Adding labels mapping for classification models in their respective config.

transformers/configuration_utils.py

@@ -58,8 +58,8 @@ class PretrainedConfig(object):
         self.use_bfloat16 = kwargs.pop('use_bfloat16', False)
         self.pruned_heads = kwargs.pop('pruned_heads', {})
         self.is_decoder = kwargs.pop('is_decoder', False)
-        self.idx2label = kwargs.pop('idx2label', {i: 'LABEL_{}'.format(i) for i in range(self.num_labels)})
-        self.label2idx = kwargs.pop('label2idx', dict(zip(self.idx2label.values(), self.idx2label.keys())))
+        self.id2label = kwargs.pop('id2label', {i: 'LABEL_{}'.format(i) for i in range(self.num_labels)})
+        self.label2id = kwargs.pop('label2id', dict(zip(self.id2label.values(), self.id2label.keys())))
 
     def save_pretrained(self, save_directory):
         """ Save a configuration object to the directory `save_directory`, so that it