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fix #827

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thomwolf
2019-07-23 14:06:43 +02:00
parent 2f869dc665
commit 0227b4a940
8 changed files with 42 additions and 42 deletions
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4
pytorch_transformers/modeling_xlm.py
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@@ -436,7 +436,7 @@ XLM_INPUTS_DOCSTRING = r"""
A parallel sequence of tokens to be used to indicate the language of each token in the input.
Indices are selected in the pre-trained language vocabulary,
i.e. in the range ``[0, config.n_langs - 1[``.
**attention_mask**: (`optional`) ``torch.Tensor`` of shape ``(batch_size, sequence_length)``:
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
@@ -449,7 +449,7 @@ XLM_INPUTS_DOCSTRING = r"""
hidden-states (key and values in the attention blocks) as computed by the model
(see `cache` output below). Can be used to speed up sequential decoding.
The dictionary object will be modified in-place during the forward pass to add newly computed hidden-states.
**head_mask**: (`optional`) ``torch.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.