From da334a57fe8d83e03e37ca4bc9669366403b35ec Mon Sep 17 00:00:00 2001 From: kubapok Date: Wed, 26 May 2021 12:43:49 +0200 Subject: [PATCH] add crf in progress --- cw/10_CRF_ODPOWIEDZI.ipynb | 10970 +++++++++++++++++++++++++++++++++++ 1 file changed, 10970 insertions(+) create mode 100644 cw/10_CRF_ODPOWIEDZI.ipynb diff --git a/cw/10_CRF_ODPOWIEDZI.ipynb b/cw/10_CRF_ODPOWIEDZI.ipynb new file mode 100644 index 0000000..d5ea6c1 --- /dev/null +++ b/cw/10_CRF_ODPOWIEDZI.ipynb @@ -0,0 +1,10970 @@ +{ + "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", + " 'until',\n", + " 'scientists',\n", + " 'determine',\n", + " 'whether',\n", + " 'mad',\n", + " 'cow',\n", + " 'disease',\n", + " 'can',\n", + " 'be',\n", + " 'transmitted',\n", + " 'to',\n", + " 'sheep',\n", + " '.'],\n", + " ['Germany',\n", + " \"'s\",\n", + " 'representative',\n", + " 'to',\n", + " 'the',\n", + " 'European',\n", + " 'Union',\n", + " \"'s\",\n", + " 'veterinary',\n", + " 'committee',\n", + " 'Werner',\n", + " 'Zwingmann',\n", + " 'said',\n", + " 'on',\n", + " 'Wednesday',\n", + " 'consumers',\n", + " 'should',\n", + " 'buy',\n", + " 'sheepmeat',\n", + " 'from',\n", + " 'countries',\n", + " 'other',\n", + " 'than',\n", + " 'Britain',\n", + " 'until',\n", + " 'the',\n", + " 'scientific',\n", + " 'advice',\n", + " 'was',\n", + " 'clearer',\n", + " '.'],\n", + " ['\"',\n", + " 'We',\n", + " 'do',\n", + " \"n't\",\n", + " 'support',\n", + " 'any',\n", + " 'such',\n", + " 'recommendation',\n", + " 'because',\n", + " 'we',\n", + " 'do',\n", + " \"n't\",\n", + " 'see',\n", + " 'any',\n", + " 'grounds',\n", + " 'for',\n", + " 'it',\n", + " ',',\n", + " '\"',\n", + " 'the',\n", + " 'Commission',\n", + " \"'s\",\n", + " 'chief',\n", + " 'spokesman',\n", + " 'Nikolaus',\n", + " 'van',\n", + " 'der',\n", + " 'Pas',\n", + " 'told',\n", + " 'a',\n", + " 'news',\n", + " 'briefing',\n", + " '.'],\n", + " ['He',\n", + " 'said',\n", + " 'further',\n", + " 'scientific',\n", + " 'study',\n", + " 'was',\n", + " 'required',\n", + " 'and',\n", + " 'if',\n", + " 'it',\n", + " 'was',\n", + " 'found',\n", + " 'that',\n", + " 'action',\n", + " 'was',\n", + " 'needed',\n", + " 'it',\n", + " 'should',\n", + " 'be',\n", + " 'taken',\n", + " 'by',\n", + " 'the',\n", + " 'European',\n", + " 'Union',\n", + " '.'],\n", + " ['He',\n", + " 'said',\n", + " 'a',\n", + " 'proposal',\n", + " 'last',\n", + " 'month',\n", + " 'by',\n", + " 'EU',\n", + " 'Farm',\n", + " 'Commissioner',\n", + " 'Franz',\n", + " 'Fischler',\n", + " 'to',\n", + " 'ban',\n", + " 'sheep',\n", + " 'brains',\n", + " ',',\n", + " 'spleens',\n", + " 'and',\n", + " 'spinal',\n", + " 'cords',\n", + " 'from',\n", + " 'the',\n", + " 'human',\n", + " 'and',\n", + " 'animal',\n", + " 'food',\n", + " 'chains',\n", + " 'was',\n", + " 'a',\n", + " 'highly',\n", + " 'specific',\n", + " 'and',\n", + " 'precautionary',\n", + " 'move',\n", + " 'to',\n", + " 'protect',\n", + " 'human',\n", + " 'health',\n", + " '.'],\n", + " ['Fischler',\n", + " 'proposed',\n", + " 'EU-wide',\n", + " 'measures',\n", + " 'after',\n", + " 'reports',\n", + " 'from',\n", + " 'Britain',\n", + " 'and',\n", + " 'France',\n", + " 'that',\n", + " 'under',\n", + " 'laboratory',\n", + " 'conditions',\n", + " 'sheep',\n", + " 'could',\n", + " 'contract',\n", + " 'Bovine',\n", + " 'Spongiform',\n", + " 'Encephalopathy',\n", + " '(',\n", + " 'BSE',\n", + " ')',\n", + " '--',\n", + " 'mad',\n", + " 'cow',\n", + " 