From pretrained correct initialization. Unknown token handling for gpt2.

pytorch_transformers/modeling_gpt2.py

@@ -423,7 +423,7 @@ class GPT2PreTrainedModel(PreTrainedModel):
         """
         num_special_tokens = kwargs.pop('num_special_tokens', None)
 
-        model = super().from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+        model = super(GPT2PreTrainedModel, cls).from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
 
         # Add additional embeddings for special tokens if needed
         # This step also make sure we are still sharing the output and input embeddings after loading weights

pytorch_transformers/modeling_openai.py

@@ -431,7 +431,7 @@ class OpenAIGPTPreTrainedModel(PreTrainedModel):
         num_special_tokens = kwargs.get('num_special_tokens', None)
         kwargs.pop('num_special_tokens', None)
 
-        model = super(PreTrainedModel, cls).from_pretrained(pretrained_model_name_or_path, pretrained_model_name_or_path, *inputs, **kwargs)
+        model = super(OpenAIGPTPreTrainedModel, cls).from_pretrained(pretrained_model_name_or_path, pretrained_model_name_or_path, *inputs, **kwargs)
 
         # Add additional embeddings for special tokens if needed
         # This step also make sure we are still sharing the output and input embeddings after loading weights

pytorch_transformers/tokenization_gpt2.py

@@ -177,11 +177,11 @@ class GPT2Tokenizer(PreTrainedTokenizer):
 
     def _convert_token_to_id(self, token):
         """ Converts a token (str/unicode) in an id using the vocab. """
-        return self.encoder.get(token, self.encoder.get(self.unk_token))
+        return self.encoder.get(token)
 
     def _convert_id_to_token(self, index):
         """Converts an index (integer) in a token (string/unicode) using the vocab."""
-        return self.decoder.get(index, self.unk_token)
+        return self.decoder.get(index)
 
     def _convert_ids_to_string(self, tokens_ids):
         """Converts a sequence of ids in a string."""