def do_shell(args):
config = Config(args)
helper = ModelHelper.load(args.model_path)
embeddings = load_embeddings(args, helper)
config.embed_size = embeddings.shape[1]
with tf.Graph().as_default():
logger.info("Building model...",)
start = time.time()
model = NERModel(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)
while True:
# Create simple REPL
try:
sentence = eval(input("input> "))
tokens = sentence.strip().split(" ")
for sentence, _, predictions in model.output(session, [(tokens, ["O"] * len(tokens))]):
predictions = [LBLS[l] for l in predictions]
print_sentence(sys.stdout, sentence, [
""] * len(tokens), predictions)
except EOFError:
print("Closing session.")
break
def do_train(args):
# Set up some parameters.
config = Config(args)
helper, train, dev, train_raw, dev_raw = load_and_preprocess_data(args)
embeddings = load_embeddings(args, helper)
config.embed_size = embeddings.shape[1]
helper.save(config.output_path)
handler = logging.FileHandler(config.log_output)
handler.setLevel(logging.DEBUG)
handler.setFormatter(logging.Formatter(
'%(asctime)s:%(levelname)s: %(message)s'))
logging.getLogger().addHandler(handler)
report = None # Report(Config.eval_output)
with tf.Graph().as_default():
logger.info("Building model...",)
start = time.time()
model = NERModel(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)
model.fit(session, saver, train, dev)
if report:
report.log_output(model.output(session, dev_raw))
report.save()
else:
# Save predictions in a text file.
output = model.output(session, dev_raw)
sentences, labels, predictions = list(zip(*output))
predictions = [[LBLS[l] for l in preds]
for preds in predictions]
output = list(zip(sentences, labels, predictions))
with open(model.config.conll_output, 'w') as f:
write_conll(f, output)
with open(model.config.eval_output, 'w') as f:
for sentence, labels, predictions in output:
print_sentence(f, sentence, labels, predictions)
def do_evaluate(args):
config = Config(args)
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 = NERModel(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)