update io/file_utils.py

fastNLP/io/file_utils.py

@@ -21,8 +21,8 @@ PRETRAINED_BERT_MODEL_DIR = {
 
     'en-base-cased-mrpc': 'bert-base-cased-finetuned-mrpc.zip',
 
-    'en-base-multi-cased': 'bert-base-multilingual-cased.zip',
-    'en-base-multi-uncased': 'bert-base-multilingual-uncased.zip',
+    'multi-base-cased': 'bert-base-multilingual-cased.zip',
+    'multi-base-uncased': 'bert-base-multilingual-uncased.zip',
 
     'cn': 'bert-chinese-wwm.zip',
     'cn-base': 'bert-base-chinese.zip',
@@ -38,7 +38,7 @@ PRETRAINED_ELMO_MODEL_DIR = {
 }
 
 PRETRAIN_STATIC_FILES = {
-    'en': 'glove.840B.300d.tar.gz',
+    'en': 'glove.840B.300d.zip',
 
     'en-glove-6b-50d': 'glove.6B.50d.zip',
     'en-glove-6b-100d': 'glove.6B.100d.zip',
@@ -184,26 +184,26 @@ def _get_base_url(name):
         return URLS[name.lower()]
 
 
-def _get_embedding_url(type, name):
+def _get_embedding_url(embed_type, name):
     """
     给定embedding类似和名称，返回下载url
 
-    :param str type: 支持static, bert, elmo。即embedding的类型
+    :param str embed_type: 支持static, bert, elmo。即embedding的类型
     :param str name: embedding的名称, 例如en, cn, based等
     :return: str, 下载的url地址
     """
     PRETRAIN_MAP = {'elmo': PRETRAINED_ELMO_MODEL_DIR,
                     "bert": PRETRAINED_BERT_MODEL_DIR,
                     "static": PRETRAIN_STATIC_FILES}
-    map = PRETRAIN_MAP.get(type, None)
-    if map:
-        filename = map.get(name, None)
+    embed_map = PRETRAIN_MAP.get(embed_type, None)
+    if embed_map:
+        filename = embed_map.get(name, None)
         if filename:
             url = _get_base_url('embedding') + filename
             return url
-        raise KeyError("There is no {}. Only supports {}.".format(name, list(map.keys())))
+        raise KeyError("There is no {}. Only supports {}.".format(name, list(embed_map.keys())))
     else:
-        raise KeyError(f"There is no {type}. Only supports bert, elmo, static")
+        raise KeyError(f"There is no {embed_type}. Only supports bert, elmo, static")
 
 
 def _get_dataset_url(name):