{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Podejście softmax z embeddingami na przykładzie NER"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"https://pytorch-crf.readthedocs.io/en/stable/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://www.aclweb.org/anthology/W03-0419.pdf"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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 is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
" warnings.warn(msg)\n"
]
}
],
"source": [
"import numpy as np\n",
"import gensim\n",
"import torch\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"from datasets import load_dataset\n",
"from torchtext.vocab import Vocab\n",
"from collections import Counter\n",
"\n",
"from sklearn.datasets import fetch_20newsgroups\n",
"# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n",
"\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"from tqdm.notebook import tqdm\n",
"\n",
"import torch\n",
"from torchcrf import CRF"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Reusing dataset conll2003 (/home/kuba/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6)\n"
]
}
],
"source": [
"dataset = load_dataset(\"conll2003\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def build_vocab(dataset):\n",
" counter = Counter()\n",
" for document in dataset:\n",
" counter.update(document)\n",
" return Vocab(counter, specials=['', '', '', ''])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"vocab = build_vocab(dataset['train']['tokens'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'],\n",
" ['Peter', 'Blackburn'],\n",
" ['BRUSSELS', '1996-08-22'],\n",
" ['The',\n",
" 'European',\n",
" 'Commission',\n",
" 'said',\n",
" 'on',\n",
" 'Thursday',\n",
" 'it',\n",
" 'disagreed',\n",
" 'with',\n",
" 'German',\n",
" 'advice',\n",
" 'to',\n",
" 'consumers',\n",
" 'to',\n",
" 'shun',\n",
" 'British',\n",
" 'lamb',\n",
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" 'determine',\n",
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" 'be',\n",
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" 'to',\n",
" 'sheep',\n",
" '.'],\n",
" ['Germany',\n",
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" \"n't\",\n",
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" 'health',\n",
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" 'slight',\n",
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" '.'],\n",
" ['Spanish',\n",
" 'Farm',\n",
" 'Minister',\n",
" 'Loyola',\n",
" 'de',\n",
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" 'scrapie',\n",
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" 'brain-wasting',\n",
" 'disease',\n",
" 'similar',\n",
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" 'transferred',\n",
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" 'through',\n",
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" 'waste',\n",
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" \"'s\",\n",
" '1,000',\n",
" 'metres',\n",
" 'and',\n",
" 'the',\n",
" 'men',\n",
" \"'s\",\n",
" '3,000',\n",
" 'metres',\n",
" '.'],\n",
" ['GOLF', '-', 'GERMAN', 'OPEN', 'FIRST', 'ROUND', 'SCORES', '.'],\n",
" ['STUTTGART', ',', 'Germany', '1996-08-22'],\n",
" ['Leading', 'first', 'round'],\n",
" ['scores',\n",
