Example #1
0
def do_evaluate(args):
    config = Config(args.model_path)
    helper = ModelHelper.load(args.model_path)
    input_data = read_conll(args.data)
    embeddings = load_embeddings(args, helper)
    config.embed_size = embeddings.shape[1]

    logger.info("Building model...", )
    start = time.time()
    model = WindowModel(helper, config, embeddings)

    logger.info("took %.2f seconds", time.time() - start)

    for sentence, labels, predictions in model.output(input_data):
        predictions = [LBLS[l] for l in predictions]
        print_sentence(args.output, sentence, labels, predictions)
Example #2
0
def do_evaluate(args):
    config = Config(args.model_path)
    helper = ModelHelper.load(args.model_path)
    input_data = read_conll(args.data)
    embeddings = load_embeddings(args, helper)
    config.embed_size = embeddings.shape[1]

    with tf.Graph().as_default():
        logger.info("Building model...",)
        start = time.time()
        model = WindowModel(helper, config, embeddings)

        logger.info("took %.2f seconds", time.time() - start)

        init = tf.global_variables_initializer()
        saver = tf.train.Saver()

        with tf.Session() as session:
            session.run(init)
            saver.restore(session, model.config.model_output)
            for sentence, labels, predictions in model.output(session, input_data):
                predictions = [LBLS[l] for l in predictions]
                print_sentence(args.output, sentence, labels, predictions)
Example #3
0
def do_evaluate(args):
    config = Config(args.model_path)
    helper = ModelHelper.load(args.model_path)
    input_data = read_conll(args.data)
    embeddings = load_embeddings(args, helper)
    config.embed_size = embeddings.shape[1]

    with tf.Graph().as_default():
        logger.info("Building model...", )
        start = time.time()
        model = WindowModel(helper, config, embeddings)

        logger.info("took %.2f seconds", time.time() - start)

        init = tf.global_variables_initializer()
        saver = tf.train.Saver()

        with tf.Session() as session:
            session.run(init)
            saver.restore(session, model.config.model_output)
            for sentence, labels, predictions in model.output(session, input_data):
                predictions = [LBLS[l] for l in predictions]
                print_sentence(args.output, sentence, labels, predictions)