SUMIN/HuggingFace_transformer
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make examples consistent, revert error in num_train_steps calculation

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This commit is contained in:
Matej Svejda
2019-01-30 11:47:25 +01:00
parent 9c6a48c8c3
commit 5169069997
5 changed files with 21 additions and 17 deletions
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6
examples/run_classifier.py
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@@ -411,7 +411,7 @@ def main():
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
@@ -441,8 +441,8 @@ def main():
num_train_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
num_train_steps = int(
len(train_examples) / args.train_batch_size * args.num_train_epochs)
num_train_steps =
len(train_examples) // args.train_batch_size // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare model
model = BertForSequenceClassification.from_pretrained(args.bert_model,