" 'in',\n",
" 'the',\n",
" 'German',\n",
" 'Open',\n",
" 'golf',\n",
" 'championship',\n",
" 'on',\n",
" 'Thursday',\n",
" '(',\n",
" 'Britain'],\n",
" ['unless', 'stated', ')', ':'],\n",
" ['62', 'Paul', 'Broadhurst'],\n",
" ['63', 'Raymond', 'Russell'],\n",
" ['64',\n",
" 'David',\n",
" 'J.',\n",
" 'Russell',\n",
" ',',\n",
" 'Michael',\n",
" 'Campbell',\n",
" '(',\n",
" 'New',\n",
" 'Zealand',\n",
" ')',\n",
" ',',\n",
" 'Ian'],\n",
" ['Woosnam',\n",
" ',',\n",
" 'Bernhard',\n",
" 'Langer',\n",
" '(',\n",
" 'Germany',\n",
" ')',\n",
" ',',\n",
" 'Ronan',\n",
" 'Rafferty',\n",
" ',',\n",
" 'Mats'],\n",
" ['Lanner', '(', 'Sweden', ')', ',', 'Wayne', 'Riley', '(', 'Australia', ')'],\n",
" ['65',\n",
" 'Eamonn',\n",
" 'Darcy',\n",
" '(',\n",
" 'Ireland',\n",
" ')',\n",
" ',',\n",
" 'Per',\n",
" 'Nyman',\n",
" '(',\n",
" 'Sweden',\n",
" ')',\n",
" ',',\n",
" 'Russell',\n",
" 'Claydon',\n",
" ','],\n",
" ['Mark',\n",
" 'Roe',\n",
" ',',\n",
" 'Retief',\n",
" 'Goosen',\n",
" '(',\n",
" 'South',\n",
" 'Africa',\n",
" ')',\n",
" ',',\n",
" 'Carl',\n",
" 'Suneson'],\n",
" ['66',\n",
" 'Stephen',\n",
" 'Field',\n",
" ',',\n",
" 'Paul',\n",
" 'Lawrie',\n",
" ',',\n",
" 'Ian',\n",
" 'Pyman',\n",
" ',',\n",
" 'Max',\n",
" 'Anglert'],\n",
" ['(',\n",
" 'Sweden',\n",
" ')',\n",
" ',',\n",
" 'Miles',\n",
" 'Tunnicliff',\n",
" ',',\n",
" 'Christian',\n",
" 'Cevaer',\n",
" '(',\n",
" 'France',\n",
" ')',\n",
" ','],\n",
" ['Des',\n",
" 'Smyth',\n",
" '(',\n",
" 'Ireland',\n",
" ')',\n",
" ',',\n",
" 'David',\n",
" 'Carter',\n",
" ',',\n",
" 'Lee',\n",
" 'Westwood',\n",
" ',',\n",
" 'Greg'],\n",
" ['Chalmers',\n",
" '(',\n",
" 'Australia',\n",
" ')',\n",
" ',',\n",
" 'Miguel',\n",
" 'Angel',\n",
" 'Martin',\n",
" '(',\n",
" 'Spain',\n",
" ')',\n",
" ','],\n",
" ['Thomas',\n",
" 'Bjorn',\n",
" '(',\n",
" 'Denmark',\n",
" ')',\n",
" ',',\n",
" 'Fernando',\n",
" 'Roca',\n",
" '(',\n",
" 'Spain',\n",
" ')',\n",
" ',',\n",
" 'Derrick'],\n",
" ['Cooper'],\n",
" ['67',\n",
" 'Jeff',\n",
" 'Hawksworth',\n",
" ',',\n",
" 'Padraig',\n",
" 'Harrington',\n",
" '(',\n",
" 'Ireland',\n",
" ')',\n",
" ',',\n",
" 'Michael'],\n",
" ['Welch',\n",
" ',',\n",
" 'Thomas',\n",
" 'Gogele',\n",
" '(',\n",
" 'Germany',\n",
" ')',\n",
" ',',\n",
" 'Paul',\n",
" 'McGinley',\n",
" '(',\n",
" 'Ireland',\n",
" ')',\n",
" ','],\n",
" ['Gary',\n",
" 'Orr',\n",
" ',',\n",
" 'Jose-Maria',\n",
" 'Canizares',\n",
" '(',\n",
" 'Spain',\n",
" ')',\n",
" ',',\n",
" 'Michael',\n",
" 'Jonzon'],\n",
" ['(',\n",
" 'Sweden',\n",
" ')',\n",
" ',',\n",
" 'Paul',\n",
" 'Eales',\n",
" ',',\n",
" 'David',\n",
" 'Williams',\n",
" ',',\n",
" 'Andrew',\n",
" 'Coltart',\n",
" ','],\n",
" ['Jonathan',\n",
" 'Lomas',\n",
" ',',\n",
" 'Jose',\n",
" 'Rivero',\n",
" '(',\n",
" 'Spain',\n",
" ')',\n",
" ',',\n",
" 'Robert',\n",
" 'Karlsson'],\n",
" ['(',\n",
" 'Sweden',\n",
" ')',\n",
" ',',\n",
" 'Marcus',\n",
" 'Wills',\n",
" ',',\n",
" 'Pedro',\n",
" 'Linhart',\n",
" '(',\n",
" 'Spain',\n",
" ')',\n",
" ',',\n",
" 'Jamie'],\n",
" ['Spence',\n",
" ',',\n",
" 'Terry',\n",
" 'Price',\n",
" '(',\n",
" 'Australia',\n",
" ')',\n",
" ',',\n",
" 'Juan',\n",
" 'Carlos',\n",
" 'Pinero',\n",
" '(',\n",
" 'Spain',\n",
" ')',\n",
" ','],\n",
" ['Mark', 'Mouland'],\n",
