fix #971

pytorch_transformers/modeling_bert.py

@@ -603,7 +603,7 @@ BERT_INPUTS_DOCSTRING = r"""
             :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             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)``:
             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``

pytorch_transformers/modeling_gpt2.py

@@ -393,7 +393,7 @@ GPT2_INPUTS_DOCSTRING = r"""    Inputs:
             :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             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)``:
             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.
@@ -627,7 +627,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
             Selected in the range ``[0, input_ids.size(-1) - 1[``.
         **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.
-            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)``:
             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.

pytorch_transformers/modeling_openai.py

@@ -407,7 +407,7 @@ OPENAI_GPT_INPUTS_DOCSTRING = r"""    Inputs:
             :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             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)``:
             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.
@@ -617,7 +617,7 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
             Selected in the range ``[0, input_ids.size(-1) - 1[``.
         **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.
-            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)``:
             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.

pytorch_transformers/modeling_xlm.py

@@ -427,7 +427,7 @@ XLM_INPUTS_DOCSTRING = r"""
             :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
             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)``:
             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.