def main(config):
# load vocabs
vocab_words = load_vocab(config.words_filename)
vocab_tags = load_vocab(config.tags_filename)
vocab_chars = load_vocab(config.chars_filename)
# get processing functions
processing_word = get_processing_word(vocab_words, vocab_chars,
lowercase=True, chars=config.chars)
processing_tag = get_processing_word(vocab_tags,
lowercase=False, allow_unk=False)
# get pre trained embeddings
embeddings = get_trimmed_glove_vectors(config.trimmed_filename)
# create dataset
dev = CoNLLDataset(config.dev_filename, processing_word,
processing_tag, config.max_iter)
test = CoNLLDataset(config.test_filename, processing_word,
processing_tag, config.max_iter)
train = CoNLLDataset(config.train_filename, processing_word,
processing_tag, config.max_iter)
# build model
model = NERModel(config, embeddings, ntags=len(vocab_tags),
nchars=len(vocab_chars))
model.build()
# train, evaluate and interact
model.train(train, dev, vocab_tags)
model.evaluate(test, vocab_tags)
model.interactive_shell(vocab_tags, processing_word)
vocab_tags = load_vocab(config.tags_filename)
vocab_chars = load_vocab(config.chars_filename)
# get processing functions
processing_word = get_processing_word(vocab_words, vocab_chars,
lowercase=True, chars=config.chars)
processing_tag = get_processing_word(vocab_tags,
lowercase=False)
# get pre trained embeddings
embeddings = get_trimmed_glove_vectors(config.trimmed_filename)
# create dataset
dev = CoNLLDataset(config.dev_filename, processing_word,
processing_tag, config.max_iter)
test = CoNLLDataset(config.test_filename, processing_word,
processing_tag, config.max_iter)
train = CoNLLDataset(config.train_filename, processing_word,
processing_tag, config.max_iter)
# build model
model = NERModel(config, embeddings, ntags=len(vocab_tags),
nchars=len(vocab_chars))
model.build()
# train, evaluate and interact
model.train(train, dev, vocab_tags)
model.evaluate(test, vocab_tags)
model.interactive_shell(vocab_tags, processing_word)
# get processing functions
processing_word = get_processing_word(vocab_words,
vocab_chars,
lowercase=True,
chars=config.chars)
processing_tag = get_processing_word(vocab_tags, lowercase=False)
# get pre trained embeddings
embeddings = get_trimmed_glove_vectors(config.trimmed_filename)
# create dataset
dev = CoNLLDataset(config.dev_filename, processing_word, processing_tag,
config.max_iter)
test = CoNLLDataset(config.test_filename, processing_word, processing_tag,
config.max_iter)
train = CoNLLDataset(config.train_filename, processing_word, processing_tag,
config.max_iter)
# build model
model = NERModel(config,
embeddings,
ntags=len(vocab_tags),
nchars=len(vocab_chars))
model.build()
# train, evaluate and interact
model.train(train, dev, vocab_tags)
model.evaluate(test, vocab_tags)
model.interactive_shell(vocab_tags, processing_word)