mean_pooling fix

semscore.py

@@ -89,7 +89,8 @@ class SemScore(evaluate.Metric):
         self.model.eval()
         self.tokenizer = AutoTokenizer.from_pretrained(checkpoint)
 
-    def mean_pooling(model_output, attention_mask):
+    @staticmethod
+    def _mean_pooling(model_output, attention_mask):
         """Mean pooling over all tokens - take attention mask into account for correct averaging"""
         token_embeddings = model_output[0]
         input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
@@ -122,8 +123,8 @@ class SemScore(evaluate.Metric):
                 encoded_preds = self.tokenizer(batch_preds, padding=True, truncation=True, return_tensors='pt')
                 model_output_refs = self.model(**encoded_refs.to(device))
                 model_output_preds = self.model(**encoded_preds.to(device))
-                batch_pooled_refs = self.mean_pooling(model_output_refs, encoded_refs['attention_mask'])
-                batch_pooled_preds = self.mean_pooling(model_output_preds, encoded_preds['attention_mask'])
+                batch_pooled_refs = self._mean_pooling(model_output_refs, encoded_refs['attention_mask'])
+                batch_pooled_preds = self._mean_pooling(model_output_preds, encoded_preds['attention_mask'])
                 pooled_refs.append(batch_pooled_refs)
                 pooled_preds.append(batch_pooled_preds)
         pooled_refs, pooled_preds = torch.cat(pooled_refs), torch.cat(pooled_preds)