More fine-grained control over pipeline creation with config argument.

transformers/pipelines.py

@@ -497,7 +497,7 @@ class QuestionAnsweringPipeline(Pipeline):
                     'score': score.item(),
                     'start': np.where(char_to_word == feature.token_to_orig_map[s])[0][0].item(),
                     'end': np.where(char_to_word == feature.token_to_orig_map[e])[0][-1].item(),
-                    'answer': ' '.join(example.doc_tokens[feature.token_to_orig_map[s]: feature.token_to_orig_map[e] + 1])
+                    'answer': ' '.join(example.doc_tokens[feature.token_to_orig_map[s]:feature.token_to_orig_map[e] + 1])
                 }
                 for s, e, score in zip(starts, ends, scores)
             ]
@@ -612,7 +612,8 @@ SUPPORTED_TASKS = {
 }
 
 
-def pipeline(task: str, model, config: Optional[PretrainedConfig] = None, tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None, **kwargs) -> Pipeline:
+def pipeline(task: str, model, config: Optional[Union[str, PretrainedConfig]] = None,
+             tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None, **kwargs) -> Pipeline:
     """
     Utility factory method to build a pipeline.
     Pipeline are made of:
@@ -637,13 +638,21 @@ def pipeline(task: str, model, config: Optional[PretrainedConfig] = None, tokeni
     task, allocator = targeted_task['impl'], targeted_task['tf'] if is_tf_available() else targeted_task['pt']
 
     # Special handling for model conversion
-    from_tf = model.endswith('.h5') and not is_tf_available()
-    from_pt = model.endswith('.bin') and not is_torch_available()
+    if isinstance(model, str):
+        from_tf = model.endswith('.h5') and not is_tf_available()
+        from_pt = model.endswith('.bin') and not is_torch_available()
+
+        if from_tf:
+            logger.warning('Model might be a TensorFlow model (ending with `.h5`) but TensorFlow is not available. '
+                           'Trying to load the model with PyTorch.')
+        elif from_pt:
+            logger.warning('Model might be a PyTorch model (ending with `.bin`) but PyTorch is not available. '
+                           'Trying to load the model with Tensorflow.')
+    else:
+        from_tf = from_pt = False
 
-    if from_tf:
-        logger.warning('Model might be a TensorFlow model (ending with `.h5`) but TensorFlow is not available. Trying to load the model with PyTorch.')
-    elif from_pt:
-        logger.warning('Model might be a PyTorch model (ending with `.bin`) but PyTorch is not available. Trying to load the model with Tensorflow.')
+    if isinstance(config, str):
+        config = PretrainedConfig.from_pretrained(config)
 
     if allocator.__name__.startswith('TF'):
         model = allocator.from_pretrained(model, config=config, from_pt=from_pt)