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Unverified Commit d2ed8134 authored by IMvision12's avatar IMvision12 Committed by GitHub
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Update modeling_markuplm.py (#19723)

parent 7df0751c
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src/transformers/models/markuplm/modeling_markuplm.py
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...@@ -975,20 +975,20 @@ class MarkupLMForQuestionAnswering(MarkupLMPreTrainedModel): ...@@ -975,20 +975,20 @@ class MarkupLMForQuestionAnswering(MarkupLMPreTrainedModel):
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
xpath_tags_seq=None, xpath_tags_seq: Optional[torch.Tensor] = None,
xpath_subs_seq=None, xpath_subs_seq: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
start_positions=None, start_positions: Optional[torch.Tensor] = None,
end_positions=None, end_positions: Optional[torch.Tensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
r""" r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss. Labels for position (index) of the start of the labelled span for computing the token classification loss.
...@@ -1099,19 +1099,19 @@ class MarkupLMForTokenClassification(MarkupLMPreTrainedModel): ...@@ -1099,19 +1099,19 @@ class MarkupLMForTokenClassification(MarkupLMPreTrainedModel):
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
xpath_tags_seq=None, xpath_tags_seq: Optional[torch.Tensor] = None,
xpath_subs_seq=None, xpath_subs_seq: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
labels=None, labels: Optional[torch.Tensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
...@@ -1206,19 +1206,19 @@ class MarkupLMForSequenceClassification(MarkupLMPreTrainedModel): ...@@ -1206,19 +1206,19 @@ class MarkupLMForSequenceClassification(MarkupLMPreTrainedModel):
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
xpath_tags_seq=None, xpath_tags_seq: Optional[torch.Tensor] = None,
xpath_subs_seq=None, xpath_subs_seq: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
labels=None, labels: Optional[torch.Tensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
......
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