" ['SOCCER',\n",
" '-',\n",
" 'UEFA',\n",
" 'REWARDS',\n",
" 'THREE',\n",
" 'COUNTRIES',\n",
" 'FOR',\n",
" 'FAIR',\n",
" 'PLAY',\n",
" '.'],\n",
" ['GENEVA', '1996-08-22'],\n",
" ['Norway',\n",
" ',',\n",
" 'England',\n",
" 'and',\n",
" 'Sweden',\n",
" 'were',\n",
" 'rewarded',\n",
" 'for',\n",
" 'their',\n",
" 'fair',\n",
" 'play',\n",
" 'on',\n",
" 'Thursday',\n",
" 'with',\n",
" 'an',\n",
" 'additional',\n",
" 'place',\n",
" 'in',\n",
" 'the',\n",
" '1997-98',\n",
" 'UEFA',\n",
" 'Cup',\n",
" 'competition',\n",
" '.'],\n",
" ['Norway',\n",
" 'headed',\n",
" 'the',\n",
" 'UEFA',\n",
" 'Fair',\n",
" 'Play',\n",
" 'rankings',\n",
" 'for',\n",
" '1995-96',\n",
" 'with',\n",
" '8.62',\n",
" 'points',\n",
" ',',\n",
" 'ahead',\n",
" 'of',\n",
" 'England',\n",
" 'with',\n",
" '8.61',\n",
" 'and',\n",
" 'Sweden',\n",
" '8.57',\n",
" '.'],\n",
" ['The',\n",
" 'rankings',\n",
" 'are',\n",
" 'based',\n",
" 'on',\n",
" 'a',\n",
" 'formula',\n",
" 'that',\n",
" 'takes',\n",
" 'into',\n",
" 'account',\n",
" 'many',\n",
" 'factors',\n",
" 'including',\n",
" 'red',\n",
" 'and',\n",
" 'yellow',\n",
" 'cards',\n",
" ',',\n",
" 'and',\n",
" 'coaching',\n",
" 'and',\n",
" 'spectators',\n",
" \"'\",\n",
" 'behaviour',\n",
" 'at',\n",
" 'matches',\n",
" 'played',\n",
" 'at',\n",
" 'an',\n",
" 'international',\n",
" 'level',\n",
" 'by',\n",
" 'clubs',\n",
" 'and',\n",
" 'national',\n",
" 'teams',\n",
" '.'],\n",
" ['Only',\n",
" 'the',\n",
" 'top',\n",
" 'three',\n",
" 'countries',\n",
" 'are',\n",
" 'allocated',\n",
" 'additional',\n",
" 'places',\n",
" '.'],\n",
" ['The', 'UEFA', 'Fair', 'Play', 'rankings', 'are', ':', '1', '.'],\n",
" ['Norway', '8.62', 'points'],\n",
" ['2.', 'England', '8.61'],\n",
" ['3.', 'Sweden', '8.57'],\n",
" ['4.', 'Faroe', 'Islands', '8.56'],\n",
" ['5.', 'Wales', '8.54'],\n",
" ['6.', 'Estonia', '8.52'],\n",
" ['7.', 'Ireland', '8.45'],\n",
" ['8.', 'Belarus', '8.39'],\n",
" ['9.', 'Iceland', '8.35'],\n",
" ['10.', 'Netherlands', '8.30'],\n",
" ['10.', 'Denmark', '8.30'],\n",
" ['10.', 'Germany', '8.30'],\n",
" ['13.', 'Scotland', '8.29'],\n",
" ['13.', 'Latvia', '8.29'],\n",
" ['15.', 'Moldova', '8.24'],\n",
" ['16.', 'Yugoslavia', '8.22'],\n",
" ['16.', 'Belgium', '8.22'],\n",
" ['18.', 'Luxembourg', '8.20'],\n",
" ['19.', 'France', '8.18'],\n",
" ['20.', 'Israel', '8.17'],\n",
" ['21.', 'Switzerland', '8.15'],\n",
" ['21.', 'Slovakia', '8.15'],\n",
" ['23.', 'Poland', '8.12'],\n",
" ['23.', 'Portugal', '8.12'],\n",
" ['25.', 'Georgia', '8.10'],\n",
" ['26.', 'Ukraine', '8.09'],\n",
" ['26.', 'Spain', '8.09'],\n",
" ['26.', 'Finland', '8.09'],\n",
" ['29.', 'Macedonia', '8.07'],\n",
" ['30.', 'Lithuania', '8.06'],\n",
" ['31.', 'Austria', '8.05'],\n",
" ['32.', 'Russia', '8.03'],\n",
" ['33.', 'Romania', '8.02'],\n",
" ['33.', 'Turkey', '8.02'],\n",
" ['35.', 'Hungary', '7.98'],\n",
" ['36.', 'Czech', 'Republic', '7.95'],\n",
" ['37.', 'Greece', '7.89'],\n",
" ['37.', 'Northern', 'Ireland', '7.89'],\n",
" ['39.', 'Italy', '7.85'],\n",
" ['40.', 'Cyprus', '7.83'],\n",
" ['41.', 'Armenia', '7.80'],\n",
" ['42.', 'Slovenia', '7.77'],\n",
" ['43.', 'Croatia', '7.75'],\n",
" ['44.', 'Bulgaria', '7.73'],\n",
" ['45.', 'Malta', '7.40'],\n",
" ['CRICKET',\n",
" '-',\n",
" 'POLICE',\n",
