484 KiB
484 KiB
# !unzip -q /content/en-ner-conll-2003.zip -d /content/!pip uninstall torch
!pip install torch!pip install transformersRequirement already satisfied: transformers in c:\users\pawel\anaconda3\lib\site-packages (4.32.1) Requirement already satisfied: filelock in c:\users\pawel\appdata\roaming\python\python311\site-packages (from transformers) (3.14.0) Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in c:\users\pawel\anaconda3\lib\site-packages (from transformers) (0.15.1) Requirement already satisfied: numpy>=1.17 in c:\users\pawel\appdata\roaming\python\python311\site-packages (from transformers) (1.26.4) Requirement already satisfied: packaging>=20.0 in c:\users\pawel\anaconda3\lib\site-packages (from transformers) (23.1) Requirement already satisfied: pyyaml>=5.1 in c:\users\pawel\anaconda3\lib\site-packages (from transformers) (6.0) Requirement already satisfied: regex!=2019.12.17 in c:\users\pawel\anaconda3\lib\site-packages (from transformers) (2022.7.9) Requirement already satisfied: requests in c:\users\pawel\appdata\roaming\python\python311\site-packages (from transformers) (2.32.2) Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in c:\users\pawel\anaconda3\lib\site-packages (from transformers) (0.13.2) Requirement already satisfied: safetensors>=0.3.1 in c:\users\pawel\anaconda3\lib\site-packages (from transformers) (0.3.2) Requirement already satisfied: tqdm>=4.27 in c:\users\pawel\appdata\roaming\python\python311\site-packages (from transformers) (4.66.4) Requirement already satisfied: fsspec in c:\users\pawel\appdata\roaming\python\python311\site-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2024.5.0) Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\users\pawel\appdata\roaming\python\python311\site-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.12.0) Requirement already satisfied: colorama in c:\users\pawel\appdata\roaming\python\python311\site-packages (from tqdm>=4.27->transformers) (0.4.6) Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\pawel\appdata\roaming\python\python311\site-packages (from requests->transformers) (3.3.2) Requirement already satisfied: idna<4,>=2.5 in c:\users\pawel\appdata\roaming\python\python311\site-packages (from requests->transformers) (3.7) Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\pawel\appdata\roaming\python\python311\site-packages (from requests->transformers) (2.2.1) Requirement already satisfied: certifi>=2017.4.17 in c:\users\pawel\appdata\roaming\python\python311\site-packages (from requests->transformers) (2024.2.2)
import os
import pandas as pd
import gensim
from gensim.models import KeyedVectors
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import matplotlib.pyplot as plt
from keras.regularizers import l2
from collections import Counter
from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer[1;31m---------------------------------------------------------------------------[0m [1;31mOSError[0m Traceback (most recent call last) File [1;32m~\anaconda3\Lib\site-packages\transformers\utils\import_utils.py:1130[0m, in [0;36m_LazyModule._get_module[1;34m(self, module_name)[0m [0;32m 1129[0m [38;5;28;01mtry[39;00m: [1;32m-> 1130[0m [38;5;28;01mreturn[39;00m importlib[38;5;241m.[39mimport_module([38;5;124m"[39m[38;5;124m.[39m[38;5;124m"[39m [38;5;241m+[39m module_name, [38;5;28mself[39m[38;5;241m.[39m[38;5;18m__name__[39m) [0;32m 1131[0m [38;5;28;01mexcept[39;00m [38;5;167;01mException[39;00m [38;5;28;01mas[39;00m e: File [1;32m~\anaconda3\Lib\importlib\__init__.py:126[0m, in [0;36mimport_module[1;34m(name, package)[0m [0;32m 125[0m level [38;5;241m+[39m[38;5;241m=[39m [38;5;241m1[39m [1;32m--> 126[0m [38;5;28;01mreturn[39;00m _bootstrap[38;5;241m.