[DOCTEST] Fix the documentation of RoCBert (#20142)

* update part of the doc

* add temp values, fix part of the doc

* add template outputs

* add correct models and outputss

* style

* fixup

src/transformers/models/roc_bert/modeling_roc_bert.py

@@ -52,6 +52,29 @@ _CHECKPOINT_FOR_DOC = "weiweishi/roc-bert-base-zh"
 _CONFIG_FOR_DOC = "RoCBertConfig"
 _TOKENIZER_FOR_DOC = "RoCBertTokenizer"
 
+# Base model docstring
+_EXPECTED_OUTPUT_SHAPE = [1, 8, 768]
+
+# Token Classification output
+_CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "ArthurZ/dummy-rocbert-ner"
+# fmt: off
+_TOKEN_CLASS_EXPECTED_OUTPUT = ["S-EVENT", "S-FAC", "I-ORDINAL", "I-ORDINAL", "E-ORG", "E-LANGUAGE", "E-ORG", "E-ORG", "E-ORG", "E-ORG", "I-EVENT", "S-TIME", "S-TIME", "E-LANGUAGE", "S-TIME", "E-DATE", "I-ORDINAL", "E-QUANTITY", "E-LANGUAGE", "S-TIME", "B-ORDINAL", "S-PRODUCT", "E-LANGUAGE", "E-LANGUAGE", "E-ORG", "E-LOC", "S-TIME", "I-ORDINAL", "S-FAC", "O", "S-GPE", "I-EVENT", "S-GPE", "E-LANGUAGE", "E-ORG", "S-EVENT", "S-FAC", "S-FAC", "S-FAC", "E-ORG", "S-FAC", "E-ORG", "S-GPE"]
+# fmt: on
+_TOKEN_CLASS_EXPECTED_LOSS = 3.62
+
+# SequenceClassification docstring
+_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/dummy-rocbert-seq"
+_SEQ_CLASS_EXPECTED_OUTPUT = "'financial news'"
+_SEQ_CLASS_EXPECTED_LOSS = 2.31
+
+# QuestionAsnwering docstring
+_CHECKPOINT_FOR_QA = "ArthurZ/dummy-rocbert-qa"
+_QA_EXPECTED_OUTPUT = "''"
+_QA_EXPECTED_LOSS = 3.75
+_QA_TARGET_START_INDEX = 14
+_QA_TARGET_END_INDEX = 15
+
+# Maske language modeling
 ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "weiweishi/roc-bert-base-zh",
     # See all RoCBert models at https://huggingface.co/models?filter=roc_bert
@@ -917,6 +940,7 @@ class RoCBertModel(RoCBertPreTrainedModel):
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=BaseModelOutputWithPoolingAndCrossAttentions,
         config_class=_CONFIG_FOR_DOC,
+        expected_output=_EXPECTED_OUTPUT_SHAPE,
     )
     def forward(
         self,
@@ -1146,20 +1170,20 @@ class RoCBertForPreTraining(RoCBertPreTrainedModel):
         >>> model = RoCBertForPreTraining.from_pretrained("weiweishi/roc-bert-base-zh")
 
         >>> inputs = tokenizer("你好，很高兴认识你", return_tensors="pt")
-        >>> attack_inputs = tokenizer("你号，很高兴认识你", return_tensors="pt")
-        >>> attack_keys = list(attack_inputs.keys())
-        >>> for key in attack_keys:
-        ...     attack_inputs[f"attack_{key}"] = attack_inputs.pop(key)
-        >>> label_inputs = tokenizer("你好，很高兴认识你", return_tensors="pt")
-        >>> label_keys = list(attack_inputs.keys())
-        >>> for key in label_keys:
-        ...     label_inputs[f"labels_{key}"] = label_inputs.pop(key)
+        >>> attack_inputs = {}
+        >>> for key in list(inputs.keys()):
+        ...     attack_inputs[f"attack_{key}"] = inputs[key]
+        >>> label_inputs = {}
+        >>> for key in list(inputs.keys()):
+        ...     label_inputs[f"labels_{key}"] = inputs[key]
 
         >>> inputs.update(label_inputs)
         >>> inputs.update(attack_inputs)
         >>> outputs = model(**inputs)
 
         >>> logits = outputs.logits
+        >>> logits.shape
+        torch.Size([1, 11, 21128])
         ```
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
@@ -1271,12 +1295,6 @@ class RoCBertForMaskedLM(RoCBertPreTrainedModel):
         self.cls.predictions.decoder = new_embeddings
 
     @add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MaskedLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def forward(
         self,
         input_ids: Optional[torch.Tensor] = None,
@@ -1299,6 +1317,27 @@ class RoCBertForMaskedLM(RoCBertPreTrainedModel):
             Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
             config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
             loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Example:
+        ```python
+        >>> from transformers import RoCBertTokenizer, RoCBertForMaskedLM
+        >>> import torch
+
+        >>> tokenizer = RoCBertTokenizer.from_pretrained("weiweishi/roc-bert-base-zh")
+        >>> model = RoCBertForMaskedLM.from_pretrained("weiweishi/roc-bert-base-zh")
+
+        >>> inputs = tokenizer("法国是首都[MASK].", return_tensors="pt")
+
+        >>> with torch.no_grad():
+        ...     logits = model(**inputs).logits
+
+        >>> # retrieve index of {mask}
+        >>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
+
+        >>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
+        >>> tokenizer.decode(predicted_token_id)
+        '.'
+        ```
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -1461,7 +1500,8 @@ class RoCBertForCausalLM(RoCBertPreTrainedModel):
         >>> outputs = model(**inputs)
 
         >>> prediction_logits = outputs.logits
-        ```"""
+        ```
+        """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         outputs = self.roc_bert(
@@ -1570,9 +1610,11 @@ class RoCBertForSequenceClassification(RoCBertPreTrainedModel):
     @add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
+        expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
     )
     def forward(
         self,
@@ -1782,9 +1824,11 @@ class RoCBertForTokenClassification(RoCBertPreTrainedModel):
     @add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION,
         output_type=TokenClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT,
+        expected_loss=_TOKEN_CLASS_EXPECTED_LOSS,
     )
     def forward(
         self,
@@ -1865,9 +1909,13 @@ class RoCBertForQuestionAnswering(RoCBertPreTrainedModel):
     @add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_QA,
         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,
     )
     def forward(
         self,