diff --git a/haystack/reader/transformers.py b/haystack/reader/transformers.py
index 197c7a248..c43527323 100644
--- a/haystack/reader/transformers.py
+++ b/haystack/reader/transformers.py
@@ -23,6 +23,7 @@ class TransformersReader(BaseReader):
         context_window_size: int = 30,
         use_gpu: int = 0,
         n_best_per_passage: int = 2,
+        no_answer: bool = True
     ):
         """
         Load a QA model from Transformers.
@@ -39,11 +40,16 @@ class TransformersReader(BaseReader):
                             The context usually helps users to understand if the answer really makes sense.
         :param use_gpu: < 0  -> use cpu
                         >= 0 -> ordinal of the gpu to use
+        :param n_best_per_passage: num of best answers to take into account for each passage
+        :param no_answer: True -> Hugging Face model could return an "impossible"/"empty" answer (i.e. when there is an unanswerable question)
+                        False -> otherwise
+
         """
         self.model = pipeline('question-answering', model=model, tokenizer=tokenizer, device=use_gpu)
         self.context_window_size = context_window_size
         self.n_best_per_passage = n_best_per_passage
-        #TODO param to modify bias for no_answer
+        self.no_answer = no_answer
+
         # TODO context_window_size behaviour different from behavior in FARMReader
 
     def predict(self, question: str, documents: List[Document], top_k: Optional[int] = None):
@@ -76,25 +82,24 @@ class TransformersReader(BaseReader):
         answers = []
         for doc in documents:
             query = {"context": doc.text, "question": question}
-            predictions = self.model(query, topk=self.n_best_per_passage)
+            predictions = self.model(query, topk=self.n_best_per_passage,handle_impossible_answer=self.no_answer)
             # for single preds (e.g. via top_k=1) transformers returns a dict instead of a list
             if type(predictions) == dict:
                 predictions = [predictions]
             # assemble and format all answers
             for pred in predictions:
-                if pred["answer"]:
-                    context_start = max(0, pred["start"] - self.context_window_size)
-                    context_end = min(len(doc.text), pred["end"] + self.context_window_size)
-                    answers.append({
-                        "answer": pred["answer"],
-                        "context": doc.text[context_start:context_end],
-                        "offset_start": pred["start"],
-                        "offset_end": pred["end"],
-                        "probability": pred["score"],
-                        "score": None,
-                        "document_id": doc.id,
-                        "meta": doc.meta
-                    })
+                context_start = max(0, pred["start"] - self.context_window_size)
+                context_end = min(len(doc.text), pred["end"] + self.context_window_size)
+                answers.append({
+                    "answer": pred["answer"],
+                    "context": doc.text[context_start:context_end],
+                    "offset_start": pred["start"],
+                    "offset_end": pred["end"],
+                    "probability": pred["score"],
+                    "score": None,
+                    "document_id": doc.id,
+                    "meta": doc.meta
+                })
 
         # sort answers by their `probability` and select top-k
         answers = sorted(