Hubconf doc - Specia case loading

hubconf.py

@@ -191,6 +191,12 @@ def bertForSequenceClassification(*args, **kwargs):
     The sequence-level classifier is a linear layer that takes as input the
     last hidden state of the first character in the input sequence
     (see Figures 3a and 3b in the BERT paper).
+
+    Args:
+    num_labels: the number (>=2) of classes for the classifier.
+
+    Example:
+        >>> torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertForSequenceClassification', 'bert-base-cased', num_labels=2, force_reload=True)
     """
     model = BertForSequenceClassification.from_pretrained(*args, **kwargs)
     return model
@@ -201,6 +207,12 @@ def bertForMultipleChoice(*args, **kwargs):
     """
     BertForMultipleChoice is a fine-tuning model that includes BertModel and a
     linear layer on top of the BertModel.
+
+    Args:
+    num_choices: the number (>=2) of classes for the classifier.
+
+    Example:
+        >>> torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertForMultipleChoice', 'bert-base-cased', num_choices=2, force_reload=True)
     """
     model = BertForMultipleChoice.from_pretrained(*args, **kwargs)
     return model
@@ -225,6 +237,12 @@ def bertForTokenClassification(*args, **kwargs):
 
     The token-level classifier is a linear layer that takes as input the last
     hidden state of the sequence.
+
+    Args:
+    num_labels: the number (>=2) of classes for the classifier.
+
+    Example:
+        >>> torch.hub.load('huggingface/pytorch-pretrained-BERT', 'bertForTokenClassification', 'bert-base-cased', num_labels=2, force_reload=True)
     """
     model = BertForTokenClassification.from_pretrained(*args, **kwargs)
     return model