embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=True)
# 6. initialize trainer
trainer = SequenceTaggerTrainer(tagger, corpus, test_mode=False)
# 7. start training
trainer.train(model_path,
learning_rate=0.1,
mini_batch_size=32,
max_epochs=60,
embeddings_in_memory=True)
tagger.load_parameters(os.path.join(model_path, 'model.bin'),
ctx=mxnet_prefer_gpu())
trainer = SequenceTaggerTrainer(tagger, corpus, test_mode=True)
test_score, test_fp, test_result = trainer.evaluate(
corpus.test,
model_path,
evaluation_method='span-F1',
embeddings_in_memory=True)
print(test_score)
with open(os.path.join(model_path, 'test.log'), 'w') as out:
out.write('%.2f' % test_score)
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
print(tag_dictionary.idx2item)
# 4. initialize embeddings
with mx.Context(mxnet_prefer_gpu()):
embedding_types = [
WordEmbeddings('data/embedding/glove/glove.6B.100d.debug.txt'),
# CharLMEmbeddings('data/model/lm-news-forward'),
# CharLMEmbeddings('data/model/lm-news-backward'),
]
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=False)
# 6. initialize trainer
trainer = SequenceTaggerTrainer(tagger, corpus, test_mode=False)
# 7. start training
trainer.train('data/model/wsj-pos',
learning_rate=0.1,
mini_batch_size=32,
max_epochs=150,
embeddings_in_memory=True)