def main(args):
# hyper param
root = args.root_dir
assert os.path.exists(root)
tokenizer = AutoTokenizer.from_pretrained(args.model,
cache_dir=args.transformer_cache)
mt_dnn_root = os.path.join(root, args.model)
if not os.path.isdir(mt_dnn_root):
os.makedirs(mt_dnn_root)
task_defs = TaskDefs(args.task_def)
for task in task_defs.get_task_names():
task_def = task_defs.get_task_def(task)
logger.info("Task %s" % task)
for split_name in task_def.split_names:
file_path = os.path.join(root, "%s_%s.tsv" % (task, split_name))
if not os.path.exists(file_path):
logger.warning("File %s doesnot exit")
sys.exit(1)
rows = load_data(file_path, task_def)
dump_path = os.path.join(mt_dnn_root,
"%s_%s.json" % (task, split_name))
logger.info(dump_path)
build_data(
rows,
dump_path,
tokenizer,
task_def.data_type,
lab_dict=task_def.label_vocab,
workers=args.workers,
)
def main(args):
# hyper param
do_lower_case = args.do_lower_case
root = "dl/" + args.root_dir
assert os.path.exists(root)
literal_model_type = args.model.split('-')[0].upper()
encoder_model = EncoderModelType[literal_model_type]
literal_model_type = literal_model_type.lower()
mt_dnn_suffix = literal_model_type
if 'base' in args.model:
mt_dnn_suffix += "_base"
elif 'large' in args.model:
mt_dnn_suffix += "_large"
config_class, model_class, tokenizer_class = MODEL_CLASSES[
literal_model_type]
tokenizer = tokenizer_class.from_pretrained("dl/mt-dnn-models/vocab.txt",
do_lower_case=do_lower_case)
if 'uncased' in args.model:
mt_dnn_suffix = '{}_uncased'.format(mt_dnn_suffix)
else:
mt_dnn_suffix = '{}_cased'.format(mt_dnn_suffix)
if do_lower_case:
mt_dnn_suffix = '{}_lower'.format(mt_dnn_suffix)
mt_dnn_root = os.path.join(root, mt_dnn_suffix)
if not os.path.isdir(mt_dnn_root):
os.mkdir(mt_dnn_root)
task_defs = TaskDefs(args.task_def)
for task in task_defs.get_task_names():
task_def = task_defs.get_task_def(task)
logger.info("Task %s" % task)
for split_name in ['test']:
file_path = os.path.join(root, "%s_%s.tsv" % (task, split_name))
print(file_path)
if not os.path.exists(file_path):
logger.warning("File %s doesnot exit")
sys.exit(1)
rows = load_data(file_path, task_def)
dump_path = os.path.join(mt_dnn_root,
"%s_%s.json" % (task, split_name))
logger.info(dump_path)
build_data(rows,
dump_path,
tokenizer,
task_def.data_type,
encoderModelType=encoder_model,
lab_dict=task_def.label_vocab)
def main(args):
# hyper param
root = args.root_dir
assert os.path.exists(root)
tokenizer = AutoTokenizer.from_pretrained(args.model, mirror='tuna')
mt_dnn_root = os.path.join(root, args.model)
if not os.path.isdir(mt_dnn_root):
os.makedirs(mt_dnn_root)
task_defs = TaskDefs(args.task_def)
for task in task_defs.get_task_names():
task_def = task_defs.get_task_def(task)
logger.info("Task %s" % task)
for split_name in task_def.split_names:
if args.task_type == "clue":
file_path = os.path.join(root, task, f"{split_name}.json")
else:
file_path = os.path.join(root,
"%s_%s.tsv" % (task, split_name))
if not os.path.exists(file_path):
logger.warning("File %s doesnot exit" % file_path)
sys.exit(1)
if args.task_type == "glue":
rows = load_data(file_path, task_def)
elif args.task_type == "clue":
rows = load_clue_data(file_path, task_def)
elif args.task_type == "qianyan":
rows = load_qianyan_data(file_path, task_def)
else:
raise ValueError(f"{args.task_type} not implemented")
dump_path = os.path.join(mt_dnn_root,
"%s_%s.json" % (task, split_name))
logger.info(dump_path)
build_data(rows,
dump_path,
tokenizer,
task_def.data_type,
lab_dict=task_def.label_vocab)
def main(args):
# hyper param
root = args.root_dir
assert os.path.exists(root)
suffix = args.model.split("/")[-1]
literal_model_type = suffix.split("-")[0].upper()
encoder_model = EncoderModelType[literal_model_type]
literal_model_type = literal_model_type.lower()
mt_dnn_suffix = literal_model_type
if "base" in args.model:
mt_dnn_suffix += "_base"
elif "large" in args.model:
mt_dnn_suffix += "_large"
# tokenizer
tokenizer = AutoTokenizer.from_pretrained(
args.model,
cache_dir=args.cache_dir,
use_fast=True,
from_slow=True,
revision=args.model_revision,
)
# Padding side determines if we do (question|context) or (context|question).
pad_on_right = tokenizer.padding_side == "right"
if "uncased" in args.model:
mt_dnn_suffix = "{}_uncased".format(mt_dnn_suffix)
else:
mt_dnn_suffix = "{}_cased".format(mt_dnn_suffix)
mt_dnn_root = os.path.join(root, mt_dnn_suffix)
if not os.path.isdir(mt_dnn_root):
os.mkdir(mt_dnn_root)
task_defs = TaskDefs(args.task_def)
for task in task_defs.get_task_names():
task_def = task_defs.get_task_def(task)
logger.info("Task %s" % task)
for split_name in task_def.split_names:
print(root)
file_path = os.path.join(root, "%s_%s.json" % (task, split_name))
print(file_path)
if not os.path.exists(file_path):
logger.warning("File %s doesnot exit")
sys.exit(1)
logger.warning("processing %s" % file_path)
is_training = True
if not "train" in split_name:
is_training = False
rows = flat_squad(file_path, is_training)
dump_path = os.path.join(mt_dnn_root, "%s_%s.json" % (task, split_name))
logger.info(dump_path)
if is_training:
prepare_train_feature(
tokenizer,
rows,
dump_path,
pad_on_right=pad_on_right,
label_mapper=task_def.label_vocab,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
)
else:
prepare_validation_features(
tokenizer,
rows,
dump_path,
pad_on_right=pad_on_right,
label_mapper=task_def.label_vocab,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
)