2.8 MiB
2.8 MiB
!unzip -q /content/en-ner-conll-2003.zip -d /content/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 torchtext.vocab import vocab
from tqdm.notebook import tqdm
import torch/usr/local/lib/python3.10/dist-packages/torchtext/vocab/__init__.py:4: UserWarning: /!\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\ Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()` warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG) /usr/local/lib/python3.10/dist-packages/torchtext/utils.py:4: UserWarning: /!\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\ Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()` warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)
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'])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', 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'neves', 'bishops', 'flees', 'passport', 'consular', 'soliciting', 'icac', 'graft', 'bribe', 'scams', 'honduran', 'passports', 'thinks', 'solve', 'woes', 'rajapat', 'warnings', 'pullouts', 'chatichai', 'choonhavan', 'chavalit', 'yongchaiyudh', 'aspiration', 'insincere', 'slowdown', 'switched', 'monarchy', 'yassin', 'ramle', 'fundamentalist', 'wrecking', 'confined', 'freeing', 'skhirat', 'purely', 'vital', 'conlon', 'triumphal', 'abruptly', 'eavesdrop', 'conversations', 'campaigns', 'invaluable', 'captivated', 'worried', 'senator', 'dianne', 'feinstein', 'bump', 'dogged', 'wrongdoing', 'misconduct', 'questionable', 'judgment', 'selecting', 'advisers', 'revert', 'revelled', 'heartland', 'hoarse', 'voice', 'resolute', 'wit', 'stilted', 'glib', 'piper', 'jaffray', 'compiled', 'gen', 'muscular', 'dystrophy', 'technique', 'telethon', 'method', 'muscle', 'degenerates', 'adulthood', 'dystrophin', 'altered', 'minimise', 'susceptibility', 'injected', 'mice', 'bred', 'genes', 'experiment', 'fibers', 'diminishing', 'oilseeds', 'oilseed', 'assn', 'cargill', 'mitsui', 'dreyfus', 'dns', 'ww', 'donation', 'taichung', 'procurement', 'soybeans', 'bunge', 'pnw', 'pl', 'buyer', 'upland', 'sorghum', 'pima', 'poultry', 'fruits', 'gaining', 'quota', 'onic', 'panel', 'cutout', 'offal', 'moines', 'steer', 'uneven', 'goldstein', 'hiring', 'matched', 'heist', 'jewelry', 'showboat', 'capt', 'andrews', 'videotape', 'suitcase', 'resembling', 'wholesaler', 'schein', 'suitcases', 'closet', 'jewellers', 'twenties', 'distract', 'mcgilley', 'description', 'simpson', 'hints', 'financially', 'jury', 'gag', 'prohibits', 'acquittal', 'crying', 'blues', 'erroneous', 'jam', 'distanced', 'buffalo', 'wildly', 'supportive', 'gifts', 'supermarket', 'reprinted', 'editions', 'vitriol', 'journalism', 'dignify', 'surgeon', 'discarding', 'amputated', 'improperly', 'disposed', 'deformed', 'infant', 'prosthesis', 'orthopedic', 'educational', 'freezer', 'spoiled', 'crab', 'trap', 'flesh', 'cop', 'shrub', 'guys', 'bagged', 'checker', 'pines', 'toting', 'earl', 'feugill', 'camouflaged', 'shaggy', 'freeze', 'marines', 'attaching', 'strips', 'burlap', 'disguise', 'staking', 'robberies', 'stakeout', 'dla', 'logistics', 'nbc', 'consultant', 'reshaped', 'ire', 'repositioning', 'gluten', 'meal', 'pickup', 'pellets', 'decatur', 'il', 'cedar', 'rapids', 'ia', 'cereals', 'ecus', 'provisionally', 'optional', 'enjoys', 'subtracting', 'shipyard', 'cherbourg', 'yeard', 'cutback', 'indre', 'managements', 'curtail', 'estates', 'hub', 'gadoon', 'unearthed', 'cages', 'tando', 'allahyar', 'hyderabad', 'recorder', 'sui', 'technologists', 'sugarcane', 'pillaged', 'gujar', 'megawatt', 'mohib', 'textile', 'defaulted', 'leasing', 'modarabas', 'multiply', 'shahid', 'privatisation', 'sindh', 'restraining', 'javedan', 'dadabhoy', 'pvt', 'directions', 'occupying', 'rebelled', 'wrecked', 'mined', 'halo', 'roadsides', 'qotbuddin', 'hilal', 'officiated', 'sprinkling', 'petrol', 'andhra', 'pradesh', 'rarest', 'beings', 'roasted', 'appellants', 'plotted', 'elephant', 'tramples', 'rampaging', 'trampled', 'hari', 'maya', 'poudels', 'madhumalla', 'asleep', 'beast', 'kingdoms', 'elephants', 'overrun', 'kudapokuna', 'welikanda', 'vavuniya', 'undercover', 'vicorp', 'sabourin', 'cfo', 'denver', 'bestop', 'boulder', 'socal', 'edison', 'kilovolt', 'kv', 'wildfire', 'raging', 'conroy', 'crews', 'residues', 'megawatts', 'mw', 'containment', 'arid', 'windy', 'leong', 'munis', 'municipals', 'semiconductor', 'expectation', 'emerge', 'modem', 'networking', 'elias', 'moosa', 'roberston', 'pmc', 'chipsets', 'severance', 'randall', 'soundview', 'focusing', 'develops', 'shrink', 'hambrecht', 'quist', 'slowing', 'housecall', 'sink', 'underperform', 'hospice', 'limitation', 'subsidiaries', 'forma', 'oasis', 'gallagher', 