coh mech

ccl_win.py

@@ -133,19 +133,21 @@ class ccl_win(evaluate.Measurement):
 
         model = AutoModelForSequenceClassification.from_pretrained(os.path.join(BASEDIR,dataset))
         model.to(device)
+        model.eval()
 
         pred_list,len_by_sample = self.preprocess_adjacent_window(predictions)
 
         scores = []
         n_preds = len(pred_list)
-        for b in range(0,n_preds,batch_size):
-            strides = [x.lower() for x in pred_list[b:b+batch_size]]
-            tinput = tokenizer(strides,padding=True,truncation=True,max_length=512,return_tensors="pt")
-            tinput = {k:v.to(device) for k,v in tinput.items()}
-            output = model(**tinput)
-            probs = torch.softmax(output.logits,dim=-1).detach().cpu().numpy()
-            scores.extend(probs[:,0].tolist())
-        #
+        with torch.no_grad():
+            for b in range(0,n_preds,batch_size):
+                strides = [x.lower() for x in pred_list[b:b+batch_size]]
+                tinput = tokenizer(strides,padding=True,truncation=True,max_length=512,return_tensors="pt")
+                tinput = {k:v.to(device) for k,v in tinput.items()}
+                output = model(**tinput)
+                probs = torch.softmax(output.logits,dim=-1).detach().cpu().numpy()
+                scores.extend(probs[:,0].tolist())
+            #
         results = []
         offset = 0
         for _len in len_by_sample: