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fix consistency CrossEntropyLoss in modeling_bart (#6265)

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idoh
2020-08-07 12:44:28 +03:00
committed by GitHub
parent c72f9c90a1
commit 3be2d04884
1 changed files with 3 additions and 2 deletions
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src/transformers/modeling_bart.py
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@@ -1040,7 +1040,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
masked_lm_loss = None masked_lm_loss = None
if labels is not None: if labels is not None:
loss_fct = nn.CrossEntropyLoss() loss_fct = CrossEntropyLoss()
# TODO(SS): do we need to ignore pad tokens in labels? # TODO(SS): do we need to ignore pad tokens in labels?
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
@@ -1179,7 +1179,8 @@ class BartForSequenceClassification(PretrainedBartModel):
loss = None loss = None
if labels is not None: if labels is not None:
loss = F.cross_entropy(logits.view(-1, self.config.num_labels), labels.view(-1)) loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
if not return_dict: if not return_dict:
output = (logits,) + outputs[1:] output = (logits,) + outputs[1:]