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