'obscene', 'swore', 'hate', 'supermodel', 'noel', 'laryngitis', 'patsy', 'kensit', 'tickets', 'slough', 'upbeat', 'pretax', 'hammerson', 'warburg', 'stances', 'maintaining', 'rental', 'incomes', 'shaved', 'nigel', 'mobbs', 'birt', 'shrinks', 'seasonally', 'canadians', 'burea', 'shaken', 'backdrop', 'espionage', 'rings', 'jomhuri', 'eslami', 'photographing', 'pan', 'turkism', 'exiles', 'fallahiyan', 'distillates', 'kerosene', 'sort', 'noticeable', 'differential', 'ports', 'exceed', 'cargoes', 'lowest', 'suspicious', 'overhang', 'sulphur', 'manuscript', 'thieves', 'testament', 'akhbar', 'undated', 'pretended', 'wyche', 'invoked', 'recess', 'confirming', 'predecessor', 'mabus', 'shops', 'shutters', 'exporters', 'purchasing', 'destinations', 'southland', 'mainframe', 'delivering', 'constitutes', 'contends', 'nba', 'continually', 'baxter', 'immuno', 'miilion', 'secures', 'cos', 'chipset', 'hotels', 'doubletree', 'toxic', 'compuserve', 'impasse', 'authorised', 'trashed', 'nudge', 'gop', 'reporter', 'beachcomber', 'highlands', 'debris', 'bearing', 'markings', 'conducting', 'fireball', 'wallets', 'shoes', 'mechanical', 'leila', 'corcoran', 'writes', 'enslaved', 'pleads', 'abraham', 'maimed', 'poses', 'volunteers', 'preserved', 'microfilm', 'stowed', 'rarely', 'enlistment', 'discharge', 'certificates', 'unveiling', 'memorial', 'shall', 'oportunity', 'enjoyment', 'sic', 'slavery', 'oppreses', 'liberty', 'outpouring', 'curse', 'separated', 'matron', 'plead', 'dishonourable', 'prisoner', 'thoughtless', 'folly', 'notation', 'regiment', 'pardon', 'anguish', 'amputee', 'terrible', 'haunted', 'posed', 'chested', 'insight', 'rhythms', 'carbondale', 'ulysses', 'writings', 'poetic', 'linguistic', 'treasure', 'trove', 'sociology', 'seared', 'consciousness', 'confederacy', 'retirees', 'budge', 'weidman', 'shepherded', 'predicts', 'databases', 'battlefields', 'discharged', 'incapacity', 'inebetude', 'arise', 'masturbation', 'vow', 'sylvie', 'ajaccio', 'mediterranean', 'defused', 'debre', 'corse', 'lax', 'powerless', 'beauvau', 'nightly', 'figaro', 'shaky', 'racked', 'principally', 'backlash', 'om', 'gruppen', 'omlx', 'pulpex', 'pulp', 'timber', 'recycled', 'complements', 'softs', 'cocoa', 'unpredictable', 'risky', 'hedge', 'tankage', 'naphtha', 'kero', 'throughput', 'bunkering', 'blenkinsop', 'pantomime', 'enacting', 'brandished', 'theatre', 'prop', 'gorleben', 'depot', 'catania', 'sicily', 'slaying', 'nephew', 'puglisi', 'salvatore', 'botta', 'cemetery', 'sicilian', 'hardened', 'tip', 'consciences', 'nitto', 'santapaola', 'knelt', 'ambush', 'cemetary', 'canteen', 'limerick', 'warders', 'takings', 'italians', 'hiv', 'pensioner', 'harassing', 'hookers', 'aids', 'pietro', 'cruising', 'hurling', 'blanks', 'sighted', 'arctic', 'spitzbergen', 'rune', 'ntb', 'vnukovo', 'tupolev', 'longyearbyen', 'airstrip', 'rescuers', 'opera', 'mountainside', 'barentsburg', 'galeforce', 'meteorological', 'cellars', 'trees', 'uprooted', 'roofs', 'trains', 'cm', 'communes', 'rmt', 'turnhout', 'scouts', 'camping', 'meadow', 'hindered', 'excavations', 'jumet', 'lightly', 'tides', 'pesetas', 'chemicals', 'madness', 'overreacted', 'liberte', 'leveilly', 'finns', 'captive', 'tampere', 'luxury', 'ilkka', 'laasonen', 'iltalehti', 'audi', 'ingolstadt', 'demel', 'hedged', 'kekkila', 'fertilisers', 'saplings', 'trend', 'fluctuations', 'untroubled', 'bumble', 'predict', 'echoing', 'automotive', 'equally', 'predicting', 'rationalised', 'destocked', 'destocking', 'relaxed', 'cycle', 'fraction', 'commenting', 'piston', 'kolbenschmidt', 'obstacles', 'huddart', 'jolts', 'zealands', 'waiau', 'hanmer', 'prone', 'earthquakes', 'smooth', 'trevelyan', 'yearly', 'absolutely', 'predicated', 'precepts', 'trough', 'draconian', 'rentals', 'reclamation', 'narrowly', 'downtrend', 'hover', 'touching', 'intraday', 'technically', 'doreen', 'siow', 'siam', 'banker', 'bishi', 'int', 'jcr', 'jbri', 'daewoo', 'dacom', 'commem', 'manufactures', 'ski', 'antara', 'evident', 'happening', 'motivated', 'utomo', 'indonesias', 'extinguished', 'injures', 'exaggerate', 'rents', 'haneda', 'itami', 'contesting', 'stymies', 'dismantle', 'considerable', 'directorate', 'bureaucrats', 'mandarin', 'secretaries', 'src', 'distillation', 'stuck', 'varied', 'exchanger', 'petrolem', 'chevron', 'texaco', 'loxley', 'reviewed', 'jobless', 'inem', 'cur', 'prod', 'adj', 'trln', 'fcast', 'def', 'qtr', 'epa', 'annualised', 'ine', 'variation', 'corresponds', 'relate', 'finances', 'obstructing', 'lasa', 'zabala', 'gal', 'prescriptions', 'gomez', 'liano', 'aznar', 'bch', 'hive', 'coopers', 'lybrand', 'emigrates', 'lenzing', 'viscose', 'fibre', 'preview', 'schillings', 'fibres', 'economies', 'fundamentalists', 'masterminds', 'ideologists', 'financers', 'clarifications', 'clarification', 'amending', 'donna', 'topic', 'pundits', 'debated', 'forgo', 'unflagging', 'sidestepped', 'bentsen', 'viewed', 'distinctively', 'widness', 'timely', 'instances', 'stands', 'expertise', 'faust', 'bailard', 'biehl', 'kaiser', 'awfully', 'greenspan', 'reappointment', 'perelstein', 'mainstay', 'amstetten', 'ewald', 'brenner', 'stieglmair', 'brunmayr', 'marino', 'hartson', 'samokhalova', 'spirited', 'quigley', 'numerical', '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 = 100
