self.words = words
self.starts = starts
self.ends = ends
self.labels = labels
self.predict_labels = []
if __name__ == '__main__':
# parameters
config_file = 'default.ini'
config = Configurable(config_file)
# model
model = CompanyPredict()
# load data
train_data = read_pkl(config.train_pkl)
dev_data = None
if config.para_dev_file:
dev_data = read_pkl(config.dev_pkl)
test_data = read_pkl(config.test_pkl)
word_list = read_pkl(config.load_feature_voc)
p_label_list, s_label_list = read_pkl(config.load_label_voc)
word_voc = VocabSrc(word_list)
p_label_voc = VocabTgt(p_label_list)
s_label_voc = VocabTgt(s_label_list)
embedding = None
if os.path.isfile(config.embedding_pkl):
embedding = read_pkl(config.embedding_pkl)
parse = argparse.ArgumentParser('Attention Target Classifier')
parse.add_argument('--config_file', type=str, default='default.ini')
parse.add_argument('--thread', type=int, default=1)
parse.add_argument('--use_cuda', action='store_true', default=False)
parse.add_argument('--model', type=str, default='model.742')
args, extra_args = parse.parse_known_args()
config = Configurable(args.config_file, extra_args)
torch.set_num_threads(args.thread)
config.use_cuda = False
if gpu and args.use_cuda:
config.use_cuda = True
print("\nGPU using status: ", config.use_cuda)
# load vocab and model
feature_list = read_pkl(config.load_feature_voc)
label_list = read_pkl(config.load_label_voc)
feature_vec = VocabSrc(feature_list)
label_vec = VocabTgt(label_list)
# model
if config.which_model == 'Vanilla':
model = Vanilla(config, feature_vec.size, config.embed_dim, PAD,
label_vec.size)
elif config.which_model == 'Contextualized':
model = Contextualized(config, feature_vec.size, config.embed_dim, PAD,
label_vec.size)
elif config.which_model == 'ContextualizedGates':
model = ContextualizedGates(config, feature_vec.size, config.embed_dim,
PAD, label_vec.size)
else: