SUMIN/HuggingFace_transformer
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fixing target size in crossentropy losses

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thomwolf
2018-11-26 09:51:34 +01:00
parent 982339d829
commit 870d71636e
1 changed files with 6 additions and 5 deletions
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11
pytorch_pretrained_bert/modeling.py
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@@ -678,8 +678,8 @@ class BertForPreTraining(PreTrainedBertModel):
if masked_lm_labels is not None and next_sentence_label is not None:
loss_fct = CrossEntropyLoss(ignore_index=-1)
masked_lm_loss = loss_fct(prediction_scores, masked_lm_labels)
next_sentence_loss = loss_fct(seq_relationship_score, next_sentence_label)
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels(-1))
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
total_loss = masked_lm_loss + next_sentence_loss
return total_loss
else:
@@ -741,7 +741,7 @@ class BertForMaskedLM(PreTrainedBertModel):
if masked_lm_labels is not None:
loss_fct = CrossEntropyLoss(ignore_index=-1)
masked_lm_loss = loss_fct(prediction_scores, masked_lm_labels)
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
return masked_lm_loss
else:
return prediction_scores
@@ -803,7 +803,7 @@ class BertForNextSentencePrediction(PreTrainedBertModel):
if next_sentence_label is not None:
loss_fct = CrossEntropyLoss(ignore_index=-1)
next_sentence_loss = loss_fct(seq_relationship_score, next_sentence_label)
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
return next_sentence_loss
else:
return seq_relationship_score
@@ -856,6 +856,7 @@ class BertForSequenceClassification(PreTrainedBertModel):
"""
def __init__(self, config, num_labels=2):
super(BertForSequenceClassification, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, num_labels)
@@ -868,7 +869,7 @@ class BertForSequenceClassification(PreTrainedBertModel):
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits, labels)
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
return loss, logits
else:
return logits