Update harim_plus.py

harim_plus.py

@@ -171,7 +171,7 @@ class Harimplus_Scorer:
                         bsz:int=32,
                         use_aggregator:bool=False,
                         return_details:bool=False,
-                        tokenwise_score:bool=False,
+                        # tokenwise_score:bool=False,
                         ):
         '''
         returns harim+ score (List[float]) for predictions (summaries) and references (articles)
@@ -238,15 +238,15 @@ class Harimplus_Scorer:
                 harim_tok = self.harim(s2s_logits, lm_logits, tgt_in.input_ids, tgt_mask)
                 harim = harim_tok.sum(-1) / sent_lengths
 
-                harim_plus_normalized = ll + self._lambda * harim # loglikelihood + lambda * negative_harim (negative harim=-1* risk)
+                harim_plus_normalized = (ll + self._lambda * harim)/sent_lengths # loglikelihood + lambda * negative_harim (negative harim=-1* risk)
 
                 scores['harim+'].extend(harim_plus_normalized.tolist())
                 scores['harim'].extend(harim.tolist())
                 scores['log_ppl'].extend(ll.tolist())
 
-                if tokenwise_score:
-                    scores['tok_harim+'].append(harim_tok*self._lambda + ll_tok)
-                    scores['tok_predictions'].append( [self._tokenizer.convert_ids_to_token(idxs) for idxs in src_in.labels] )
+                # if tokenwise_score:
+                #     scores['tok_harim+'].append(harim_tok*self._lambda + ll_tok)
+                #     scores['tok_predictions'].append( [self._tokenizer.convert_ids_to_token(idxs) for idxs in src_in.labels] )
 
         if use_aggregator: # after
             for k, v in scores.items():
@@ -314,13 +314,14 @@ class Harimplus(evaluate.Metric):
                         references=None,
                         use_aggregator=False,
                         bsz=32,
-                        tokenwise_score=False,
-                        return_details=False):
+                        return_details=False):       
+                        # tokenwise_score=False,
+                        
         summaries = predictions
         articles = references
         scores = self.scorer.compute(predictions=summaries,
                                     references=articles,
                                     use_aggregator=use_aggregator,
-                                    bsz=bsz, tokenwise_score=tokenwise_score,
+                                    bsz=bsz, #tokenwise_score=tokenwise_score,
                                     return_details=return_details)
         return scores