modify from_pretrained for OpenAIGPT

pytorch_pretrained_bert/modeling_openai.py

@@ -419,9 +419,7 @@ class OpenAIGPTPreTrainedModel(nn.Module):
         pass
 
     @classmethod
-    def from_pretrained(
-        cls, pretrained_model_name_or_path, num_special_tokens=None, state_dict=None, cache_dir=None, from_tf=False, *inputs, **kwargs
-    ):
+    def from_pretrained(cls, pretrained_model_name_or_path, num_special_tokens=None, *inputs, **kwargs):
         """
         Instantiate a OpenAIGPTPreTrainedModel from a pre-trained model file or a pytorch state dict.
         Download and cache the pre-trained model file if needed.
@@ -434,14 +432,20 @@ class OpenAIGPTPreTrainedModel(nn.Module):
                     . `openai_gpt_config.json` a configuration file for the model
                     . `pytorch_model.bin` a PyTorch dump of a OpenAIGPTModel instance
                 - a path or url to a pretrained model archive containing:
-                    . `bert_config.json` a configuration file for the model
+                    . `openai-gpt-config.json` a configuration file for the model
                     . a series of NumPy files containing OpenAI TensorFlow trained weights
             from_tf: should we load the weights from a locally saved TensorFlow checkpoint
             cache_dir: an optional path to a folder in which the pre-trained models will be cached.
             state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
-            *inputs, **kwargs: additional input for the specific Bert class
-                (ex: num_labels for BertForSequenceClassification)
+            *inputs, **kwargs: additional input for the specific OpenAI-GPT class
         """
+        state_dict = kwargs.get('state_dict', None)
+        kwargs.pop('state_dict', None)
+        cache_dir = kwargs.get('cache_dir', None)
+        kwargs.pop('cache_dir', None)
+        from_tf = kwargs.get('from_tf', False)
+        kwargs.pop('from_tf', None)
+
         if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
             archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
             config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]