fixes

modeling_deberta.py

@@ -35,7 +35,7 @@ from transformers.modeling_outputs import (
 )
 from transformers.modeling_utils import PreTrainedModel
 from transformers.pytorch_utils import softmax_backward_data
-from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
+from transformers.utils import add_code_sample_docstrings, add_start_docstrings, logging
 from .configuration_deberta import DebertaV2Config
 
 
@@ -1418,12 +1418,6 @@ class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
     def set_input_embeddings(self, new_embeddings):
         self.deberta.set_input_embeddings(new_embeddings)
 
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     # Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification.forward with Deberta->DebertaV2
     def forward(
         self,
@@ -1517,12 +1511,6 @@ class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
         # Initialize weights and apply final processing
         self.post_init()
 
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def forward(
         self,
         input_ids: Optional[torch.Tensor] = None,
@@ -1582,14 +1570,6 @@ class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
         # Initialize weights and apply final processing
         self.post_init()
 
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=QuestionAnsweringModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-        qa_target_start_index=_QA_TARGET_START_INDEX,
-        qa_target_end_index=_QA_TARGET_END_INDEX,
-    )
     # Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering.forward with Deberta->DebertaV2
     def forward(
         self,
@@ -1688,12 +1668,6 @@ class DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel):
     def set_input_embeddings(self, new_embeddings):
         self.deberta.set_input_embeddings(new_embeddings)
 
-    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MultipleChoiceModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def forward(
         self,
         input_ids: Optional[torch.Tensor] = None,