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)