more explicit variable name

pytorch_pretrained_bert/modeling.py

@@ -512,14 +512,14 @@ class BertPreTrainedModel(nn.Module):
             module.bias.data.zero_()
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name, state_dict=None, cache_dir=None,
+    def from_pretrained(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None,
                         from_tf=False, *inputs, **kwargs):
         """
         Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
         Download and cache the pre-trained model file if needed.
 
         Params:
-            pretrained_model_name: either:
+            pretrained_model_name_or_path: either:
                 - a str with the name of a pre-trained model to load selected in the list of:
                     . `bert-base-uncased`
                     . `bert-large-uncased`
@@ -540,10 +540,10 @@ class BertPreTrainedModel(nn.Module):
             *inputs, **kwargs: additional input for the specific Bert class
                 (ex: num_labels for BertForSequenceClassification)
         """
-        if pretrained_model_name in PRETRAINED_MODEL_ARCHIVE_MAP:
-            archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name]
+        if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
+            archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
         else:
-            archive_file = pretrained_model_name
+            archive_file = pretrained_model_name_or_path
         # redirect to the cache, if necessary
         try:
             resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
@@ -552,7 +552,7 @@ class BertPreTrainedModel(nn.Module):
                 "Model name '{}' was not found in model name list ({}). "
                 "We assumed '{}' was a path or url but couldn't find any file "
                 "associated to this path or url.".format(
-                    pretrained_model_name,
+                    pretrained_model_name_or_path,
                     ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
                     archive_file))
             return None

pytorch_pretrained_bert/modeling_openai.py

@@ -418,14 +418,14 @@ class OpenAIGPTPreTrainedModel(nn.Module):
 
     @classmethod
     def from_pretrained(
-        cls, pretrained_model_name, num_special_tokens=None, state_dict=None, cache_dir=None, from_tf=False, *inputs, **kwargs
+        cls, pretrained_model_name_or_path, num_special_tokens=None, state_dict=None, cache_dir=None, from_tf=False, *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.
 
         Params:
-            pretrained_model_name: either:
+            pretrained_model_name_or_path: either:
                 - a str with the name of a pre-trained model to load selected in the list of:
                     . `openai-gpt`
                 - a path or url to a pretrained model archive containing:
@@ -440,11 +440,11 @@ class OpenAIGPTPreTrainedModel(nn.Module):
             *inputs, **kwargs: additional input for the specific Bert class
                 (ex: num_labels for BertForSequenceClassification)
         """
-        if pretrained_model_name in PRETRAINED_MODEL_ARCHIVE_MAP:
-            archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name]
+        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]
         else:
-            archive_file = pretrained_model_name
+            archive_file = pretrained_model_name_or_path
             config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
         # redirect to the cache, if necessary
         try:
@@ -455,7 +455,7 @@ class OpenAIGPTPreTrainedModel(nn.Module):
                 "Model name '{}' was not found in model name list ({}). "
                 "We assumed '{}' was a path or url but couldn't find files {} and {} "
                 "at this path or url.".format(
-                    pretrained_model_name, ", ".join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()), pretrained_model_name_or_path,
+                    pretrained_model_name_or_path, ", ".join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()), pretrained_model_name_or_path,
                     archive_file, config_file
                 )
             )

pytorch_pretrained_bert/tokenization.py

@@ -116,15 +116,15 @@ class BertTokenizer(object):
         return tokens
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs):
+    def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
         """
         Instantiate a PreTrainedBertModel from a pre-trained model file.
         Download and cache the pre-trained model file if needed.
         """
-        if pretrained_model_name in PRETRAINED_VOCAB_ARCHIVE_MAP:
-            vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name]
+        if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
+            vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
         else:
-            vocab_file = pretrained_model_name
+            vocab_file = pretrained_model_name_or_path
         if os.path.isdir(vocab_file):
             vocab_file = os.path.join(vocab_file, VOCAB_NAME)
         # redirect to the cache, if necessary
@@ -135,7 +135,7 @@ class BertTokenizer(object):
                 "Model name '{}' was not found in model name list ({}). "
                 "We assumed '{}' was a path or url but couldn't find any file "
                 "associated to this path or url.".format(
-                    pretrained_model_name,
+                    pretrained_model_name_or_path,
                     ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
                     vocab_file))
             return None
@@ -144,10 +144,10 @@ class BertTokenizer(object):
         else:
             logger.info("loading vocabulary file {} from cache at {}".format(
                 vocab_file, resolved_vocab_file))
-        if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
+        if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
             # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
             # than the number of positional embeddings
-            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name]
+            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
             kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
         # Instantiate tokenizer.
         tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)