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: 0.8253814880312118
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dev_accuracy: 0.8272775336821571
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dev_accuracy: 0.8288636487940055
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dev_accuracy: 0.8296293595376566
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dev_accuracy: 0.8295382034967457
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dev_accuracy: 0.8261289675666806
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dev_accuracy: 0.8233395927148092
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dev_accuracy: 0.8254361816557583
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dev_accuracy: 0.8291188857085559
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dev_accuracy: 0.8282255565076297
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dev_accuracy: 0.8284260997976336
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dev_accuracy: 0.8291735793331024
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dev_accuracy: 0.8304133014894897
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dev_accuracy: 0.8287724927530947
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dev_accuracy: 0.8272228400576106
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dev_accuracy: 0.8259284242766768
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dev_accuracy: 0.8301033709503929
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dev_accuracy: 0.8318900293522452
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dev_accuracy: 0.8309602377349546
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dev_accuracy: 0.8275145393885253
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dev_accuracy: 0.8292829665821954
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dev_accuracy: 0.8277333138867112
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dev_accuracy: 0.8284990246303623
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dev_accuracy: 0.8304315326976719
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dev_accuracy: 0.8267488286448743
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dev_accuracy: 0.8274051521394323
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dev_accuracy: 0.8274233833476144
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dev_accuracy: 0.8282984813403584
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dev_accuracy: 0.8308690816940438
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dev_accuracy: 0.8321634974749776
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dev_accuracy: 0.8278609323439864
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dev_accuracy: 0.8294288162476527
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dev_accuracy: 0.826384204481231
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dev_accuracy: 0.8301945269913037
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dev_accuracy: 0.8305226887385827
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dev_accuracy: 0.8314707115640554
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dev_accuracy: 0.8291006545003737
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dev_accuracy: 0.8289183424185521
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dev_accuracy: 0.8299392900767534
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dev_accuracy: 0.8310513937758656
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dev_accuracy: 0.8308873129022261
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dev_accuracy: 0.8304862263222184
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dev_accuracy: 0.831653023645877
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dev_accuracy: 0.830559151154947
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dev_accuracy: 0.8280250132176259
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dev_accuracy: 0.8291188857085559
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dev_accuracy: 0.831981185393156
