[Doctests] Make roberta-like meaningfull (#16363)

* [Doctests] Make roberta-like meaningfull

* correct

* final correct

* Trigger test

* make style

* apply suggestion from sylvain

src/transformers/models/roberta/modeling_roberta.py

@@ -1067,6 +1067,8 @@ class RobertaForMaskedLM(RobertaPreTrainedModel):
         output_type=MaskedLMOutput,
         config_class=_CONFIG_FOR_DOC,
         mask="<mask>",
+        expected_output="' Paris'",
+        expected_loss=0.1,
     )
     def forward(
         self,
@@ -1177,9 +1179,11 @@ class RobertaForSequenceClassification(RobertaPreTrainedModel):
     @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="cardiffnlp/twitter-roberta-base-emotion",
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="'optimism'",
+        expected_loss=0.08,
     )
     def forward(
         self,
@@ -1372,9 +1376,11 @@ class RobertaForTokenClassification(RobertaPreTrainedModel):
     @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="Jean-Baptiste/roberta-large-ner-english",
         output_type=TokenClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']",
+        expected_loss=0.01,
     )
     def forward(
         self,
@@ -1475,9 +1481,11 @@ class RobertaForQuestionAnswering(RobertaPreTrainedModel):
     @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="deepset/roberta-base-squad2",
         output_type=QuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="' puppet'",
+        expected_loss=0.86,
     )
     def forward(
         self,

src/transformers/utils/doc.py

@@ -156,12 +156,28 @@ PT_TOKEN_CLASSIFICATION_SAMPLE = r"""
     >>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
     >>> model = {model_class}.from_pretrained("{checkpoint}")
 
-    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-    >>> labels = torch.tensor([1] * inputs["input_ids"].size(1)).unsqueeze(0)  # Batch size 1
+    >>> inputs = tokenizer(
+    ...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
+    ... )
 
-    >>> outputs = model(**inputs, labels=labels)
-    >>> loss = outputs.loss
-    >>> logits = outputs.logits
+    >>> with torch.no_grad():
+    ...     logits = model(**inputs).logits
+
+    >>> predicted_token_class_ids = logits.argmax(-1)
+
+    >>> # Note that tokens are classified rather then input words which means that
+    >>> # there might be more predicted token classes than words.
+    >>> # Multiple token classes might account for the same word
+    >>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
+    >>> predicted_tokens_classes
+    {expected_output}
+    ```
+
+    ```python
+    >>> labels = predicted_token_class_ids
+    >>> loss = model(**inputs, labels=labels).loss
+    >>> round(loss.item(), 2)
+    {expected_loss}
     ```
 """
 
@@ -172,28 +188,31 @@ PT_QUESTION_ANSWERING_SAMPLE = r"""
     >>> from transformers import {processor_class}, {model_class}
     >>> import torch
 
-    >>> torch.manual_seed(0)  # doctest: +IGNORE_RESULT
-
     >>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
     >>> model = {model_class}.from_pretrained("{checkpoint}")
 
     >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
+
     >>> inputs = tokenizer(question, text, return_tensors="pt")
-    >>> start_positions = torch.tensor([1])
-    >>> end_positions = torch.tensor([3])
+    >>> with torch.no_grad():
+    ...     outputs = model(**inputs)
 
-    >>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
-    >>> loss = outputs.loss
-    >>> round(loss.item(), 2)
-    {expected_loss}
+    >>> answer_start_index = outputs.start_logits.argmax()
+    >>> answer_end_index = outputs.end_logits.argmax()
 
-    >>> start_scores = outputs.start_logits
-    >>> list(start_scores.shape)
+    >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
+    >>> tokenizer.decode(predict_answer_tokens)
     {expected_output}
+    ```
 
-    >>> end_scores = outputs.end_logits
-    >>> list(end_scores.shape)
-    {expected_output}
+    ```python
+    >>> # target is "nice puppet"
+    >>> target_start_index, target_end_index = torch.tensor([14]), torch.tensor([15])
+
+    >>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
+    >>> loss = outputs.loss
+    >>> round(loss.item(), 2)
+    {expected_loss}
     ```
 """
 
@@ -204,40 +223,62 @@ PT_SEQUENCE_CLASSIFICATION_SAMPLE = r"""
     >>> import torch
     >>> from transformers import {processor_class}, {model_class}
 
-    >>> torch.manual_seed(0)  # doctest: +IGNORE_RESULT
-
     >>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
-    >>> model = {model_class}.from_pretrained("{checkpoint}", num_labels=2)
+    >>> model = {model_class}.from_pretrained("{checkpoint}")
 
     >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-    >>> labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
-    >>> outputs = model(**inputs, labels=labels)
-    >>> loss = outputs.loss
-    >>> logits = outputs.logits
-    >>> list(logits.shape)
+
+    >>> with torch.no_grad():
+    ...     logits = model(**inputs).logits
+
+    >>> predicted_class_id = logits.argmax().item()
+    >>> model.config.id2label[predicted_class_id]
     {expected_output}
     ```
 
+    ```python
+    >>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
+    >>> num_labels = len(model.config.id2label)
+    >>> model = {model_class}.from_pretrained("{checkpoint}", num_labels=num_labels)
+
+    >>> labels = torch.tensor(1)
+    >>> loss = model(**inputs, labels=labels).loss
+    >>> round(loss.item(), 2)
+    {expected_loss}
+    ```
+
     Example of multi-label classification:
 
