forked from kubapok/en-ner-conll-2003
lab8
This commit is contained in:
Alicja Szulecka
parent
1397a7a5c2
commit
ef0e228ed8
668
lab8.ipynb
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668
lab8.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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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 collections import Counter\n",
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"from torchtext.vocab import vocab\n",
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"from sklearn.metrics import accuracy_score\n",
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"from tqdm import tqdm"
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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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"#Wczytanie zbioru danych\n",
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"\n",
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"train_set = pd.read_csv('./train/train.tsv', sep='\\t', header=None, names=['labels', 'text'])\n",
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"val_set = pd.read_csv('./dev-0/expected.tsv', sep='\\t', header=None, names=['labels'])\n",
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"val_set['text'] = pd.read_csv('./dev-0/in.tsv', sep='\\t', header=None, names=['text'])\n",
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"test_set = pd.read_csv('./test-A/in.tsv', sep='\\t', header=None, names=['text'])"
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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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"#Tokenizacja danych\n",
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"train_set['text'] = train_set[\"text\"].apply(lambda x : x.split())\n",
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"train_set['labels'] = train_set[\"labels\"].apply(lambda x : x.split())\n",
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"\n",
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"val_set['text'] = val_set[\"text\"].apply(lambda x : x.split())\n",
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"val_set['labels'] = val_set[\"labels\"].apply(lambda x : x.split())\n",
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"\n",
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"test_set['text'] = test_set[\"text\"].apply(lambda x : x.split())"
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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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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>labels</th>\n",
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" <th>text</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>[B-ORG, O, B-MISC, O, O, O, B-MISC, O, O, O, B...</td>\n",
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" <td>[EU, rejects, German, call, to, boycott, Briti...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>[O, B-PER, O, O, O, O, O, O, O, O, O, B-LOC, O...</td>\n",
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" <td>[Rare, Hendrix, song, draft, sells, for, almos...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>[B-LOC, O, B-LOC, O, O, O, O, O, O, B-LOC, O, ...</td>\n",
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" <td>[China, says, Taiwan, spoils, atmosphere, for,...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>[B-LOC, O, O, O, O, B-LOC, O, O, O, B-LOC, O, ...</td>\n",
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" <td>[China, says, time, right, for, Taiwan, talks,...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>[B-MISC, O, O, O, O, O, O, O, O, O, O, O, B-LO...</td>\n",
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" <td>[German, July, car, registrations, up, 14.2, p...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>[B-MISC, O, O, O, O, O, O, O, O, O, O, B-LOC, ...</td>\n",
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" <td>[GREEK, SOCIALISTS, GIVE, GREEN, LIGHT, TO, PM...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>[B-ORG, O, B-MISC, O, O, O, O, O, O, B-LOC, O,...</td>\n",
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" <td>[BayerVB, sets, C$, 100, million, six-year, bo...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>[B-ORG, O, O, O, O, O, O, O, O, O, B-LOC, O, O...</td>\n",
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" <td>[Venantius, sets, $, 300, million, January, 19...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>[O, O, O, O, B-LOC, O, B-ORG, I-ORG, O, O, O, ...</td>\n",
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" <td>[Port, conditions, update, -, Syria, -, Lloyds...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>[B-LOC, O, O, O, O, O, O, B-LOC, O, O, B-PER, ...</td>\n",
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" <td>[Israel, plays, down, fears, of, war, with, Sy...</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" labels \\\n",
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"0 [B-ORG, O, B-MISC, O, O, O, B-MISC, O, O, O, B... \n",
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"1 [O, B-PER, O, O, O, O, O, O, O, O, O, B-LOC, O... \n",
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"2 [B-LOC, O, B-LOC, O, O, O, O, O, O, B-LOC, O, ... \n",
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"3 [B-LOC, O, O, O, O, B-LOC, O, O, O, B-LOC, O, ... \n",
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"4 [B-MISC, O, O, O, O, O, O, O, O, O, O, O, B-LO... \n",
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"5 [B-MISC, O, O, O, O, O, O, O, O, O, O, B-LOC, ... \n",
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"6 [B-ORG, O, B-MISC, O, O, O, O, O, O, B-LOC, O,... \n",
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"7 [B-ORG, O, O, O, O, O, O, O, O, O, B-LOC, O, O... \n",
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"8 [O, O, O, O, B-LOC, O, B-ORG, I-ORG, O, O, O, ... \n",
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"9 [B-LOC, O, O, O, O, O, O, B-LOC, O, O, B-PER, ... \n",
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"\n",
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" text \n",
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"0 [EU, rejects, German, call, to, boycott, Briti... \n",
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"1 [Rare, Hendrix, song, draft, sells, for, almos... \n",
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"2 [China, says, Taiwan, spoils, atmosphere, for,... \n",
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"3 [China, says, time, right, for, Taiwan, talks,... \n",
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"4 [German, July, car, registrations, up, 14.2, p... \n",
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"5 [GREEK, SOCIALISTS, GIVE, GREEN, LIGHT, TO, PM... \n",
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"6 [BayerVB, sets, C$, 100, million, six-year, bo... \n",
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"7 [Venantius, sets, $, 300, million, January, 19... \n",
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"8 [Port, conditions, update, -, Syria, -, Lloyds... \n",
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"9 [Israel, plays, down, fears, of, war, with, Sy... "
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]
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},
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"execution_count": 22,
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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_set.head(10)\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": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Budowanie słownika\n",
