update __init__ and saving

transformers/__init__.py

@@ -19,7 +19,7 @@ logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 # Files and general utilities
 from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
                          cached_path, add_start_docstrings, add_end_docstrings,
-                         WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
+                         WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME, MODEL_CARD_NAME,
                          is_tf_available, is_torch_available)
 
 from .data import (is_sklearn_available,

transformers/model_card.py

@@ -67,14 +67,14 @@ class ModelCard(object):
                 logger.error("Can't set {} with value {} for {}".format(key, value, self))
                 raise err
 
-    def save_pretrained(self, save_directory):
-        """ Save a model card object to the directory `save_directory`, so that it
-            can be re-loaded using the :func:`~transformers.ModelCard.from_pretrained` class method.
+    def save_pretrained(self, save_directory_or_file):
+        """ Save a model card object to the directory or file `save_directory_or_file`.
         """
-        assert os.path.isdir(save_directory), "Saving path should be a directory where the model card can be saved"
-
-        # If we save using the predefined names, we can load using `from_pretrained`
-        output_model_card_file = os.path.join(save_directory, MODEL_CARD_NAME)
+        if os.path.isdir(save_directory_or_file):
+            # If we save using the predefined names, we can load using `from_pretrained`
+            output_model_card_file = os.path.join(save_directory_or_file, MODEL_CARD_NAME)
+        else:
+            output_model_card_file = save_directory_or_file
 
         self.to_json_file(output_model_card_file)
         logger.info("Model card saved in {}".format(output_model_card_file))
@@ -139,8 +139,9 @@ class ModelCard(object):
             model_card_file = pretrained_model_name_or_path
         else:
             model_card_file = hf_bucket_url(pretrained_model_name_or_path, postfix=MODEL_CARD_NAME)
-        # redirect to the cache, if necessary
+
         try:
+            # Load from URL or cache if already cached
             resolved_model_card_file = cached_path(model_card_file, cache_dir=cache_dir, force_download=force_download,
                                                proxies=proxies, resume_download=resume_download)
             if resolved_model_card_file == model_card_file:
@@ -163,6 +164,7 @@ class ModelCard(object):
                         ', '.join(ALL_PRETRAINED_CONFIG_ARCHIVE_MAP.keys()),
                         model_card_file, MODEL_CARD_NAME))
             logger.warning("Creating an empty model card.")
+
             # We fall back on creating an empty model card
             model_card = cls()
 
@@ -171,6 +173,7 @@ class ModelCard(object):
                            "model card file is not a valid JSON file. "
                            "Please check network or file content here: {}.".format(model_card_file, resolved_model_card_file))
             logger.warning("Creating an empty model card.")
+
             # We fall back on creating an empty model card
             model_card = cls()