[39m_gcd_import(name[level:], package, level) File [1;32m<frozen importlib._bootstrap>:1204[0m, in [0;36m_gcd_import[1;34m(name, package, level)[0m File [1;32m<frozen importlib._bootstrap>:1176[0m, in [0;36m_find_and_load[1;34m(name, import_)[0m File [1;32m<frozen importlib._bootstrap>:1147[0m, in [0;36m_find_and_load_unlocked[1;34m(name, import_)[0m File [1;32m<frozen importlib._bootstrap>:690[0m, in [0;36m_load_unlocked[1;34m(spec)[0m File [1;32m<frozen importlib._bootstrap_external>:940[0m, in [0;36mexec_module[1;34m(self, module)[0m File [1;32m<frozen importlib._bootstrap>:241[0m, in [0;36m_call_with_frames_removed[1;34m(f, *args, **kwds)[0m File [1;32m~\anaconda3\Lib\site-packages\transformers\pipelines\__init__.py:28[0m [0;32m 27[0m [38;5;28;01mfrom[39;00m [38;5;21;01m.[39;00m[38;5;21;01m.[39;00m[38;5;21;01mfeature_extraction_utils[39;00m [38;5;28;01mimport[39;00m PreTrainedFeatureExtractor [1;32m---> 28[0m [38;5;28;01mfrom[39;00m [38;5;21;01m.[39;00m[38;5;21;01m.[39;00m[38;5;21;01mimage_processing_utils[39;00m [38;5;28;01mimport[39;00m BaseImageProcessor [0;32m 29[0m [38;5;28;01mfrom[39;00m [38;5;21;01m.[39;00m[38;5;21;01m.[39;00m[38;5;21;01mmodels[39;00m[38;5;21;01m.[39;00m[38;5;21;01mauto[39;00m[38;5;21;01m.[39;00m[38;5;21;01mconfiguration_auto[39;00m [38;5;28;01mimport[39;00m AutoConfig File [1;32m~\anaconda3\Lib\site-packages\transformers\image_processing_utils.py:28[0m [0;32m 27[0m [38;5;28;01mfrom[39;00m [38;5;21;01m.[39;00m[38;5;21;01mfeature_extraction_utils[39;00m [38;5;28;01mimport[39;00m BatchFeature [38;5;28;01mas[39;00m BaseBatchFeature [1;32m---> 28[0m [38;5;28;01mfrom[39;00m [38;5;21;01m.[39;00m[38;5;21;01mimage_transforms[39;00m [38;5;28;01mimport[39;00m center_crop, normalize, rescale [0;32m 29[0m [38;5;28;01mfrom[39;00m [38;5;21;01m.[39;00m[38;5;21;01mimage_utils[39;00m [38;5;28;01mimport[39;00m ChannelDimension File [1;32m~\anaconda3\Lib\site-packages\transformers\image_transforms.py:44[0m [0;32m 43[0m [38;5;28;01mif[39;00m is_torch_available(): [1;32m---> 44[0m [38;5;28;01mimport[39;00m [38;5;21;01mtorch[39;00m [0;32m 46[0m [38;5;28;01mif[39;00m is_tf_available(): File [1;32m~\AppData\Roaming\Python\Python311\site-packages\torch\__init__.py:141[0m [0;32m 140[0m err[38;5;241m.[39mstrerror [38;5;241m+[39m[38;5;241m=[39m [38;5;124mf[39m[38;5;124m'[39m[38;5;124m Error loading [39m[38;5;124m"[39m[38;5;132;01m{[39;00mdll[38;5;132;01m}[39;00m[38;5;124m"[39m[38;5;124m or one of its dependencies.[39m[38;5;124m'[39m [1;32m--> 141[0m [38;5;28;01mraise[39;00m err [0;32m 143[0m kernel32[38;5;241m.[39mSetErrorMode(prev_error_mode) [1;31mOSError[0m: [WinError 126] Nie można odnaleźć określonego modułu. Error loading "C:\Users\Pawel\AppData\Roaming\Python\Python311\site-packages\torch\lib\shm.dll" or one of its dependencies. The above exception was the direct cause of the following exception: [1;31mRuntimeError[0m Traceback (most recent call last) Cell [1;32mIn[6], line 11[0m [0;32m 9[0m [38;5;28;01mfrom[39;00m [38;5;21;01mkeras[39;00m[38;5;21;01m.[39;00m[38;5;21;01mregularizers[39;00m [38;5;28;01mimport[39;00m l2 [0;32m 10[0m [38;5;28;01mfrom[39;00m [38;5;21;01mcollections[39;00m [38;5;28;01mimport[39;00m Counter [1;32m---> 11[0m [38;5;28;01mfrom[39;00m [38;5;21;01mtransformers[39;00m [38;5;28;01mimport[39;00m pipeline, AutoModelForTokenClassification, AutoTokenizer File [1;32m<frozen importlib._bootstrap>:1229[0m, in [0;36m_handle_fromlist[1;34m(module, fromlist, import_, recursive)[0m File [1;32m~\anaconda3\Lib\site-packages\transformers\utils\import_utils.py:1120[0m, in [0;36m_LazyModule.