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import os
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import argparse
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from datetime import datetime
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from flair.data import Corpus
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from flair.models import SequenceTagger
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from flair.trainers import ModelTrainer
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from flair.datasets import UniversalDependenciesCorpus
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from flair.embeddings import WordEmbeddings, StackedEmbeddings
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parser = argparse.ArgumentParser(description='Flair Training Part-of-speech tagging')
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parser.add_argument('-output', type=str, default="models/", help='The output directory')
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parser.add_argument('-epochs', type=int, default=1, help='Number of Epochs')
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args = parser.parse_args()
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output = os.path.join(args.output, "UPOS_UD_FRENCH_PLUS_" + str(args.epochs) + "_" + datetime.today().strftime('%Y-%m-%d-%H:%M:%S'))
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print(output)
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corpus: Corpus = UniversalDependenciesCorpus(
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data_folder='UD_FRENCH_PLUS',
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train_file="fr_gsd-ud-train.conllu",
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test_file="fr_gsd-ud-test.conllu",
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dev_file="fr_gsd-ud-dev.conllu",
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)
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tag_type = 'upos'
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tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
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embedding_types = [
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WordEmbeddings('fr'),
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]
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embeddings: StackedEmbeddings = StackedEmbeddings(embeddings=embedding_types)
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tagger: SequenceTagger = SequenceTagger(
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hidden_size=256,
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embeddings=embeddings,
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tag_dictionary=tag_dictionary,
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tag_type=tag_type,
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use_crf=True
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)
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trainer: ModelTrainer = ModelTrainer(tagger, corpus)
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trainer.train(
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output,
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learning_rate=0.1,
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mini_batch_size=128,
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max_epochs=args.epochs
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) |
