[Doctest] added doctest changes for electra (#16675)

* added doctest changes for electra

* fixed doctest tests

* updated changes

src/transformers/models/electra/modeling_electra.py

@@ -967,9 +967,11 @@ class ElectraForSequenceClassification(ElectraPreTrainedModel):
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="bhadresh-savani/electra-base-emotion",
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="'joy'",
+        expected_loss=0.06,
     )
     def forward(
         self,
@@ -1087,16 +1089,25 @@ class ElectraForPreTraining(ElectraPreTrainedModel):
         Examples:
 
         ```python
-        >>> from transformers import ElectraTokenizer, ElectraForPreTraining
+        >>> from transformers import ElectraForPreTraining, ElectraTokenizerFast
         >>> import torch
 
-        >>> tokenizer = ElectraTokenizer.from_pretrained("google/electra-small-discriminator")
-        >>> model = ElectraForPreTraining.from_pretrained("google/electra-small-discriminator")
+        >>> discriminator = ElectraForPreTraining.from_pretrained("google/electra-base-discriminator")
+        >>> tokenizer = ElectraTokenizerFast.from_pretrained("google/electra-base-discriminator")
+
+        >>> sentence = "The quick brown fox jumps over the lazy dog"
+        >>> fake_sentence = "The quick brown fox fake over the lazy dog"
+
+        >>> fake_tokens = tokenizer.tokenize(fake_sentence, add_special_tokens=True)
+        >>> fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
+        >>> discriminator_outputs = discriminator(fake_inputs)
+        >>> predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
+
+        >>> fake_tokens
+        ['[CLS]', 'the', 'quick', 'brown', 'fox', 'fake', 'over', 'the', 'lazy', 'dog', '[SEP]']
 
-        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(
-        ...     0
-        >>> )  # Batch size 1
-        >>> logits = model(input_ids).logits
+        >>> predictions.squeeze().tolist()
+        [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
         ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -1167,9 +1178,12 @@ class ElectraForMaskedLM(ElectraPreTrainedModel):
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="google/electra-small-generator",
         output_type=MaskedLMOutput,
         config_class=_CONFIG_FOR_DOC,
+        mask="[MASK]",
+        expected_output="'paris'",
+        expected_loss=1.22,
     )
     def forward(
         self,
@@ -1251,9 +1265,11 @@ class ElectraForTokenClassification(ElectraPreTrainedModel):
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="bhadresh-savani/electra-base-discriminator-finetuned-conll03-english",
         output_type=TokenClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="['B-LOC', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'I-LOC']",
+        expected_loss=0.11,
     )
     def forward(
         self,
@@ -1331,9 +1347,13 @@ class ElectraForQuestionAnswering(ElectraPreTrainedModel):
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="bhadresh-savani/electra-base-squad2",
         output_type=QuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
+        qa_target_start_index=11,
+        qa_target_end_index=12,
+        expected_output="'a nice puppet'",
+        expected_loss=2.64,
     )
     def forward(
         self,

src/transformers/models/electra/modeling_tf_electra.py

@@ -1160,9 +1160,12 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="google/electra-small-generator",
         output_type=TFMaskedLMOutput,
         config_class=_CONFIG_FOR_DOC,
+        mask="[MASK]",
+        expected_output="'paris'",
+        expected_loss=1.22,
     )
     def call(
         self,
@@ -1269,9 +1272,11 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="bhadresh-savani/electra-base-emotion",
         output_type=TFSequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="'joy'",
+        expected_loss=0.06,
     )
     def call(
         self,
@@ -1478,9 +1483,11 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="bhadresh-savani/electra-base-discriminator-finetuned-conll03-english",
         output_type=TFTokenClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="['B-LOC', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC', 'I-LOC']",
+        expected_loss=0.11,
     )
     def call(
         self,
@@ -1558,9 +1565,13 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="bhadresh-savani/electra-base-squad2",
         output_type=TFQuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
+        qa_target_start_index=11,
+        qa_target_end_index=12,
+        expected_output="'a nice puppet'",
+        expected_loss=2.64,
     )
     def call(
         self,

utils/documentation_tests.txt

@@ -17,6 +17,8 @@ src/transformers/models/convnext/modeling_convnext.py
 src/transformers/models/data2vec/modeling_data2vec_audio.py
 src/transformers/models/deit/modeling_deit.py
 src/transformers/models/dpt/modeling_dpt.py
+src/transformers/models/electra/modeling_electra.py
+src/transformers/models/electra/modeling_tf_electra.py
 src/transformers/models/glpn/modeling_glpn.py
 src/transformers/models/gpt2/modeling_gpt2.py
 src/transformers/models/gptj/modeling_gptj.py