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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>\"])\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": 24,
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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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"['<unk>',\n",
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" '<pad>',\n",
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" '<bos>',\n",
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" '<eos>',\n",
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" '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']"
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]
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},
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"execution_count": 24,
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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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"v = build_vocab(train_set['text'])\n",
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"v.set_default_index(v[\"<unk>\"])\n",
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"\n",
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"itos = v.get_itos()\n",
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"\n",
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"itos[:10]"
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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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"def data_process(dt):\n",
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" # Wektoryzacja dokumentów tekstowych.\n",
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" return [\n",
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" torch.tensor(\n",
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" [v[\"<bos>\"]] + [v[token] for token in document] + [v[\"<eos>\"]],\n",
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" dtype=torch.long,\n",
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" )\n",
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" for document in dt\n",
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" ]\n",
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"\n",
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"def labels_process(dt):\n",
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" # Wektoryzacja etykiet (NER)\n",
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" return [torch.tensor([0] + document + [0], 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": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Różne tagi NER\n",
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"num_tags = {\n",
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" \"O\" : 0,\n",
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" \"B-PER\" : 1,\n",
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" \"I-PER\" : 2,\n",
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" \"B-ORG\" : 3,\n",
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" \"I-ORG\" : 4,\n",
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" \"B-LOC\" : 5,\n",
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" \"I-LOC\" : 6,\n",
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" \"B-MISC\" : 7,\n",
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" \"I-MISC\" : 8,\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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"def covert_to_int(dt, tags):\n",
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" labels = []\n",
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" for label in dt:\n",
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" labels.append([tags[i] for i in label])\n",
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" return labels"
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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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"outputs": [],
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"source": [
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"train_tokens_ids = data_process(train_set['text'])\n",
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"train_labels_ids = labels_process(covert_to_int(train_set['labels'], tags=num_tags))\n",
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"\n",
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"val_tokens_ids = data_process(val_set['text'])\n",
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"val_labels_ids = labels_process(covert_to_int(val_set['labels'], tags=num_tags))\n",
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"\n",
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"test_tokens_ids = data_process(train_set['text'])"
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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": 43,
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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, num_tags):\n",
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" super(LSTM, self).__init__()\n",
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" self.emb = torch.nn.Embedding(len(v.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, num_tags)\n",
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" self.hidden2tag = torch.nn.Linear(20, num_tags)\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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" lstm_output, (h_n, c_n) = self.rec(emb)\n",
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" out_weights = self.fc1(lstm_output)\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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"EPOCHS = 10\n",
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"LR = 0.001\n",
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"NUM_TAGS = len(num_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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = LSTM(num_tags=NUM_TAGS)\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=LR)\n",
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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": 39,
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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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" # Funkcja zwraca precyzję, pokrycie i F1\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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" 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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" 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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" 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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" acc = accuracy_score(y_true, y_pred)\n",
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" return precision, recall, f1, acc"
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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": 40,
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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 = dataset_labels[i].unsqueeze(1)\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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" precision, recall, f1, acc = get_scores(Y_true, Y_pred)\n",
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" print(f'precision: {precision}, recall: {recall}, f1: {f1}, val accuracy: {acc}')"
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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": 46,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 945/945 [02:21<00:00, 6.68it/s]\n",
