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updating classification example

This commit is contained in:
thomwolf 2019-06-18 16:45:52 +02:00
parent 7388c83b60
commit 40dbda6871
1 changed files with 20 additions and 4 deletions
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24
examples/run_classifier.py
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@ -228,10 +228,10 @@ def main():
# Prepare data loader
train_examples = processor.get_train_examples(args.data_dir)
cached_train_features_file = args.data_dir + '_{0}_{1}_{2}'.format(
cached_train_features_file = os.path.join(args.data_dir, 'train_{0}_{1}_{2}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length),
str(task_name))
str(task_name)))
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader)
@ -311,7 +311,7 @@ def main():
input_ids, input_mask, segment_ids, label_ids = batch
# define a new function to compute loss values for both output_modes
logits = model(input_ids, segment_ids, input_mask, labels=None)
logits = model(input_ids, segment_ids, input_mask)
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
@ -380,6 +380,22 @@ def main():
### Evaluation
if args.do_eval:
eval_examples = processor.get_dev_examples(args.data_dir)
cached_train_features_file = os.path.join(args.data_dir, 'dev_{0}_{1}_{2}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length),
str(task_name)))
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader)
except:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving train features into cached file %s", cached_train_features_file)
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
logger.info("***** Running evaluation *****")
@ -414,7 +430,7 @@ def main():
label_ids = label_ids.to(device)
with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask, labels=None)
logits = model(input_ids, segment_ids, input_mask)
# create eval loss and other metric required by the task
if output_mode == "classification":