SUMIN/HuggingFace_transformer
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add tqdm, clean up logging

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thomwolf
2018-11-04 11:07:34 +01:00
parent d4e3cf3520
commit 0b7a20c651
2 changed files with 2 additions and 4 deletions
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2
modeling.py
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@@ -435,7 +435,6 @@ class BertForSequenceClassification(nn.Module):
def init_weights(m):
if isinstance(m, (nn.Linear, nn.Embedding)):
print("Initializing {}".format(m))
# Slight difference here with the TF version which uses truncated_normal
# cf https://github.com/pytorch/pytorch/pull/5617
m.weight.data.normal_(config.initializer_range)
@@ -481,7 +480,6 @@ class BertForQuestionAnswering(nn.Module):
def init_weights(m):
if isinstance(m, (nn.Linear, nn.Embedding)):
print("Initializing {}".format(m))
# Slight difference here with the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
m.weight.data.normal_(config.initializer_range)

4
run_squad.py
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@@ -912,9 +912,9 @@ def main():
model.eval()
all_results = []
logger.info("Start evaulating")
logger.info("Start evaluating")
#for input_ids, input_mask, segment_ids, label_ids, example_index in eval_dataloader:
for input_ids, input_mask, segment_ids, example_index in eval_dataloader:
for input_ids, input_mask, segment_ids, example_index in tqdm(eval_dataloader, descr="Evaluating"):
if len(all_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(all_results)))