Enable the Tensorflow backend.

transformers/pipelines.py

@@ -151,8 +151,7 @@ class QuestionAnsweringPipeline(Pipeline):
         texts = [(text['question'], text['context']) for text in texts]
 
         inputs = self.tokenizer.batch_encode_plus(
-            # texts, add_special_tokens=True, return_tensors='tf' if is_tf_available() else 'pt'
-            texts, add_special_tokens=True, return_tensors='pt'
+            texts, add_special_tokens=True, return_tensors='tf' if is_tf_available() else 'pt'
         )
 
         # Remove special_tokens_mask to avoid KeyError
@@ -161,10 +160,10 @@ class QuestionAnsweringPipeline(Pipeline):
         # TODO : Harmonize model arguments across all model
         inputs['attention_mask'] = inputs.pop('encoder_attention_mask')
 
-        # if is_tf_available():
-        if False:
+        if is_tf_available():
             # TODO trace model
             start, end = self.model(inputs)
+            start, end = start.numpy(), end.numpy()
         else:
             import torch
             with torch.no_grad():
@@ -204,9 +203,7 @@ class QuestionAnsweringPipeline(Pipeline):
         # Remove candidate with end < start and end - start > max_answer_len
         candidates = np.tril(np.triu(outer), max_answer_len - 1)
 
-        # start = np.max(candidates, axis=2).argmax(-1)
-        # end = np.max(candidates, axis=1).argmax(-1)
-
+        #  Inspired by Chen & al. (https://github.com/facebookresearch/DrQA)
         scores_flat = candidates.flatten()
         if topk == 1:
             idx_sort = [np.argmax(scores_flat)]
@@ -257,7 +254,7 @@ SUPPORTED_TASKS = {
     },
     'question-answering': {
         'impl': QuestionAnsweringPipeline,
-        # 'tf': TFAutoModelForQuestionAnswering if is_tf_available() else None,
+        'tf': TFAutoModelForQuestionAnswering if is_tf_available() else None,
         'pt': AutoModelForQuestionAnswering if is_torch_available() else None
     }
 }
@@ -280,8 +277,7 @@ def pipeline(task: str, model, tokenizer: Optional[Union[str, PreTrainedTokenize
         raise KeyError("Unknown task {}, available tasks are {}".format(task, list(SUPPORTED_TASKS.keys())))
 
     targeted_task = SUPPORTED_TASKS[task]
-    # task, allocator = targeted_task['impl'], targeted_task['tf'] if is_tf_available() else targeted_task['pt']
-    task, allocator = targeted_task['impl'], targeted_task['pt']
+    task, allocator = targeted_task['impl'], targeted_task['tf'] if is_tf_available() else targeted_task['pt']
 
     model = allocator.from_pretrained(model)
     return task(model, tokenizer, **kwargs)