116 lines
4.5 KiB
Python
116 lines
4.5 KiB
Python
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from conllu import parse_incr
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from flair.data import Corpus, Sentence, Token
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from flair.datasets import SentenceDataset
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from flair.embeddings import StackedEmbeddings
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from flair.embeddings import WordEmbeddings
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from flair.embeddings import CharacterEmbeddings
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from flair.embeddings import FlairEmbeddings
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from flair.models import SequenceTagger
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from flair.trainers import ModelTrainer
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import random
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import torch
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import os
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class NLU:
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def __init__(self):
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self.model = None
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def nolabel2o(self, line, i):
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return 'O' if line[i] == 'NoLabel' else line[i]
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def conllu2flair(self, sentences, label=None):
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fsentences = []
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for sentence in sentences:
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fsentence = Sentence()
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for token in sentence:
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ftoken = Token(token['form'])
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if label:
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ftoken.add_tag(label, token[label])
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fsentence.add_token(ftoken)
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fsentences.append(fsentence)
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return SentenceDataset(fsentences)
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def load_model(self, model_path):
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self.model = SequenceTagger.load(model_path)
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def train_model(self, train_path, test_path):
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fields = ['id', 'form', 'frame', 'slot']
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with open(train_path, encoding='utf-8') as trainfile:
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trainset = list(parse_incr(trainfile, fields=fields, field_parsers={'slot': self.nolabel2o}))
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with open(test_path, encoding='utf-8') as testfile:
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testset = list(parse_incr(testfile, fields=fields, field_parsers={'slot': self.nolabel2o}))
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random.seed(42)
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torch.manual_seed(42)
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if torch.cuda.is_available():
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torch.cuda.manual_seed(0)
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torch.cuda.manual_seed_all(0)
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torch.backends.cudnn.enabled = False
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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corpus = Corpus(train=self.conllu2flair(trainset, 'slot'), test=self.conllu2flair(testset, 'slot'))
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tag_dictionary = corpus.make_tag_dictionary(tag_type='slot')
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embedding_types = [
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WordEmbeddings('pl'),
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FlairEmbeddings('pl-forward'),
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FlairEmbeddings('pl-backward'),
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CharacterEmbeddings(),
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]
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embeddings = StackedEmbeddings(embeddings=embedding_types)
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tagger = SequenceTagger(hidden_size=256, embeddings=embeddings,
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tag_dictionary=tag_dictionary,
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tag_type='slot', use_crf=True)
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if not os.path.isdir('slot-model-pl'):
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trainer = ModelTrainer(tagger, corpus)
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trainer.train('slot-model-pl',
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learning_rate=0.1,
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mini_batch_size=32,
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max_epochs=10,
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train_with_dev=True)
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try:
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self.load_model('slot-model-pl/best-model.pt')
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except:
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self.load_model('slot-model-pl/final-model.pt')
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# Tworzenie osobnego pliku z metrykami dla modelu
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log_file = open('slot-model-pl/training.log', encoding='utf-8')
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log_lines = log_file.readlines()
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log_file.close()
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with open('slot-model-pl/training.log', encoding='utf-8') as log_file, open('evaluation.txt', 'w',
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encoding='utf-8') \
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as eval_file:
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for num, line in enumerate(log_file):
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if line == 'Results:\n':
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lines_to_write_start = num
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eval_file.write('*** This evaluation file was generated automatically by the training script ***\n\n')
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for line in log_lines[lines_to_write_start:]:
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eval_file.write(line)
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def predict(self, sentence):
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sentence = sentence.split()
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csentence = [{'form': word} for word in sentence]
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fsentence = self.conllu2flair([csentence])[0]
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self.model.predict(fsentence)
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return [(token, ftoken.get_tag('slot').value) for token, ftoken in zip(sentence, fsentence)]
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# Można przetestować...
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# nlu = NLU()
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# nlu.train_model('train-pl.conllu', 'test-pl.conllu')
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# lub
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# nlu.load_model('slot-model-pl/final-model.pt')
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# print(nlu.predict("Poproszę jeden bilet na film Batman na imię Jan Kowalski"))
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# Zwrócone:
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# [('Poproszę', 'O'), ('jeden', 'O'), ('bilet', 'O'), ('na', 'O'), ('film', 'O'), ('Batman', 'B-movie'),
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# ('na', 'O'), ('imię', 'O'), ('Jan', 'B-name'), ('Kowalski', 'I-name')]
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