" 'COMMANDOS',\n",
" 'ON',\n",
" 'HAND',\n",
" 'FOR',\n",
" 'AUSTRALIANS',\n",
" \"'\",\n",
" 'FIRST',\n",
" 'MATCH',\n",
" '.'],\n",
" ['COLOMBO', '1996-08-22'],\n",
" ['Armed',\n",
" 'police',\n",
" 'commandos',\n",
" 'patrolled',\n",
" 'the',\n",
" 'ground',\n",
" 'when',\n",
" 'Australia',\n",
" 'opened',\n",
" 'their',\n",
" 'short',\n",
" 'tour',\n",
" 'of',\n",
" 'Sri',\n",
" 'Lanka',\n",
" 'with',\n",
" 'a',\n",
" 'five-run',\n",
" 'win',\n",
" 'over',\n",
" 'the',\n",
" 'country',\n",
" \"'s\",\n",
" 'youth',\n",
" 'team',\n",
" 'on',\n",
" 'Thursday',\n",
" '.'],\n",
" ['Australia',\n",
" ',',\n",
" 'in',\n",
" 'Sri',\n",
" 'Lanka',\n",
" 'for',\n",
" 'a',\n",
" 'limited',\n",
" 'overs',\n",
" 'tournament',\n",
" 'which',\n",
" 'also',\n",
" 'includes',\n",
" 'India',\n",
" 'and',\n",
" 'Zimbabwe',\n",
" ',',\n",
" 'have',\n",
" 'been',\n",
" 'promised',\n",
" 'the',\n",
" 'presence',\n",
" 'of',\n",
" 'commandos',\n",
" ',',\n",
" 'sniffer',\n",
" 'dogs',\n",
" 'and',\n",
" 'plainclothes',\n",
" 'policemen',\n",
" 'to',\n",
" 'ensure',\n",
" 'the',\n",
" 'tournament',\n",
" 'is',\n",
" 'trouble-free',\n",
" '.'],\n",
" ['They',\n",
" 'are',\n",
" 'making',\n",
" 'their',\n",
" 'first',\n",
" 'visit',\n",
" 'to',\n",
" 'the',\n",
" 'island',\n",
" 'since',\n",
" 'boycotting',\n",
" 'a',\n",
" 'World',\n",
" 'Cup',\n",
" 'fixture',\n",
" 'in',\n",
" 'February',\n",
" 'because',\n",
" 'of',\n",
" 'fears',\n",
" 'over',\n",
" 'ethnic',\n",
" 'violence',\n",
" '.'],\n",
" ['Australia',\n",
" ',',\n",
" 'batting',\n",
" 'first',\n",
" 'in',\n",
" 'Thursday',\n",
" \"'s\",\n",
" 'the',\n",
" 'warm-up',\n",
" 'match',\n",
" ',',\n",
" 'scored',\n",
" '251',\n",
" 'for',\n",
" 'seven',\n",
" 'from',\n",
" 'their',\n",
" '50',\n",
" 'overs',\n",
" '.'],\n",
" ['Ricky',\n",
" 'Ponting',\n",
" 'led',\n",
" 'the',\n",
" 'way',\n",
" 'with',\n",
" '100',\n",
" 'off',\n",
" '119',\n",
" 'balls',\n",
" 'with',\n",
" 'two',\n",
" 'sixes',\n",
" 'and',\n",
" 'nine',\n",
" 'fours',\n",
" 'before',\n",
" 'retiring',\n",
" '.'],\n",
" ['The', 'youth', 'side', 'replied', 'with', '246', 'for', 'seven', '.'],\n",
" ...]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset['train']['tokens']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23627"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(vocab.itos)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vocab['on']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def data_process(dt):\n",
" return [ torch.tensor([vocab['']] +[vocab[token] for token in document ] + [vocab['']], dtype = torch.long) for document in dt]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def labels_process(dt):\n",
" return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"train_tokens_ids = data_process(dataset['train']['tokens'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"test_tokens_ids = data_process(dataset['test']['tokens'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"validation_tokens_ids = data_process(dataset['validation']['tokens'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"train_labels = labels_process(dataset['train']['ner_tags'])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"validation_labels = labels_process(dataset['validation']['ner_tags'])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"test_labels = labels_process(dataset['test']['ner_tags'])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 2, 966, 22409, 238, 773, 9, 4588, 212, 7686, 4,\n",