__getattr__[1;34m(self, name)[0m [0;32m 1118[0m value [38;5;241m=[39m [38;5;28mself[39m[38;5;241m.[39m_get_module(name) [0;32m 1119[0m [38;5;28;01melif[39;00m name [38;5;129;01min[39;00m [38;5;28mself[39m[38;5;241m.[39m_class_to_module[38;5;241m.[39mkeys(): [1;32m-> 1120[0m module [38;5;241m=[39m [38;5;28mself[39m[38;5;241m.[39m_get_module([38;5;28mself[39m[38;5;241m.[39m_class_to_module[name]) [0;32m 1121[0m value [38;5;241m=[39m [38;5;28mgetattr[39m(module, name) [0;32m 1122[0m [38;5;28;01melse[39;00m: File [1;32m~\anaconda3\Lib\site-packages\transformers\utils\import_utils.py:1132[0m, in [0;36m_LazyModule._get_module[1;34m(self, module_name)[0m [0;32m 1130[0m [38;5;28;01mreturn[39;00m importlib[38;5;241m.[39mimport_module([38;5;124m"[39m[38;5;124m.[39m[38;5;124m"[39m [38;5;241m+[39m module_name, [38;5;28mself[39m[38;5;241m.[39m[38;5;18m__name__[39m) [0;32m 1131[0m [38;5;28;01mexcept[39;00m [38;5;167;01mException[39;00m [38;5;28;01mas[39;00m e: [1;32m-> 1132[0m [38;5;28;01mraise[39;00m [38;5;167;01mRuntimeError[39;00m( [0;32m 1133[0m [38;5;124mf[39m[38;5;124m"[39m[38;5;124mFailed to import [39m[38;5;132;01m{[39;00m[38;5;28mself[39m[38;5;241m.[39m[38;5;18m__name__[39m[38;5;132;01m}[39;00m[38;5;124m.[39m[38;5;132;01m{[39;00mmodule_name[38;5;132;01m}[39;00m[38;5;124m because of the following error (look up to see its[39m[38;5;124m"[39m [0;32m 1134[0m [38;5;124mf[39m[38;5;124m"[39m[38;5;124m traceback):[39m[38;5;130;01m\n[39;00m[38;5;132;01m{[39;00me[38;5;132;01m}[39;00m[38;5;124m"[39m [0;32m 1135[0m ) [38;5;28;01mfrom[39;00m [38;5;21;01me[39;00m [1;31mRuntimeError[0m: Failed to import transformers.pipelines because of the following error (look up to see its traceback): [WinError 126] Nie można odnaleźć określonego modułu. Error loading "C:\Users\Pawel\AppData\Roaming\Python\Python311\site-packages\torch\lib\shm.dll" or one of its dependencies.
Declare path
data_dir_path = 'en-ner-conll-2003'
train_path = os.path.join(data_dir_path, 'train', 'train.tsv')
dev_texts_path = os.path.join(data_dir_path, 'dev-0', 'in.tsv')
dev_labels_path = os.path.join(data_dir_path, 'dev-0', 'expected.tsv')
dev_predicted_path = os.path.join(data_dir_path, 'dev-0', 'out.tsv')
test_texts_path = os.path.join(data_dir_path, 'test-A', 'in.tsv')
test_predicted_path = os.path.join(data_dir_path, 'test-A', 'out.tsv')
word2vec_file_path = 'word2vec_100_3_polish.bin'Load files
train_data = pd.read_csv(train_path, sep='\t', usecols=[0, 1], header=None, names=['label', 'text'])
dev_texts_data = pd.read_csv(dev_texts_path, sep='\t', usecols=[0], header=None, names=['text'])
dev_labels_data = pd.read_csv(dev_labels_path, sep='\t', usecols=[0], header=None, names=['label'])
test_texts_data = pd.read_csv(test_texts_path, sep='\t', usecols=[0], header=None, names=['text'])Do transformer
model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
recognizer = pipeline("ner", model=model, tokenizer=tokenizer)[1;31m---------------------------------------------------------------------------[0m [1;31mNameError[0m Traceback (most recent call last) Cell [1;32mIn[1], line 1[0m [1;32m----> 1[0m model [38;5;241m=[39m AutoModelForTokenClassification[38;5;241m.[39mfrom_pretrained([38;5;124m"[39m[38;5;124mdbmdz/bert-large-cased-finetuned-conll03-english[39m[38;5;124m"[39m) [0;32m 2[0m tokenizer [38;5;241m=[39m AutoTokenizer[38;5;241m.[39mfrom_pretrained([38;5;124m"[39m[38;5;124mgoogle-bert/bert-base-cased[39m[38;5;124m"[39m) [0;32m 3[0m recognizer [38;5;241m=[39m pipeline([38;5;124m"[39m[38;5;124mner[39m[38;5;124m"[39m, model[38;5;241m=[39mmodel, tokenizer[38;5;241m=[39mtokenizer) [1;31mNameError[0m: name 'AutoModelForTokenClassification' is not defined
