Fix `OPTForQuestionAnswering` doctest (#19479)

* Fix doc example for OPTForQuestionAnswering
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/opt/modeling_opt.py

@@ -53,12 +53,6 @@ _CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc"
 _SEQ_CLASS_EXPECTED_LOSS = 1.71
 _SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
 
-# QuestionAnswering docstring
-_QA_EXPECTED_OUTPUT = "'a nice puppet'"
-_QA_EXPECTED_LOSS = 7.41
-_QA_TARGET_START_INDEX = 14
-_QA_TARGET_END_INDEX = 15
-
 OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "facebook/opt-125m",
     "facebook/opt-350m",
@@ -1140,16 +1134,7 @@ class OPTForQuestionAnswering(OPTPreTrainedModel):
         self.post_init()
 
     @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        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,
-        expected_output=_QA_EXPECTED_OUTPUT,
-        expected_loss=_QA_EXPECTED_LOSS,
-    )
+    @replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
@@ -1173,7 +1158,36 @@ class OPTForQuestionAnswering(OPTPreTrainedModel):
             Labels for position (index) of the end of the labelled span for computing the token classification loss.
             Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
             are not taken into account for computing the loss.
-        """
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import GPT2Tokenizer, OPTForQuestionAnswering
+        >>> import torch
+
+        >>> torch.manual_seed(4)  # doctest: +IGNORE_RESULT
+        >>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m")
+
+        >>> # note: we are loading a OPTForQuestionAnswering from the hub here,
+        >>> # so the head will be randomly initialized, hence the predictions will be random
+        >>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")
+
+        >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
+
+        >>> inputs = tokenizer(question, text, return_tensors="pt")
+        >>> with torch.no_grad():
+        ...     outputs = model(**inputs)
+
+        >>> answer_start_index = outputs.start_logits.argmax()
+        >>> answer_end_index = outputs.end_logits.argmax()
+
+        >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
+        >>> predicted = tokenizer.decode(predict_answer_tokens)
+        >>> predicted
+        ' Henson?'
+        ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         transformer_outputs = self.model(