'disease',\n", + " '.'],\n", + " ['But',\n", + " 'Fischler',\n", + " 'agreed',\n", + " 'to',\n", + " 'review',\n", + " 'his',\n", + " 'proposal',\n", + " 'after',\n", + " 'the',\n", + " 'EU',\n", + " \"'s\",\n", + " 'standing',\n", + " 'veterinary',\n", + " 'committee',\n", + " ',',\n", + " 'mational',\n", + " 'animal',\n", + " 'health',\n", + " 'officials',\n", + " ',',\n", + " 'questioned',\n", + " 'if',\n", + " 'such',\n", + " 'action',\n", + " 'was',\n", + " 'justified',\n", + " 'as',\n", + " 'there',\n", + " 'was',\n", + " 'only',\n", + " 'a',\n", + " 'slight',\n", + " 'risk',\n", + " 'to',\n", + " 'human',\n", + " 'health',\n", + " '.'],\n", + " ['Spanish',\n", + " 'Farm',\n", + " 'Minister',\n", + " 'Loyola',\n", + " 'de',\n", + " 'Palacio',\n", + " 'had',\n", + " 'earlier',\n", + " 'accused',\n", + " 'Fischler',\n", + " 'at',\n", + " 'an',\n", + " 'EU',\n", + " 'farm',\n", + " 'ministers',\n", + " \"'\",\n", + " 'meeting',\n", + " 'of',\n", + " 'causing',\n", + " 'unjustified',\n", + " 'alarm',\n", + " 'through',\n", + " '\"',\n", + " 'dangerous',\n", + " 'generalisation',\n", + " '.',\n", + " '\"'],\n", + " ['.'],\n", + " ['Only',\n", + " 'France',\n", + " 'and',\n", + " 'Britain',\n", + " 'backed',\n", + " 'Fischler',\n", + " \"'s\",\n", + " 'proposal',\n", + " '.'],\n", + " ['The',\n", + " 'EU',\n", + " \"'s\",\n", + " 'scientific',\n", + " 'veterinary',\n", + " 'and',\n", + " 'multidisciplinary',\n", + " 'committees',\n", + " 'are',\n", + " 'due',\n", + " 'to',\n", + " 're-examine',\n", + " 'the',\n", + " 'issue',\n", + " 'early',\n", + " 'next',\n", + " 'month',\n", + " 'and',\n", + " 'make',\n", + " 'recommendations',\n", + " 'to',\n", + " 'the',\n", + " 'senior',\n", + " 'veterinary',\n", + " 'officials',\n", + " '.'],\n", + " ['Sheep',\n", + " 'have',\n", + " 'long',\n", + " 'been',\n", + " 'known',\n", + " 'to',\n", + " 'contract',\n", + " 'scrapie',\n", + " ',',\n", + " 'a',\n", + " 'brain-wasting',\n", + " 'disease',\n", + " 'similar',\n", + " 'to',\n", + " 'BSE',\n", + " 'which',\n", + " 'is',\n", + " 'believed',\n", + " 'to',\n", + " 'have',\n", + " 'been',\n", + " 'transferred',\n", + " 'to',\n", + " 'cattle',\n", + " 'through',\n", + " 'feed',\n", + " 'containing',\n", + " 'animal',\n", + " 'waste',\n", + " '.'],\n", + " ['British',\n", + " 'farmers',\n", + " 'denied',\n", + " 'on',\n", + " 'Thursday',\n", + " 'there',\n", + " 'was',\n", + " 'any',\n", + " 'danger',\n", + " 'to',\n", + " 'human',\n", + " 'health',\n", + " 'from',\n", + " 'their',\n", + " 'sheep',\n", + " ',',\n", + " 'but',\n", + " 'expressed',\n", + " 'concern',\n", + " 'that',\n", + " 'German',\n", + " 'government',\n", + " 'advice',\n", + " 'to',\n", + " 'consumers',\n", + " 'to',\n", + " 'avoid',\n", + " 'British',\n", + " 'lamb',\n", + " 'might',\n", + " 'influence',\n", + " 'consumers',\n", + " 'across',\n", + " 'Europe',\n", + " '.'],\n", + " ['\"',\n", + " 'What',\n", + " 'we',\n", + " 'have',\n", + " 'to',\n", + " 'be',\n", + " 'extremely',\n", + " 'careful',\n", + " 'of',\n", + " 'is',\n", + " 'how',\n", + " 'other',\n", + " 'countries',\n", + " 'are',\n", + " 'going',\n", + " 'to',\n", + " 'take',\n", + " 'Germany',\n", + " \"'s\",\n", + " 'lead',\n", + " ',',\n", + " '\"',\n", + " 'Welsh',\n", + " 'National',\n", + " 'Farmers',\n", + " \"'\",\n", + " 'Union',\n", + " '(',\n", + " 'NFU',\n", + " ')',\n", + " 'chairman',\n", + " 'John',\n", + " 'Lloyd',\n", + " 'Jones',\n", + " 'said',\n", + " 