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chenpangpang
transformers
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26bda772
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Commit
26bda772
authored
Aug 27, 2019
by
Nikolay Korolev
Committed by
GitHub
Aug 27, 2019
Browse files
Fix documentation #1117
Rename parameter in documentation + Delete its second occurrence.
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e08c01aa
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pytorch_transformers/modeling_gpt2.py
pytorch_transformers/modeling_gpt2.py
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pytorch_transformers/modeling_gpt2.py
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26bda772
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@@ -656,14 +656,11 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
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@@ -656,14 +656,11 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
Indices are selected in ``[-1, 0, ..., config.vocab_size]``
Indices are selected in ``[-1, 0, ..., config.vocab_size]``
All labels set to ``-1`` are ignored (masked), the loss is only
All labels set to ``-1`` are ignored (masked), the loss is only
computed for labels in ``[0, ..., config.vocab_size]``
computed for labels in ``[0, ..., config.vocab_size]``
**m
ultiple_choice
_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size)``:
**m
c
_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size)``:
Labels for computing the multiple choice classification loss.
Labels for computing the multiple choice classification loss.
Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
of the input tensors. (see `input_ids` above)
of the input tensors. (see `input_ids` above)
`multiple_choice_labels`: optional multiple choice labels: ``torch.LongTensor`` of shape [batch_size]
with indices selected in [0, ..., num_choices].
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**lm_loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
**lm_loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Language modeling loss.
Language modeling loss.
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