def main():
# create instance of config
config = Config()
dev = CoNLLDataset(config.filename_dev, config.processing_word,
config.processing_tag, config.max_iter)
train = CoNLLDataset(config.filename_train, config.processing_word,
config.processing_tag, config.max_iter)
test = CoNLLDataset(config.filename_test, config.processing_word,
config.processing_tag, config.max_iter)
predict = CoNLLDataset("data/source_data.txt", config.processing_word,
config.max_iter)
max_sequence_length = max(max([len(seq[0]) for seq in train]),
max([len(seq[0]) for seq in dev]),
max([len(seq[0]) for seq in test]),
max([len(seq[0]) for seq in predict]))
max_word_length = max(
max([len(word[0]) for seq in train for word in seq[0]]),
max([len(word[0]) for seq in test for word in seq[0]]),
max([len(word[0]) for seq in dev for word in seq[0]]))
print(max_word_length, max_sequence_length)
model = NERModel(config, max_word_length, max_sequence_length)
model.build()
model.restore_session(config.dir_model)
model.run_predict(predict)
def main(predict_file,save_file):
# create instance of config
config = Config()
predict=CoNLLDataset(predict_file, config.processing_word, config.max_iter)
max_sequence_length = max([len(seq[0]) for seq in predict])
max_word_length = max([len(word[0]) for seq in predict for word in seq[0]])
print(max_word_length, max_sequence_length)
model = NERModel(config, max_word_length, max_sequence_length)
model.build()
model.restore_session(config.dir_model)
model.run_predict(predict,save_file)
def BacNer(dir_path,save_file_path):
if os.path.exists(save_file_path):
pass
else:
os.mkdir(save_file_path)
file_list=os.listdir(dir_path)
for file in file_list:
file_path=os.path.join(dir_path,file)
save_file=os.path.join(save_file_path,file)
config = Config()
predict = CoNLLDataset(file_path, config.processing_word, config.max_iter)
max_sequence_length = max([len(seq[0]) for seq in predict])
max_word_length = max([len(word[0]) for seq in predict for word in seq[0]])
model = NERModel(config, max_word_length, max_sequence_length)
model.build()
model.restore_session(config.dir_model)
model.run_predict(predict, save_file)
tf.reset_default_graph()