Build Vocab
def build_vocab(dataset):
counter = Counter()
for document in dataset:
counter.update(document)
return vocab(counter, specials=["<unk>", "<pad>", "<bos>", "<eos>"])train_X = train_data['text'].apply(lambda x: gensim.utils.simple_preprocess(x))
v = build_vocab(train_X)itos = v.get_itos()print(itos)['<unk>', '<pad>', '<bos>', '<eos>', 'eu', 'rejects', 'german', 'call', 'to', 'boycott', 'british', 'lamb', 'peter', 'blackburn', 'brussels', 'the', 'european', 'commission', 'said', 'on', 'thursday', 'it', 'disagreed', 'with', 'advice', 'consumers', 'shun', 'until', 'scientists', 'determine', 'whether', 'mad', 'cow', 'disease', 'can', 'be', 'transmitted', 'sheep', 'germany', 'representative', 'union', 'veterinary', 'committee', 'werner', 'zwingmann', 'wednesday', 'should', 'buy', 'sheepmeat', 'from', 'countries', 'other', 'than', 'britain', 'scientific', 'was', 'clearer', 'we', 'do', 'support', 'any', 'such', 'recommendation', 'because', 'see', 'grounds', 'for', 'chief', 'spokesman', 'nikolaus', 'van', 'der', 'pas', 'told', 'news', 'briefing', 'he', 'further', 'study', 'required', 'and', 'if', 'found', 'that', 'action', 'needed', 'taken', 'by', 'proposal', 'last', 'month', 'farm', 'commissioner', 'franz', 'fischler', 'ban', 'brains', 'spleens', 'spinal', 'cords', 'human', 'animal', 'food', 'chains', 'highly', 'specific', 'precautionary', 'move', 'protect', 'health', 'proposed', 'wide', 'measures', 'after', 'reports', 'france', 'under', 'laboratory', 'conditions', 'could', 'contract', 'bovine', 'spongiform', 'encephalopathy', 'bse', 'but', 'agreed', 'review', 'his', 'standing', 'mational', 'officials', 'questioned', 'justified', 'as', 'there', 'only', 'slight', 'risk', 'spanish', 'minister', 'loyola', 'de', 'palacio', 'had', 'earlier', 'accused', 'at', 'an', 'ministers', 'meeting', 'of', 'causing', 'unjustified', 'alarm', 'through', 'dangerous', 'generalisation', 'backed', 'committees', 'are', 'due', 're', 'examine', 'issue', 'early', 'next', 'make', 'recommendations', 'senior', 'have', 'long', 'been', 'known', 'scrapie', 'brain', 'wasting', 'similar', 'which', 'is', 'believed', 'transferred', 'cattle', 'feed', 'containing', 'waste', 'farmers', 'denied', 'danger', 'their', 'expressed', 'concern', 'government', 'avoid', 'might', 'influence', 'across', 'europe', 'what', 'extremely', 'careful', 'how', 'going', 'take', 'lead', 'welsh', 'national', 'nfu', 'chairman', 'john', 'lloyd', 'jones', 'bbc', 'radio', 'bonn', 'has', 'led', 'efforts', 'public', 'consumer', 'confidence', 'collapsed', 'in', 'march', 'report', 'suggested', 'humans', 'illness', 'eating', 'contaminated', 'beef', 'imported', 'year', 'nearly', 'half', 'total', 'imports', 'brought', 'tonnes', 'mutton', 'some', 'percent', 'overall', 'rare', 'hendrix', 'song', 'draft', 'sells', 'almost', 'london', 'handwritten', 'guitar', 'legend', 'jimi', 'sold', 'auction', 'late', 'musician', 'favourite', 'possessions', 'florida', 'restaurant', 'paid', 'pounds', 'ai', 'no', 'telling', 'penned', 'piece', 'hotel', 'stationery', 'end', 'january', 'concert', 'english', 'city', 'nottingham', 'threw', 'sheet', 'paper', 'into', 'audience', 'where', 'retrieved', 'fan', 'buyers', 'also', 'snapped', 'up', 'items', 'were', 'put', 'former', 'girlfriend', 'kathy', 'etchingham', 'who', 'lived', 'him', 'they', 'included', 'black', 'lacquer', 'mother', 'pearl', 'inlaid', 'box', 'used', 'store', 'drugs', 'anonymous', 'australian', 'purchaser', 'bought', 'guitarist', 'died', 'overdose', 'aged', 'china', 'says', 'taiwan', 'spoils', 'atmosphere', 'talks', 'beijing', 'taipei', 'spoiling', 