add no_answer option to aggregation of paragraphs level preds

haystack/reader/farm.py

@@ -53,6 +53,7 @@ def __init__(
  self.inferencer = Inferencer.load(model_name_or_path, batch_size=batch_size, gpu=use_gpu, task_type="question_answering")
  self.inferencer.model.prediction_heads[0].context_window_size = context_window_size
  self.inferencer.model.prediction_heads[0].no_ans_threshold = no_ans_threshold
+ self.no_ans_threshold = no_ans_threshold
  self.inferencer.model.prediction_heads[0].n_best = n_candidates_per_passage
 
  def train(self, data_dir, train_filename, dev_filename=None, test_file_name=None,
@@ -183,16 +184,20 @@ def predict(self, question, paragrahps, meta_data_paragraphs=None, top_k=None, m
  }
  input_dicts.append(cur)
 
- # get answers from QA model (Top 5 per input paragraph)
+ # get answers from QA model (Default: top 5 per input paragraph)
  predictions = self.inferencer.inference_from_dicts(
  dicts=input_dicts, rest_api_schema=True, max_processes=max_processes
  )
 
  # assemble answers from all the different paragraphs & format them
+ # for the "no answer" option, we choose the no_answer score from the paragraph with the best "real answer"
+ # the score of this "no answer" is then "boosted" with the no_ans_gap
  answers = []
+ best_score_answer = 0
  for pred in predictions:
  for a in pred["predictions"][0]["answers"]:
- if a["answer"]: #skip "no answer"
+ # skip "no answers" here
+ if a["answer"]:
  cur = {"answer": a["answer"],
  "score": a["score"],
  "probability": float(expit(np.asarray([a["score"]]) / 8)), #just a pseudo prob for now
@@ -201,14 +206,28 @@ def predict(self, question, paragrahps, meta_data_paragraphs=None, top_k=None, m
  "offset_end": a["offset_answer_end"] - a["offset_context_start"],
  "document_id": a["document_id"]}
  answers.append(cur)
+ # if cur answer is the best, we store the gap to "no answer" in this paragraph
+ if a["score"] > best_score_answer:
+ best_score_answer = a["score"]
+ no_ans_gap = pred["predictions"][0]["no_ans_gap"]
+ no_ans_score = (best_score_answer+no_ans_gap)-self.no_ans_threshold
+
+ # add no answer option from the paragraph with the best answer
+ cur = {"answer": "",
+ "score": no_ans_score,
+ "probability": float(expit(np.asarray(no_ans_score) / 8)), # just a pseudo prob for now
+ "context": "",
+ "offset_start": -1,
+ "offset_end": -1,
+ "document_id": None}
+ answers.append(cur)
 
  # sort answers by their `probability` and select top-k
  answers = sorted(
  answers, key=lambda k: k["probability"], reverse=True
  )
  answers = answers[:top_k]
-
  result = {"question": question,
  "answers": answers}
 
- return result
+ return result