" 3])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_tokens_ids[0]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def get_scores(y_true, y_pred):\n",
" acc_score = 0\n",
" tp = 0\n",
" fp = 0\n",
" selected_items = 0\n",
" relevant_items = 0 \n",
"\n",
" for p,t in zip(y_pred, y_true):\n",
" if p == t:\n",
" acc_score +=1\n",
"\n",
" if p > 0 and p == t:\n",
" tp +=1\n",
"\n",
" if p > 0:\n",
" selected_items += 1\n",
"\n",
" if t > 0 :\n",
" relevant_items +=1\n",
"\n",
" \n",
" \n",
" if selected_items == 0:\n",
" precision = 1.0\n",
" else:\n",
" precision = tp / selected_items\n",
" \n",
" \n",
" if relevant_items == 0:\n",
" recall = 1.0\n",
" else:\n",
" recall = tp / relevant_items\n",
" \n",
" \n",
" if precision + recall == 0.0 :\n",
" f1 = 0.0\n",
" else:\n",
" f1 = 2* precision * recall / (precision + recall)\n",
"\n",
" return precision, recall, f1"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"num_tags = max([max(x) for x in dataset['train']['ner_tags'] ]) + 1 "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"class FF(torch.nn.Module):\n",
"\n",
" def __init__(self,):\n",
" super(FF, self).__init__()\n",
" self.emb = torch.nn.Embedding(23627,200)\n",
" self.fc1 = torch.nn.Linear(200,num_tags)\n",
" \n",
"\n",
" def forward(self, x):\n",
" x = self.emb(x)\n",
" x = self.fc1(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"ff = FF()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"model = CRF(num_tags)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"params = list(ff.parameters()) + list(model.parameters())\n",
"\n",
"optimizer = torch.optim.Adam(params)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"def eval_model(dataset_tokens, dataset_labels):\n",
" Y_true = []\n",
" Y_pred = []\n",
" ff.eval()\n",
" model.eval()\n",
" for i in tqdm(range(len(dataset_labels))):\n",
" batch_tokens = dataset_tokens[i]\n",
" tags = list(dataset_labels[i].numpy())\n",
" emissions = ff(batch_tokens).unsqueeze(1)\n",
" Y_pred += model.decode(emissions)[0]\n",
" Y_true += tags\n",
"\n",
" return get_scores(Y_true, Y_pred)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
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"source": [
"for i in range (5):\n",
" ff.train()\n",
" model.train()\n",
" for i in tqdm(range(len(train_labels))):\n",
" batch_tokens = train_tokens_ids[i]\n",
" tags = train_labels[i].unsqueeze(1)\n",
" emissions = ff(batch_tokens).unsqueeze(1)\n",
"\n",
" optimizer.zero_grad()\n",
" loss = -model(emissions,tags )\n",
" loss.backward()\n",
" optimizer.step()\n",
" \n",
" ff.eval()\n",
" model.eval()\n",
" print(eval_model(validation_tokens_ids, validation_labels))"
]
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{
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],
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{
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],
"source": [
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}
],
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