'on',\n", + " 'BBC',\n", + " 'radio',\n", + " '.'],\n", + " ['Bonn',\n", + " 'has',\n", + " 'led',\n", + " 'efforts',\n", + " 'to',\n", + " 'protect',\n", + " 'public',\n", + " 'health',\n", + " 'after',\n", + " 'consumer',\n", + " 'confidence',\n", + " 'collapsed',\n", + " 'in',\n", + " 'March',\n", + " 'after',\n", + " 'a',\n", + " 'British',\n", + " 'report',\n", + " 'suggested',\n", + " 'humans',\n", + " 'could',\n", + " 'contract',\n", + " 'an',\n", + " 'illness',\n", + " 'similar',\n", + " 'to',\n", + " 'mad',\n", + " 'cow',\n", + " 'disease',\n", + " 'by',\n", + " 'eating',\n", + " 'contaminated',\n", + " 'beef',\n", + " '.'],\n", + " ['Germany',\n", + " 'imported',\n", + " '47,600',\n", + " 'sheep',\n", + " 'from',\n", + " 'Britain',\n", + " 'last',\n", + " 'year',\n", + " ',',\n", + " 'nearly',\n", + " 'half',\n", + " 'of',\n", + " 'total',\n", + " 'imports',\n", + " '.'],\n", + " ['It',\n", + " 'brought',\n", + " 'in',\n", + " '4,275',\n", + " 'tonnes',\n", + " 'of',\n", + " 'British',\n", + " 'mutton',\n", + " ',',\n", + " 'some',\n", + " '10',\n", + " 'percent',\n", + " 'of',\n", + " 'overall',\n", + " 'imports',\n", + " '.'],\n", + " ['Rare',\n", + " 'Hendrix',\n", + " 'song',\n", + " 'draft',\n", + " 'sells',\n", + " 'for',\n", + " 'almost',\n", + " '$',\n", + " '17,000',\n", + " '.'],\n", + " ['LONDON', '1996-08-22'],\n", + " ['A',\n", 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'of',\n", + " 'London',\n", + " 'hotel',\n", + " 'stationery',\n", + " 'in',\n", + " 'late',\n", + " '1966',\n", + " '.'],\n", + " ['At',\n", + " 'the',\n", + " 'end',\n", + " 'of',\n", + " 'a',\n", + " 'January',\n", + " '1967',\n", + " 'concert',\n", + " 'in',\n", + " 'the',\n", + " 'English',\n", + " 'city',\n", + " 'of',\n", + " 'Nottingham',\n", + " 'he',\n", + " 'threw',\n", + " 'the',\n", + " 'sheet',\n", + " 'of',\n", + " 'paper',\n", + " 'into',\n", + " 'the',\n", + " 'audience',\n", + " ',',\n", + " 'where',\n", + " 'it',\n", + " 'was',\n", + " 'retrieved',\n", + " 'by',\n", + " 'a',\n", + " 'fan',\n", + " '.'],\n", + " ['Buyers',\n", + " 'also',\n", + " 'snapped',\n", + " 'up',\n", + " '16',\n", + " 'other',\n", + " 'items',\n", + " 'that',\n", + " 'were',\n", + " 'put',\n", + " 'up',\n", + " 'for',\n", + " 'auction',\n", + " 'by',\n", + " 'Hendrix',\n", + " \"'s\",\n", + " 'former',\n", + " 'girlfriend',\n", + " 'Kathy',\n", + " 'Etchingham',\n", + " ',',\n", + " 'who',\n", + " 'lived',\n", + " 'with',\n", + " 'him',\n", + " 'from',\n", + " '1966',\n", + " 'to',\n", + " '1969',\n", + " '.'],\n", + " ['They',\n", + " 'included',\n", + " 'a',\n", + " 'black',\n", + " 'lacquer',\n", + " 'and',\n", + " 'mother',\n", + " 'of',\n", + " 'pearl',\n", + " 'inlaid',\n", + " 'box',\n", + " 'used',\n", + " 'by',\n", + " 'Hendrix',\n", + " 'to',\n", + " 'store',\n", + " 'his',\n", + " 'drugs',\n", + " ',',\n", + " 'which',\n", + " 'an',\n", + " 'anonymous',\n", + " 'Australian',\n", + " 'purchaser',\n", + " 'bought',\n", + " 'for',\n", + " '5,060',\n", + " 'pounds',\n", + " '(',\n", + " '$',\n", + " '7,845',\n", + " ')',\n", + " '.'],\n", + " ['The',\n", + " 'guitarist',\n", + " 'died',\n", + " 'of',\n", + " 'a',\n", + " 'drugs',\n", + " 'overdose',\n", + " 'in',\n", + " '1970',\n", + " 'aged',\n", + " '27',\n", + " '.'],\n", + " ['China', 'says', 'Taiwan', 'spoils', 'atmosphere', 'for', 'talks', '.'],\n", + " ['BEIJING', '1996-08-22'],\n", + " ['China',\n", + " 'on',\n", + " 