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dev_accuracy: 0.8316894860622414
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dev_accuracy: 0.8291371169167381
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dev_accuracy: 0.8292647353740132
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dev_accuracy: 0.8296840531622031
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dev_accuracy: 0.830121602158575
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dev_accuracy: 0.8324551968058923
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dev_accuracy: 0.8317259484786057
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dev_accuracy: 0.8326557400958962
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dev_accuracy: 0.8298481340358426
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dev_accuracy: 0.8332573699659076
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dev_accuracy: 0.8299757524931177
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dev_accuracy: 0.834825253869574
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dev_accuracy: 0.8307596944449509
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dev_accuracy: 0.8290641920840094
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dev_accuracy: 0.8288454175858234
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dev_accuracy: 0.8292647353740132
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dev_accuracy: 0.8293194289985597
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dev_accuracy: 0.8281344004667189
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dev_accuracy: 0.8313977867313267
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dev_accuracy: 0.8272410712657928
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dev_accuracy: 0.8286448742958196
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dev_accuracy: 0.8293011977903776
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dev_accuracy: 0.8289365736267342
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dev_accuracy: 0.8287177991285483
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dev_accuracy: 0.8273504585148858
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dev_accuracy: 0.8285901806712731
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dev_accuracy: 0.8298663652440247
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dev_accuracy: 0.8284078685894514
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dev_accuracy: 0.8279338571767151
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dev_accuracy: 0.830230989407668
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dev_accuracy: 0.8311425498167764
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dev_accuracy: 0.8319082605604273
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dev_accuracy: 0.8324369655977102
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dev_accuracy: 0.8309237753185904
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dev_accuracy: 0.8289912672512807
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dev_accuracy: 0.8305773823631292
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dev_accuracy: 0.8278427011358043
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dev_accuracy: 0.8313613243149623
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dev_accuracy: 0.8255638001130335
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dev_accuracy: 0.8272775336821571
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dev_accuracy: 0.8278427011358043
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dev_accuracy: 0.8237042168784525
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dev_accuracy: 0.8291371169167381
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dev_accuracy: 0.8268764471021495
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dev_accuracy: 0.8277515450948935
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dev_accuracy: 0.8281161692585368
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dev_accuracy: 0.8263659732730488
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dev_accuracy: 0.8299392900767534
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dev_accuracy: 0.8283896373812693
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dev_accuracy: 0.8274416145557966
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dev_accuracy: 0.8283714061730871
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dev_accuracy: 0.8262930484403201
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dev_accuracy: 0.8298116716194782
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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