Explain different kinds of scores in doc string of QAPredictionHead

farm/modeling/prediction_head.py

@@ -931,6 +931,16 @@ def forward(self, X):
 class QuestionAnsweringHead(PredictionHead):
  """
  A question answering head predicts the start and end of the answer on token level.
+
+ In addition, it gives a score for the prediction so that multiple answers can be ranked.
+ There are three different kinds of scores available:
+ 1) (standard) score: the sum of the logits of the start and end index. This score is unbounded because the logits are unbounded.
+ It is the default for ranking answers.
+ 2) confidence score: also based on the logits of the start and end index but scales them to the interval 0 to 1 and incorporates no_answer.
+ It can be used for ranking by setting use_confidence_scores_for_ranking to True
+ 3) calibrated confidence score: same as 2) but divides the logits by a learned temperature_for_confidence parameter
+ so that the confidence scores are closer to the model's achieved accuracy. It can be used for ranking by setting
+ use_confidence_scores_for_ranking to True and temperature_for_confidence!=1.0. See examples/question_answering_confidence.py for more details.
  """
 
  def __init__(self, layer_dims=[768,2],
@@ -964,7 +974,7 @@ def __init__(self, layer_dims=[768,2],
  :type duplicate_filtering: int
  :param temperature_for_confidence: The divisor that is used to scale logits to calibrate confidence scores
  :type temperature_for_confidence: float
- :param use_confidence_scores_for_ranking: Whether to sort answers by confidence score (normalized between 0 and 1) or by standard score (unbounded)
+ :param use_confidence_scores_for_ranking: Whether to sort answers by confidence score (normalized between 0 and 1) or by standard score (unbounded)(default).
  :type use_confidence_scores_for_ranking: bool
  """
  super(QuestionAnsweringHead, self).__init__()