1159 lines
42 KiB
Plaintext
1159 lines
42 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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"collapsed": false
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},
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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> [\u0107wiczenia]</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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"### Podej\u015bcie softmax z embeddingami na przyk\u0142adzie 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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
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" warnings.warn(msg)\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import gensim\n",
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"import torch\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"from datasets import load_dataset\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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"from sklearn.datasets import fetch_20newsgroups\n",
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"# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\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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"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/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6)\n"
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]
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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": 3,
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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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" return Vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])"
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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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"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": 5,
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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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"23627"
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]
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},
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"execution_count": 5,
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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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"len(vocab.itos)"
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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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"15"
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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, 966, 22409, 238, 773, 9, 4588, 212, 7686, 4,\n",
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" 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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"{'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\n",
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" 'id': '0',\n",
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" 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0],\n",
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" 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\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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" '.']}"
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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'] ]) + 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.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()"
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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()"
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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)\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.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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},
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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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"\n",
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"(0.7140486069946651, 0.7001046146693014, 0.7070078647728607)\n"
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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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"\n"
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]
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"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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"\n",
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"(0.756327964151629, 0.725909566430315, 0.7408066429418744)\n"
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]
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},
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{
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"data": {
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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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"\n"
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]
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},
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{
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"data": {
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"model_id": "2f78871f366f4fd1b7de6c4be5303906",
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"HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
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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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"\n",
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"(0.7963248522230789, 0.7203301174009067, 0.7564235581324383)\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(500)):\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)\n",
|
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" tags = train_labels[i].unsqueeze(1)\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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"model_id": "5159f7a61c3a439bab45573f15ea55b2",
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"version_minor": 0
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},
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"HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
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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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"\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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"(0.7963248522230789, 0.7203301174009067, 0.7564235581324383)"
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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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"source": [
|
|
"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": 28,
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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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},
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"HBox(children=(FloatProgress(value=0.0, max=3453.0), HTML(value='')))"
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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": [
|
|
"\n"
|
|
]
|
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},
|
