coh mech

ccl_win.py

@@ -132,6 +132,7 @@ class ccl_win(evaluate.Measurement):
             device = "cuda" if torch.cuda.is_available() else "cpu"
 
         results = []
+        sent_lens = [len(x.split("\n")) for x in predictions]
         aggregator = None
         if use_aggregator:
             np.random.seed(42)
@@ -158,8 +159,8 @@ class ccl_win(evaluate.Measurement):
             #
 
         offset = 0
-        for _len in len_by_sample:
-            score = float(np.mean(scores[offset:offset+_len]))
+        for i,_len in enumerate(len_by_sample):
+            score = float(np.mean(scores[offset:offset+_len])) if sent_lens[i]>1 else 0.
             if use_aggregator:
                 aggregator.add_scores({"loc_coh_ccl": score})
             else: