parser.add_argument('--ignoreEntities', action="store_true", default=False, help='tag entities', required=False)
args = vars(parser.parse_args())
print(args)
mode=args["mode"]
tagset_flat=sequence_layered_reader.read_tagset(args["tagFile_flat"])
tagset=sequence_layered_reader.read_tagset(args["tagFile_layered"])
model_file=args["modelFile"]
cache_dir = os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(0))
model = Tagger.from_pretrained('bert-base-cased',
cache_dir=cache_dir, freeze_bert=True, tagset_flat=tagset_flat, tagset=tagset, device=device)
model.to(device)
if mode == "train":
# train_folder_flat=args["trainFolder_flat"]
# dev_folder_flat=args["devFolder_flat"]
train_folder_layered=args["trainFolder_layered"]
dev_folder_layered=args["devFolder_layered"]
# flat_metric=None
# if args["flat_metric"].lower() == "fscore":
# flat_metric=sequence_eval.check_f1_two_lists
# elif args["flat_metric"].lower() == "accuracy":
