huggingface · patrickvonplaten · Apr 7, 2022 · Mar 28, 2022 · Mar 28, 2022 · patrickvonplaten
diff --git a/src/transformers/utils/doc.py b/src/transformers/utils/doc.py
@@ -269,9 +269,10 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
     ```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)
+    >>> model = {model_class}.from_pretrained(
 if labels is not None: 
     if self.config.problem_type is None: 
         if self.num_labels == 1: 
             self.config.problem_type = "regression" 
         elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): 
             self.config.problem_type = "single_label_classification" 
         else: 
             self.config.problem_type = "multi_label_classification" 
     ```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, problem_type="multi_label_classification" 
     ... ) 
     >>> 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 
     ``` 
 """ 
 if labels is not None: 
     if self.config.problem_type is None: 
         if self.num_labels == 1: 
             self.config.problem_type = "regression" 
         elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): 
             self.config.problem_type = "single_label_classification" 
         else: 
             self.config.problem_type = "multi_label_classification" 
     ```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, problem_type="multi_label_classification" 
     ... ) 
  
     >>> 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 
     ``` 
 """ 
+    ...     "{checkpoint}", num_labels=num_labels, problem_type="multi_label_classification"
+    ... )
 
-    >>> num_labels = len(model.config.id2label)
     >>> labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
     ...     torch.float
     ... )