return details

harim_plus.py

@@ -128,5 +128,9 @@ class Harimplus(evaluate.Metric):
                         return_details=False):
         summaries = predictions
         articles = references
-        scores = self.scorer.compute(predictions=summaries, references=articles,  use_aggregator=use_aggregator, bsz=bsz, tokenwise_score=tokenwise_score, return_details=return_details)
+        scores = self.scorer.compute(predictions=summaries,
+                                    references=articles,
+                                    use_aggregator=use_aggregator,
+                                    bsz=bsz, tokenwise_score=tokenwise_score,
+                                    return_details=return_details)
         return scores

harim_scorer.py

@@ -102,6 +102,7 @@ class Harimplus_Scorer:
                         references:List[str],
                         bsz:int=32,
                         use_aggregator:bool=False,
+                        return_details:bool=False,
                         tokenwise_score:bool=False,
                         ):
         '''
@@ -190,6 +191,8 @@ class Harimplus_Scorer:
                 if not k.startswith('tok_'):
                     scores[k] = sum(v)/len(v) # aggregate (mean)
         scores['lambda'] = self._lambda
+        if not return_details:
+            scores = scores['harim+']
         return scores
 
 
@@ -221,7 +224,7 @@ def test(bsz = 16, pretrained_name='facebook/bart-large-cnn', tokenizer=None):
     ]
     articles = [ art1 ]*5 + [art2 ]*4
     # set_trace()
-    hp_score = scorer.compute(predictions=summaries, references=articles, use_aggregator=False, bsz=bsz)
+    hp_score = scorer.compute(predictions=summaries, references=articles, use_aggregator=False, bsz=bsz, return_details=False, tokenwise_score=False)
     # pprint(f"{articles=}")
     # pprint(f"{summaries=}")
     pprint(hp_score)