'resumption', 'strait', 'visit', 'ukraine', 'taiwanese', 'vice', 'president', 'lien', 'chan', 'this', 'week', 'infuriated', 'speaking', 'hours', 'chinese', 'state', 'media', 'time', 'right', 'engage', 'political', 'foreign', 'ministry', 'shen', 'guofang', 'reuters', 'necessary', 'opening', 'disrupted', 'authorities', 'quoted', 'top', 'negotiator', 'tang', 'shubei', 'visiting', 'group', 'rivals', 'hold', 'now', 'two', 'sides', 'hostility', 'overseas', 'edition', 'people', 'daily', 'saying', 'television', 'interview', 'read', 'comments', 'gave', 'details', 'why', 'considered', 'considers', 'renegade', 'province', 'opposed', 'all', 'gain', 'greater', 'international', 'recognition', 'rival', 'island', 'practical', 'steps', 'towards', 'goal', 'consultations', 'held', 'set', 'format', 'official', 'xinhua', 'agency', 'executive', 'association', 'relations', 'straits', 'july', 'car', 'registrations', 'pct', 'yr', 'frankfurt', 'first', 'motor', 'vehicles', 'jumped', 'period', 'federal', 'office', 'new', 'cars', 'registered', 'passenger', 'trucks', 'figures', 'represent', 'increase', 'decline', 'bike', 'registration', 'rose', 'growth', 'partly', 'increased', 'number', 'germans', 'buying', 'abroad', 'while', 'manufacturers', 'domestic', 'demand', 'weak', 'posted', 'gains', 'numbers', 'volkswagen', 'ag', 'won', 'slightly', 'more', 'quarter', 'opel', 'together', 'general', 'motors', 'came', 'second', 'place', 'figure', 'third', 'ford', 'or', 'seat', 'porsche', 'fewer', 'compared', 'fell', 'greek', 'socialists', 'give', 'green', 'light', 'pm', 'elections', 'athens', 'socialist', 'party', 'bureau', 'prime', 'costas', 'simitis', 'snap', 'its', 'secretary', 'skandalidis', 'reporters', 'announcement', 'cabinet', 'later', 'dimitris', 'kontogiannis', 'newsroom', 'bayervb', 'sets', 'million', 'six', 'bond', 'following', 'announced', 'manager', 'toronto', 'dominion', 'borrower', 'bayerische', 'vereinsbank', 'amt', 'mln', 'coupon', 'maturity', 'sep', 'type', 'straight', 'iss', 'price', 'pay', 'date', 'full', 'fees', 'reoffer', 'spread', 'bp', 'moody', 'aa', 'listing', 'lux', 'freq', 'denoms', 'sale', 'limits', 'us', 'uk', 'ca', 'neg', 'plg', 'crs', 'deflt', 'force', 'maj', 'gov', 'law', 'home', 'ctry', 'tax', 'provs', 'standard', 'mgt', 'und', 'sell', 'conc', 'praecip', 'underlying', 'govt', 'sept', 'notes', 'joint', 'venantius', 'frn', 'floating', 'rate', 'lehman', 'brothers', 'ab', 'swedish', 'mortgage', 'jan', 'base', 'libor', 'short', 'jp', 'fr', 'yes', 'ipma', 'sweden', 'issued', 'off', 'emtn', 'programme', 'port', 'update', 'syria', 'lloyds', 'shipping', 'intelligence', 'service', 'lattakia', 'aug', 'waiting', 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'superiority', 'goulnara', 'fatkoullina', 'snatching', 'lapped', 'ingrid', 'haringa', 'lancien', 'tatiana', 'stiajkina', 'arndt', 'vikstedt', 'boyden', 'godras', 'kombouare', 'abderdeen', 'aitken', 'affection', 'ginola', 'influential', 'influenced', 'imola', 'doohan', 'bayle', 'norifumi', 'abe', 'luca', 'cadalora', 'criville', 'tadayuki', 'okada', 'checa', 'shinichi', 'itoh', 'swe', 'bradley', 'hughes']
v.set_default_index(v["<unk>"])Preprocess data
reformat_ner_dict = {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}def fit_data_Y(column):
dt = [
[reformat_ner_dict[token] for token in row.split()] for row in column
]
return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]
def fit_data_X(dt):
return [
torch.tensor(
[v["<bos>"]] + [v[token] for token in document.split()] + [v["<eos>"]],
dtype=torch.long,
)
for document in dt
]
train_X = fit_data_X(train_data['text'])
train_Y = fit_data_Y(train_data['label'])
