46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
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from conllu import parse_incr
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from flair.data import Corpus
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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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from nlu_utils import conllu2flair, nolabel2o
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import random
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import torch
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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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def train_model(label_type, field_parsers = {}):
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with open('data/train_dialog.conllu', encoding='utf-8') as trainfile:
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trainset = list(parse_incr(trainfile, fields=['id', 'form', 'frame', 'slot'], field_parsers=field_parsers))
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corpus = Corpus(train=conllu2flair(trainset, label_type), test=conllu2flair(trainset, label_type))
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label_dictionary = corpus.make_label_dictionary(label_type=label_type)
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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, tag_dictionary=label_dictionary, tag_type=label_type, use_crf=True, tag_format="BIO")
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frame_trainer = ModelTrainer(tagger, corpus)
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frame_trainer.train(f'{label_type}-model', learning_rate=0.1, mini_batch_size=32, max_epochs=75, train_with_dev=False)
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if __name__ == '__main__':
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train_model("frame")
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train_model('slot', field_parsers={'slot': nolabel2o})
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