykładzie NER"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import torch\n",
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"import pandas as pd\n",
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"\n",
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"from datasets import load_dataset\n",
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"import torchtext\n",
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"#from torchtext.vocab import vocab\n",
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"from collections import Counter\n",
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"\n",
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"\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"from tqdm.notebook import tqdm\n",
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"\n",
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"import torch"
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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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"outputs": [],
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"source": [
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"device = 'cpu'"
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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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"scrolled": false
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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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"Reusing dataset conll2003 (/home/kuba/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/63f4ebd1bcb7148b1644497336fd74643d4ce70123334431a3c053b7ee4e96ee)\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": "5537459a83cc486e927e938f813a5794",
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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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"source": [
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"dataset = load_dataset(\"conll2003\")"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def build_vocab(dataset):\n",
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" counter = Counter()\n",
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" for document in dataset:\n",
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" counter.update(document)\n",
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" vocab = torchtext.vocab.vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])\n",
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" vocab.set_default_index(0)\n",
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" return vocab"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"vocab = build_vocab(dataset['train']['tokens'])"
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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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"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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"21"
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]
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},
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"execution_count": 6,
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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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"vocab['on']"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def data_process(dt):\n",
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" return [ torch.tensor([vocab['<bos>']] +[vocab[token] for token in document ] + [vocab['<eos>']], dtype = torch.long) for document in dt]"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def labels_process(dt):\n",
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" return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]\n"
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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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"outputs": [],
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"source": [
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"train_tokens_ids = data_process(dataset['train']['tokens'])"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"test_tokens_ids = data_process(dataset['test']['tokens'])"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"validation_tokens_ids = data_process(dataset['validation']['tokens'])"
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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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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"train_labels = labels_process(dataset['train']['ner_tags'])"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"validation_labels = labels_process(dataset['validation']['ner_tags'])"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"test_labels = labels_process(dataset['test']['ner_tags'])"
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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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"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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"tensor([ 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 3])"
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]
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},
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"execution_count": 15,
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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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"train_tokens_ids[0]"
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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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"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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"{'id': '0',\n",
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" 'tokens': ['EU',\n",
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" 'rejects',\n",
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" 'German',\n",
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" 'call',\n",
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" 'to',\n",
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" 'boycott',\n",
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" 'British',\n",
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" 'lamb',\n",
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" '.'],\n",
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" 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\n",
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" 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\n",
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" 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]}"
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]
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},
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"execution_count": 16,
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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['train'][0]"
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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": 17,
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"metadata": {
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"scrolled": true
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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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"tensor([0, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0])"
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]
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},
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"execution_count": 17,
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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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"train_labels[0]"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def get_scores(y_true, y_pred):\n",
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" acc_score = 0\n",
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" tp = 0\n",
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" fp = 0\n",
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" selected_items = 0\n",
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" relevant_items = 0 \n",
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"\n",
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" for p,t in zip(y_pred, y_true):\n",
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" if p == t:\n",
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" acc_score +=1\n",
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"\n",
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" if p > 0 and p == t:\n",
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" tp +=1\n",
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"\n",
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" if p > 0:\n",
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" selected_items += 1\n",
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"\n",
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" if t > 0 :\n",
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" relevant_items +=1\n",
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"\n",
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" \n",
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" \n",
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" if selected_items == 0:\n",
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" precision = 1.0\n",
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" else:\n",
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" precision = tp / selected_items\n",
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" \n",
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" \n",
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" if relevant_items == 0:\n",
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" recall = 1.0\n",
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" else:\n",
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" recall = tp / relevant_items\n",
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" \n",
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" \n",
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" if precision + recall == 0.0 :\n",
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" f1 = 0.0\n",
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" else:\n",
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" f1 = 2* precision * recall / (precision + recall)\n",