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"100%|██████████| 215/215 [00:02<00:00, 93.42it/s] \n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"precision: 0.8434014196726061, recall: 0.6783966441388953, f1: 0.7519535033903778, val accuracy: 0.9457621556580554\n",
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"Train accuracy: 0.9983919167623316\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 945/945 [02:14<00:00, 7.04it/s]\n",
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]
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},
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{
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"precision: 0.8440340076223981, recall: 0.6709391750174785, f1: 0.7475980264866269, val accuracy: 0.9454522251189587\n",
|
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"Train accuracy: 0.9989522403833889\n"
|
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]
|
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},
|
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 945/945 [02:08<00:00, 7.34it/s]\n",
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]
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},
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{
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"name": "stdout",
|
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"output_type": "stream",
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"text": [
|
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"precision: 0.852653120888759, recall: 0.6796783966441389, f1: 0.7564027750761848, val accuracy: 0.9472206523126288\n",
|
||||
"Train accuracy: 0.9993303449406877\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 945/945 [02:18<00:00, 6.85it/s]\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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"precision: 0.8375809935205184, recall: 0.6778140293637847, f1: 0.7492754556578862, val accuracy: 0.9455980747844159\n",
|
||||
"Train accuracy: 0.9991891251662749\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
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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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"precision: 0.8413109098749461, recall: 0.6820088557445817, f1: 0.7533303301370746, val accuracy: 0.9462908606953383\n",
|
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"Train accuracy: 0.9991435704003353\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
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"output_type": "stream",
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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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"precision: 0.8479315263908702, recall: 0.6926124446515963, f1: 0.7624422780913289, val accuracy: 0.9478769758071868\n",
|
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"Train accuracy: 0.998583246779278\n"
|
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]
|
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},
|
||||
{
|
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"name": "stderr",
|
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"output_type": "stream",
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"precision: 0.8470877294406706, recall: 0.6829410393847588, f1: 0.7562092768208503, val accuracy: 0.9471294962717179\n",
|
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"Train accuracy: 0.999180014213087\n"
|
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]
|
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},
|
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{
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"name": "stderr",
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"output_type": "stream",
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]
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|
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"output_type": "stream",
|
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"text": [
|
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"precision: 0.8728230645397337, recall: 0.6949429037520392, f1: 0.7737917612714889, val accuracy: 0.9498824087072251\n",
|
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"Train accuracy: 0.9993212339874997\n"
|
||||
]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
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"output_type": "stream",
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"text": [
|
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"precision: 0.8691318792431028, recall: 0.7011186203682125, f1: 0.7761367300870687, val accuracy: 0.9505934258263296\n",
|
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"Train accuracy: 0.9996310063958891\n"
|
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]
|
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},
|
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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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]
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},
|
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{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"precision: 0.8701146047605054, recall: 0.6900489396411092, f1: 0.7696906680530282, val accuracy: 0.949116697963574\n",
|
||||
"Train accuracy: 0.9997540042639261\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"NUM_EPOCHS = 10\n",
|
||||
"for i in range(NUM_EPOCHS):\n",
|
||||
" model.train()\n",
|
||||
" train_true = []\n",
|
||||
" train_pred = []\n",
|
||||
" for i in tqdm(range(len(train_set['labels']))):\n",
|
||||
" batch_tokens = train_tokens_ids[i].unsqueeze(0)\n",
|
||||
" tags = train_labels_ids[i].unsqueeze(1)\n",
|
||||
" train_true += tags\n",
|
||||
"\n",
|
||||
" Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
|
||||
" Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
|
||||
" train_pred += list(Y_batch_pred.numpy())\n",
|
||||
"\n",
|
||||
" predicted_tags = model(batch_tokens)\n",
|
||||
"\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))\n",
|
||||
"\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
"\n",
|
||||
" model.eval()\n",
|
||||
" eval_model(val_tokens_ids, val_labels_ids, model)\n",
|
||||
" print(f'Train accuracy: {accuracy_score(train_true, train_pred)}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def save_prediction(test_tokens, test_pred, file_name):\n",
|
||||
" with open(file_name, 'w') as f:\n",
|
||||
" for i in range(len(test_tokens)):\n",
|
||||
" for j in range(len(test_tokens[i])):\n",
|
||||
" print(i, j)\n",
|
||||
" print(test_pred[i][j])\n",
|
||||
" f.write(f'{test_tokens[i][j]}\\t{list(num_tags.keys())[test_pred[i][j]]}\\n')\n",
|
||||
" f.write('\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"test_pred = []\n",
|
||||
"\n",
|
||||
"with torch.no_grad():\n",
|
||||
" for i in range(len(test_tokens_ids)):\n",
|
||||
" batch_tokens = test_tokens_ids[i].unsqueeze(0)\n",
|
||||
"\n",
|
||||
" Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
|
||||
" Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
|
||||
" test_pred += list(Y_batch_pred.numpy())\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 86,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('test-A/out.tsv', 'w') as f:\n",
|
||||
" for i in range(len(test_pred)):\n",
|
||||
" tag = list(num_tags.keys())[test_pred[i]]\n",
|
||||
" f.write(tag)\n",
|
||||
" f.write('\\n')\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "dl",
|
||||
"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": "3.11.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
219516
test-A/out.tsv
Normal file
219516
test-A/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
945
train/train.tsv
Normal file
945
train/train.tsv
Normal file
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user