'Thursday',\n", + " 'accused',\n", + " 'Taipei',\n", + " 'of',\n", + " 'spoiling',\n", + " 'the',\n", + " 'atmosphere',\n", + " 'for',\n", + " 'a',\n", + " 'resumption',\n", + " 'of',\n", + " 'talks',\n", + " 'across',\n", + " 'the',\n", + " 'Taiwan',\n", + " 'Strait',\n", + " 'with',\n", + " 'a',\n", + " 'visit',\n", + " 'to',\n", + " 'Ukraine',\n", + " 'by',\n", + " 'Taiwanese',\n", + " 'Vice',\n", + " 'President',\n", + " 'Lien',\n", + " 'Chan',\n", + " 'this',\n", + " 'week',\n", + " 'that',\n", + " 'infuriated',\n", + " 'Beijing',\n", + " '.'],\n", + " ['Speaking',\n", + " 'only',\n", + " 'hours',\n", + " 'after',\n", + " 'Chinese',\n", + " 'state',\n", + " 'media',\n", + " 'said',\n", + " 'the',\n", + " 'time',\n", + " 'was',\n", + " 'right',\n", + " 'to',\n", + " 'engage',\n", + " 'in',\n", + " 'political',\n", + " 'talks',\n", + " 'with',\n", + " 'Taiwan',\n", + " ',',\n", + " 'Foreign',\n", + " 'Ministry',\n", + " 'spokesman',\n", + " 'Shen',\n", + " 'Guofang',\n", + " 'told',\n", + " 'Reuters',\n", + " ':',\n", + " '\"',\n", + " 'The',\n", + " 'necessary',\n", + " 'atmosphere',\n", + " 'for',\n", + " 'the',\n", + " 'opening',\n", + " 'of',\n", + " 'the',\n", + " 'talks',\n", + " 'has',\n", + " 'been',\n", + " 'disrupted',\n", + " 'by',\n", + " 'the',\n", + " 'Taiwan',\n", + " 'authorities',\n", + " '.',\n", + " '\"'],\n", + " ['State',\n", + " 'media',\n", + " 'quoted',\n", + " 'China',\n", + " \"'s\",\n", + " 'top',\n", + " 'negotiator',\n", + " 'with',\n", + " 'Taipei',\n", + " ',',\n", + " 'Tang',\n", + " 'Shubei',\n", + " ',',\n", + " 'as',\n", + " 'telling',\n", + " 'a',\n", + " 'visiting',\n", + " 'group',\n", + " 'from',\n", + " 'Taiwan',\n", + " 'on',\n", + " 'Wednesday',\n", + " 'that',\n", + " 'it',\n", + " 'was',\n", + " 'time',\n", + " 'for',\n", + " 'the',\n", + " 'rivals',\n", + " 'to',\n", + " 'hold',\n", + " 'political',\n", + " 'talks',\n", + " '.'],\n", + " ['\"',\n", + " 'Now',\n", + " 'is',\n", + " 'the',\n", + 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['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", + " 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+ " ['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": { + 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"HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "(0.8190561682482688, 0.742415436475648, 0.7788549478690324)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9de21726d2ee45489ce18536c723f29b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=3453.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "(0.6873089700996677, 0.6375739644970414, 0.6615079618852722)\n" + ] + } + ], + "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))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bd2606bae1ef45588bc5034e53045f78", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.8190561682482688, 0.742415436475648, 0.7788549478690324)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_model(validation_tokens_ids, validation_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "35edc00dbc034a65a3223ccfcbc91f6c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=3453.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.6873089700996677, 0.6375739644970414, 0.6615079618852722)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_model(test_tokens_ids, test_labels)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + 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