Added FeatureExtraction pipeline.

transformers/pipelines.py

@@ -143,7 +143,23 @@ class JsonPipelineDataFormat(PipelineDataFormat):
 class FeatureExtractionPipeline(Pipeline):
 
     def __call__(self, *texts, **kwargs):
-        pass
+        # Generic compatibility with sklearn and Keras
+        if 'X' in kwargs and not texts:
+            texts = kwargs.pop('X')
+
+        inputs = self.tokenizer.batch_encode_plus(
+            texts, add_special_tokens=True, return_tensors='tf' if is_tf_available() else 'pt'
+        )
+
+        if is_tf_available():
+            # TODO trace model
+            predictions = self.model(inputs)[0]
+        else:
+            import torch
+            with torch.no_grad():
+                predictions = self.model(**inputs)[0]
+
+        return predictions.numpy().tolist()
 
 
 class TextClassificationPipeline(Pipeline):
@@ -424,6 +440,11 @@ class QuestionAnsweringPipeline(Pipeline):
 
 # Register all the supported task here
 SUPPORTED_TASKS = {
+    'feature-extraction': {
+      'impl': FeatureExtractionPipeline,
+      'tf': TFAutoModel if is_tf_available() else None,
+      'pt': AutoModel if is_torch_available() else None,
+    },
     'text-classification': {
         'impl': TextClassificationPipeline,
         'tf': TFAutoModelForSequenceClassification if is_tf_available() else None,