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fix #971

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LysandreJik
2019-08-08 10:36:26 -04:00
parent 7729ef7381
commit 7df303f5ad
4 changed files with 6 additions and 6 deletions
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2
pytorch_transformers/modeling_bert.py
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@@ -603,7 +603,7 @@ BERT_INPUTS_DOCSTRING = r"""
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details. :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings. Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``. Selected in the range ``[0, config.max_position_embeddings - 1]``.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Segment token indices to indicate first and second portions of the inputs. Segment token indices to indicate first and second portions of the inputs.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1`` Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``

4
pytorch_transformers/modeling_gpt2.py
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@@ -393,7 +393,7 @@ GPT2_INPUTS_DOCSTRING = r""" Inputs:
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details. :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings. Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``. Selected in the range ``[0, config.max_position_embeddings - 1]``.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs). A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings. The embeddings from these tokens will be summed with the respective token embeddings.
@@ -627,7 +627,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
Selected in the range ``[0, input_ids.size(-1) - 1[``. Selected in the range ``[0, input_ids.size(-1) - 1[``.
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``: **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings. Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``. Selected in the range ``[0, config.max_position_embeddings - 1]``.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``: **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs). A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings. The embeddings from these tokens will be summed with the respective token embeddings.

4
pytorch_transformers/modeling_openai.py
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@@ -407,7 +407,7 @@ OPENAI_GPT_INPUTS_DOCSTRING = r""" Inputs:
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details. :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings. Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``. Selected in the range ``[0, config.max_position_embeddings - 1]``.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs). A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings. The embeddings from these tokens will be summed with the respective token embeddings.
@@ -617,7 +617,7 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
Selected in the range ``[0, input_ids.size(-1) - 1[``. Selected in the range ``[0, input_ids.size(-1) - 1[``.
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``: **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings. Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``. Selected in the range ``[0, config.max_position_embeddings - 1]``.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``: **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs). A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings. The embeddings from these tokens will be summed with the respective token embeddings.

2
pytorch_transformers/modeling_xlm.py
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@@ -427,7 +427,7 @@ XLM_INPUTS_DOCSTRING = r"""
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details. :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings. Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``. Selected in the range ``[0, config.max_position_embeddings - 1]``.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``: **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
A parallel sequence of tokens (can be used to indicate various portions of the inputs). A parallel sequence of tokens (can be used to indicate various portions of the inputs).
The embeddings from these tokens will be summed with the respective token embeddings. The embeddings from these tokens will be summed with the respective token embeddings.