fixed loading pre-trained tokenizer from directory

pytorch_pretrained_bert/modeling.py

@@ -478,7 +478,7 @@ class PreTrainedBertModel(nn.Module):
                 "associated to this path or url.".format(
                     pretrained_model_name,
                     ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
-                    pretrained_model_name))
+                    archive_file))
             return None
         if resolved_archive_file == archive_file:
             logger.info("loading archive file {}".format(archive_file))

pytorch_pretrained_bert/tokenization.py

@@ -39,6 +39,7 @@ PRETRAINED_VOCAB_ARCHIVE_MAP = {
     'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
     'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
 }
+VOCAB_NAME = 'vocab.txt'
 
 
 def load_vocab(vocab_file):
@@ -100,7 +101,7 @@ class BertTokenizer(object):
         return tokens
 
     @classmethod
-    def from_pretrained(cls, pretrained_model_name, do_lower_case=True):
+    def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs):
         """
         Instantiate a PreTrainedBertModel from a pre-trained model file.
         Download and cache the pre-trained model file if needed.
@@ -109,16 +110,11 @@ class BertTokenizer(object):
             vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name]
         else:
             vocab_file = pretrained_model_name
+        if os.path.isdir(vocab_file):
+            vocab_file = os.path.join(vocab_file, VOCAB_NAME)
         # redirect to the cache, if necessary
         try:
-            resolved_vocab_file = cached_path(vocab_file)
-            if resolved_vocab_file == vocab_file:
-                logger.info("loading vocabulary file {}".format(vocab_file))
-            else:
-                logger.info("loading vocabulary file {} from cache at {}".format(
-                    vocab_file, resolved_vocab_file))
-            # Instantiate tokenizer.
-            tokenizer = cls(resolved_vocab_file, do_lower_case)
+            resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
         except FileNotFoundError:
             logger.error(
                 "Model name '{}' was not found in model name list ({}). "
@@ -126,8 +122,15 @@ class BertTokenizer(object):
                 "associated to this path or url.".format(
                     pretrained_model_name,
                     ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()),
-                    pretrained_model_name))
-            tokenizer = None
+                    vocab_file))
+            return None
+        if resolved_vocab_file == vocab_file:
+            logger.info("loading vocabulary file {}".format(vocab_file))
+        else:
+            logger.info("loading vocabulary file {} from cache at {}".format(
+                vocab_file, resolved_vocab_file))
+        # Instantiate tokenizer.
+        tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
         return tokenizer