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{
|
|
"data": {
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"text/plain": [
|
|
"(0.7450810185185185, 0.6348619329388561, 0.685569755058573)"
|
|
]
|
|
},
|
|
"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": {
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|
"text/plain": [
|
|
"14041"
|
|
]
|
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},
|
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"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(train_tokens_ids)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## pytania\n",
|
|
"\n",
|
|
"- co zrobi\u0107 z trenowaniem na batchach > 1 ?\n",
|
|
"- co zrobi\u0107, \u017ceby sie\u0107 uwzgl\u0119dnia\u0142a nast\u0119pne tokeny, a nie tylko poprzednie?\n",
|
|
"- w jaki spos\u00f3b wykorzysta\u0107 tak\u0105 sie\u0107 do zadania zwyk\u0142ej klasyfikacji?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Zadanie na zaj\u0119cia ( 20 minut)\n",
|
|
"\n",
|
|
"zmodyfikowa\u0107 sie\u0107 tak, \u017ceby by\u0142a u\u017cywa\u0142a dwuwarstwowej, dwukierunkowej warstwy GRU oraz dropoutu. Dropout ma na\u0142o\u017cony na embeddingi.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"class GRU(torch.nn.Module):\n",
|
|
"\n",
|
|
" def __init__(self):\n",
|
|
" super(GRU, self).__init__()\n",
|
|
" self.emb = torch.nn.Embedding(len(vocab.itos),100)\n",
|
|
" self.dropout = torch.nn.Dropout(0.2)\n",
|
|
" self.rec = torch.nn.GRU(100, 256, 2, batch_first = True, bidirectional = True)\n",
|
|
" self.fc1 = torch.nn.Linear(2* 256 , 9)\n",
|
|
" \n",
|
|
" def forward(self, x):\n",
|
|
" emb = torch.relu(self.emb(x))\n",
|
|
" emb = self.dropout(emb)\n",
|
|
" \n",
|
|
" gru_output, h_n = self.rec(emb)\n",
|
|
" \n",
|
|
" out_weights = self.fc1(gru_output)\n",
|
|
"\n",
|
|
" return out_weights"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"gru = GRU()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"criterion = torch.nn.CrossEntropyLoss()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"optimizer = torch.optim.Adam(gru.parameters())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"NUM_EPOCHS = 5"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
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"data": {
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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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"\n"
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
|
"\n",
|
|
"(0.6431379891406104, 0.39927932116703474, 0.49268502581755597)\n"
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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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"\n"
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"name": "stdout",
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"text": [
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"\n",
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|
"(0.6638910917261432, 0.5838660932232942, 0.6213123879027769)\n"
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{
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"name": "stdout",
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"\n"
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"text": [
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"\n",
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|
"(0.6697936210131332, 0.7054515866558178, 0.6871603260869565)\n"
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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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"\n"
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]
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},
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{
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"data": {
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"HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
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},
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"metadata": {},
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},
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{
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|
"name": "stdout",
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|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"(0.7091097308488613, 0.7166104847146344, 0.7128403769439787)\n"
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]
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{
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"data": {
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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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{
|
|
"name": "stdout",
|
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"output_type": "stream",
|
|
"text": [
|
|
"\n"
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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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},
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))"
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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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{
|
|
"name": "stdout",
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"output_type": "stream",
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|
"text": [
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|
"\n",
|
|
"(0.7708963348252087, 0.740788097175404, 0.755542382928275)\n"
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]
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}
|
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],
|
|
"source": [
|
|
"for i in range(NUM_EPOCHS):\n",
|
|
" gru.train()\n",
|
|
" #for i in tqdm(range(50)):\n",
|
|
" for i in tqdm(range(len(train_labels))):\n",
|
|
" batch_tokens = train_tokens_ids[i].unsqueeze(0)\n",
|
|
" tags = train_labels[i].unsqueeze(1)\n",
|
|
" \n",
|
|
" \n",
|
|
" predicted_tags = gru(batch_tokens)\n",
|
|
"\n",
|
|
" \n",
|
|
" optimizer.zero_grad()\n",
|
|
" loss = criterion(predicted_tags.squeeze(0),tags.squeeze(1))\n",
|
|
" \n",
|
|
" loss.backward()\n",
|
|
" optimizer.step()\n",
|
|
" \n",
|
|
" \n",
|
|
" gru.eval()\n",
|
|
" print(eval_model(validation_tokens_ids, validation_labels, gru))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Zadanie domowe\n",
|
|
"\n",
|
|
"\n",
|
|
"- sklonowa\u0107 repozytorium https://git.wmi.amu.edu.pl/kubapok/en-ner-conll-2003\n",
|
|
"- stworzy\u0107 model seq labelling bazuj\u0105cy na sieci neuronowej opisanej w punkcie ni\u017cej (mo\u017cna bazowa\u0107 na tym jupyterze lub nie).\n",
|
|
"- model sieci to GRU (o dowolnych parametrach) + CRF w pytorchu korzystaj\u0105c z modu\u0142u CRF z poprzednich zaj\u0119\u0107- - stworzy\u0107 predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n",
|
|
"- wynik fscore sprawdzony za pomoc\u0105 narz\u0119dzia geval (patrz poprzednie zadanie) powinien wynosi\u0107 conajmniej 0.65\n",
|
|
"- prosz\u0119 umie\u015bci\u0107 predykcj\u0119 oraz skrypty generuj\u0105ce (w postaci tekstowej a nie jupyter) w repo, a w MS TEAMS umie\u015bci\u0107 link do swojego repo\n",
|
|
"termin 22.06, 60 punkt\u00f3w, za najlepszy wynik- 100 punkt\u00f3w\n",
|
|
" "
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.5"
|
|
},
|
|
"author": "Jakub Pokrywka",
|
|
"email": "kubapok@wmi.amu.edu.pl",
|
|
"lang": "pl",
|
|
"subtitle": "11.NER RNN[\u0107wiczenia]",
|
|
"title": "Ekstrakcja informacji",
|
|
"year": "2021"
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
} |