Removed from_config

transformers/pipelines.py

@@ -37,11 +37,6 @@ class Pipeline(ABC):
         self.model = model
         self.tokenizer = tokenizer
 
-    @classmethod
-    @abstractmethod
-    def from_config(cls, model, tokenizer: PreTrainedTokenizer, **kwargs):
-        raise NotImplementedError()
-
     def save_pretrained(self, save_directory):
         if not os.path.isdir(save_directory):
             logger.error("Provided path ({}) should be a directory".format(save_directory))
@@ -63,6 +58,12 @@ class Pipeline(ABC):
         raise NotImplementedError()
 
 
+class FeatureExtractionPipeline(Pipeline):
+
+    def __call__(self, *texts, **kwargs):
+        pass
+
+
 class TextClassificationPipeline(Pipeline):
     def __init__(self, model, tokenizer: PreTrainedTokenizer, nb_classes: int = 2):
         super().__init__(model, tokenizer)
@@ -71,10 +72,6 @@ class TextClassificationPipeline(Pipeline):
             raise Exception('Invalid parameter nb_classes. int >= 2 is required (got: {})'.format(nb_classes))
         self._nb_classes = nb_classes
 
-    @classmethod
-    def from_config(cls, model, tokenizer: PreTrainedTokenizer, **kwargs):
-        return cls(model, tokenizer, **kwargs)
-
     def __call__(self, *texts, **kwargs):
         # Generic compatibility with sklearn and Keras
         if 'X' in kwargs and not texts:
@@ -102,10 +99,6 @@ class NerPipeline(Pipeline):
     def __init__(self, model, tokenizer: PreTrainedTokenizer):
         super().__init__(model, tokenizer)
 
-    @classmethod
-    def from_config(cls, model, tokenizer: PreTrainedTokenizer, **kwargs):
-        pass
-
     def __call__(self, *texts, **kwargs):
         (texts, ), answers = texts, []