dev_X = fit_data_X(dev_texts_data['text'])
dev_Y = fit_data_Y(dev_labels_data['label'])
test_X = fit_data_X(test_texts_data['text'])Create model
num_tags = len(reformat_ner_dict)class LSTM(torch.nn.Module):
def __init__(self):
super(LSTM, self).__init__()
self.emb = torch.nn.Embedding(len(v.get_itos()), 100)
self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)
self.fc1 = torch.nn.Linear(256, num_tags)
def forward(self, x):
emb = torch.relu(self.emb(x))
lstm_output, (h_n, c_n) = self.rec(emb)
out_weights = self.fc1(lstm_output)
return out_weightslstm = LSTM()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(lstm.parameters())Evaluation model
def get_accuracy(y_true, y_pred):
hit = 0
missed = 0
for p, t in zip(y_pred, y_true):
if p == t:
hit += 1
else:
missed += 1
accuracy = hit / (hit + missed)
return accuracydef eval_model(dataset_tokens, dataset_labels, model):
Y_true = []
Y_pred = []
for i in tqdm(range(len(dataset_labels))):
batch_tokens = dataset_tokens[i].unsqueeze(0)
tags = list(dataset_labels[i].numpy())
Y_true += tags
Y_batch_pred_weights = model(batch_tokens).squeeze(0)
Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)
Y_pred += list(Y_batch_pred.numpy())
return get_accuracy(Y_true, Y_pred)Train model
NUM_EPOCHS = 10
dev_accuracy_history = list()
for i in range(NUM_EPOCHS):
lstm.train()
for i in tqdm(range(len(train_Y))):
batch_tokens = train_X[i].unsqueeze(0)
tags = train_Y[i].unsqueeze(1)
predicted_tags = lstm(batch_tokens)
optimizer.zero_grad()
loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))
loss.backward()
optimizer.step()
lstm.eval()
dev_accuracy = eval_model(dev_X, dev_Y, lstm)
dev_accuracy_history.append(dev_accuracy)
print("dev_accuracy:", dev_accuracy)
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dev_accuracy: 0.8407321653205958
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dev_accuracy: 0.8405498532387742
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dev_accuracy: 0.8447247999124902
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dev_accuracy: 0.8469672385188967
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dev_accuracy: 0.8466208455634355
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dev_accuracy: 0.8464932271061604
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dev_accuracy: 0.8453446609906838
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dev_accuracy: 0.8430657599679131
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dev_accuracy: 0.837796940803267
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dev_accuracy: 0.8345517857468414
plt.plot(dev_accuracy_history, label='Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.title('Model Accuracy Over Epochs')
plt.legend()
plt.show()Predict and save results
reversed_ner_dict = {v: k for k, v in reformat_ner_dict.items()}
print(reversed_ner_dict){0: 'O', 1: 'B-PER', 2: 'I-PER', 3: 'B-ORG', 4: 'I-ORG', 5: 'B-LOC', 6: 'I-LOC', 7: 'B-MISC', 8: 'I-MISC'}
def predict_and_save(X, filename):
Y_predicted = []
for i in tqdm(range(len(X))):
batch_tokens = X[i].unsqueeze(0)
Y_batch_pred_weights = lstm(batch_tokens).squeeze(0)
Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)
Y_processed_pred = " ".join(reversed_ner_dict[item] for item in Y_batch_pred.numpy()[1:-1])
Y_predicted.append(Y_processed_pred)
Y_predicted_df = pd.DataFrame(Y_predicted, columns=['predicted_label'])
Y_predicted_df.to_csv(filename, sep='\t', index=False, header=None)dev_predicted = predict_and_save(dev_X, dev_predicted_path)
test_predicted = predict_and_save(test_X, test_predicted_path)0%| | 0/215 [00:00<?, ?it/s]
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