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"\n",
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" return precision, recall, f1"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"num_tags = max([max(x) for x in dataset['train']['ner_tags'] if x]) + 1 "
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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": 20,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"class LSTM(torch.nn.Module):\n",
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"\n",
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" def __init__(self):\n",
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" super(LSTM, self).__init__()\n",
|
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" self.emb = torch.nn.Embedding(len(vocab.get_itos()),100)\n",
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" self.rec = torch.nn.LSTM(100, 256, 1, batch_first = True)\n",
|
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" self.fc1 = torch.nn.Linear( 256 , 9)\n",
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"\n",
|
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" def forward(self, x):\n",
|
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" emb = torch.relu(self.emb(x))\n",
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" \n",
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" lstm_output, (h_n, c_n) = self.rec(emb)\n",
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" \n",
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" out_weights = self.fc1(lstm_output)\n",
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"\n",
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" return out_weights"
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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": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"lstm = LSTM().to(device)"
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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": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"criterion = torch.nn.CrossEntropyLoss().to(device)"
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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": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"optimizer = torch.optim.Adam(lstm.parameters())"
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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": 24,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"def eval_model(dataset_tokens, dataset_labels, model):\n",
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" Y_true = []\n",
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" Y_pred = []\n",
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" for i in tqdm(range(len(dataset_labels))):\n",
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" batch_tokens = dataset_tokens[i].unsqueeze(0).to(device)\n",
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" tags = list(dataset_labels[i].numpy())\n",
|
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" Y_true += tags\n",
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" \n",
|
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" Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
|
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" Y_batch_pred = torch.argmax(Y_batch_pred_weights,1)\n",
|
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" Y_pred += list(Y_batch_pred.cpu().numpy())\n",
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" \n",
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"\n",
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" return get_scores(Y_true, Y_pred)\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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"NUM_EPOCHS = 5"
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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": 26,
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"metadata": {
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"scrolled": true
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"model_id": "9a7d4c3ffdd445fa9765bc2233bb2cf5",
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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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},
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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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"(0.7282225874618455, 0.7210275485295827, 0.7246072075229251)\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": "ca77549e5d4248a4bdd51b66865505da",
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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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},
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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": "bd2ec9174d50443db5aa98b9d8b50c66",
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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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},
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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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"(0.7124554367201426, 0.7433453446472161, 0.7275726719381079)\n"
|
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]
|
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}
|
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],
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"source": [
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"for i in range(NUM_EPOCHS):\n",
|
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" lstm.train()\n",
|
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" #for i in tqdm(range(5000)):\n",
|
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" for i in tqdm(range(len(train_labels))):\n",
|
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" batch_tokens = train_tokens_ids[i].unsqueeze(0).to(device)\n",
|
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" tags = train_labels[i].unsqueeze(1).to(device)\n",
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" \n",
|
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" \n",
|
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" predicted_tags = lstm(batch_tokens)\n",
|
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"\n",
|
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" \n",
|
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" optimizer.zero_grad()\n",
|
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" loss = criterion(predicted_tags.squeeze(0),tags.squeeze(1))\n",
|
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" \n",
|
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" loss.backward()\n",
|
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" optimizer.step()\n",
|
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" \n",
|
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" lstm.eval()\n",
|
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" print(eval_model(validation_tokens_ids, validation_labels, lstm))"
|
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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": 27,
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"metadata": {
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"scrolled": true
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},
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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": "f8fc75fb00954c3eb59aa2d40786fef7",
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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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]
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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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"data": {
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"text/plain": [
|
|
"(0.7124554367201426, 0.7433453446472161, 0.7275726719381079)"
|
|
]
|
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},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
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"source": [
|
|
"eval_model(validation_tokens_ids, validation_labels, lstm)"
|
|
]
|
|
},
|
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{
|
|
"cell_type": "code",
|
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"execution_count": 28,
|
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"metadata": {},
|
|
"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": "0709c9b9be2446ea86e1ea0bc8b5ae3a",
|
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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%| | 0/3454 [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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},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(0.6445353594389246, 0.6797337278106509, 0.6616667666646667)"
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"eval_model(test_tokens_ids, test_labels, lstm)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"14042"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(train_tokens_ids)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## pytania\n",
|
|
"\n",
|
|
"- co zrobić z trenowaniem na batchach > 1 ?\n",
|
|
"- co zrobić, żeby sieć uwzględniała następne tokeny, a nie tylko poprzednie?\n",
|
|
"- w jaki sposób wykorzystać taką sieć do zadania zwykłej klasyfikacji?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Zadanie na zajęcia ( 20 minut)\n",
|
|
"\n",
|
|
"zmodyfikować sieć tak, żeby była używała dwuwarstwowej, dwukierunkowej warstwy GRU oraz dropoutu. Dropout ma nałożony na embeddingi.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Zadanie domowe\n",
|
|
"\n",
|
|
"\n",
|
|
"- stworzyć model seq labelling bazujący na sieci neuronowej opisanej w punkcie niżej (można bazować na tym jupyterze lub nie).\n",
|
|
"- model sieci to GRU (o dowolnych parametrach) + CRF w pytorchu korzystając z modułu CRF z poprzednich zajęć- - stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
|
|
"- wynik fscore sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.65\n",
|
|
"termin 22.06, 60 punktów, za najlepszy wynik- 100 punktów\n",
|
|
" "
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"author": "Jakub Pokrywka",
|
|
"email": "kubapok@wmi.amu.edu.pl",
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"lang": "pl",
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.4"
|
|
},
|
|
"subtitle": "11.NER RNN[ćwiczenia]",
|
|
"title": "Ekstrakcja informacji",
|
|
"year": "2021"
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|