     ```python
     >>> import torch
     >>> from transformers import {processor_class}, {model_class}
 
-    >>> torch.manual_seed(0)  # doctest: +IGNORE_RESULT
-
     >>> tokenizer = {processor_class}.from_pretrained("{checkpoint}")
-    >>> model = {model_class}.from_pretrained("{checkpoint}", problem_type="multi_label_classification", num_labels=2)
+    >>> model = {model_class}.from_pretrained("{checkpoint}", problem_type="multi_label_classification")
 
     >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-    >>> labels = torch.tensor([[1, 1]], dtype=torch.float)  # need dtype=float for BCEWithLogitsLoss
-    >>> outputs = model(**inputs, labels=labels)
-    >>> loss = outputs.loss
-    >>> list(logits.shape)
+
+    >>> with torch.no_grad():
+    ...     logits = model(**inputs).logits
+
+    >>> predicted_class_id = logits.argmax().item()
+    >>> model.config.id2label[predicted_class_id]
     {expected_output}
     ```
-"""
 
+    ```python
+    >>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
+    >>> num_labels = len(model.config.id2label)
+    >>> model = {model_class}.from_pretrained("{checkpoint}", num_labels=num_labels)
+
+    >>> num_labels = len(model.config.id2label)
+    >>> labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
+    ...     torch.float
+    ... )
+    >>> loss = model(**inputs, labels=labels).loss
+    >>> loss.backward()  # doctest: +IGNORE_RESULT
+    ```
+"""
 
 PT_MASKED_LM_SAMPLE = r"""
     Example:
@@ -250,11 +291,26 @@ PT_MASKED_LM_SAMPLE = r"""
     >>> model = {model_class}.from_pretrained("{checkpoint}")
 
     >>> inputs = tokenizer("The capital of France is {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)
+    {expected_output}
+    ```
+
+    ```python
     >>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
+    >>> # mask labels of non-{mask} tokens
+    >>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
 
     >>> outputs = model(**inputs, labels=labels)
-    >>> loss = outputs.loss
-    >>> logits = outputs.logits
+    >>> round(outputs.loss.item(), 2)
+    {expected_loss}
     ```
 """
 

utils/documentation_tests.txt

 docs/source/quicktour.mdx
+docs/source/quicktour.mdx
+docs/source/task_summary.mdx
 docs/source/task_summary.mdx
 src/transformers/generation_utils.py
+src/transformers/generation_utils.py
+src/transformers/models/bart/modeling_bart.py
 src/transformers/models/bart/modeling_bart.py
 src/transformers/models/beit/modeling_beit.py
 src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py
+src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py
+src/transformers/models/blenderbot/modeling_blenderbot.py
 src/transformers/models/blenderbot/modeling_blenderbot.py
 src/transformers/models/blenderbot_small/modeling_blenderbot_small.py
+src/transformers/models/blenderbot_small/modeling_blenderbot_small.py
 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/glpn/modeling_glpn.py
 src/transformers/models/hubert/modeling_hubert.py
 src/transformers/models/marian/modeling_marian.py
+src/transformers/models/marian/modeling_marian.py
+src/transformers/models/mbart/modeling_mbart.py
 src/transformers/models/mbart/modeling_mbart.py
 src/transformers/models/pegasus/modeling_pegasus.py
+src/transformers/models/pegasus/modeling_pegasus.py
+src/transformers/models/plbart/modeling_plbart.py
 src/transformers/models/plbart/modeling_plbart.py
 src/transformers/models/poolformer/modeling_poolformer.py
 src/transformers/models/resnet/modeling_resnet.py
+src/transformers/models/resnet/modeling_resnet.py
+src/transformers/models/roberta/modeling_roberta.py
 src/transformers/models/segformer/modeling_segformer.py
 src/transformers/models/sew/modeling_sew.py
 src/transformers/models/sew_d/modeling_sew_d.py
@@ -34,16 +48,3 @@ src/transformers/models/wav2vec2/modeling_wav2vec2.py
 src/transformers/models/wav2vec2/tokenization_wav2vec2.py
 src/transformers/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.py
 src/transformers/models/wavlm/modeling_wavlm.py
-src/transformers/models/bart/modeling_bart.py
-src/transformers/models/mbart/modeling_mbart.py
-src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py
-src/transformers/models/marian/modeling_marian.py
-src/transformers/models/pegasus/modeling_pegasus.py
-src/transformers/models/blenderbot/modeling_blenderbot.py
-src/transformers/models/blenderbot_small/modeling_blenderbot_small.py
-src/transformers/models/plbart/modeling_plbart.py
-src/transformers/generation_utils.py
-docs/source/quicktour.mdx
-docs/source/task_summary.mdx
-src/transformers/models/resnet/modeling_resnet.py
-src/transformers/models/glpn/modeling_glpn.py