k

app.py

@@ -23,7 +23,7 @@ def askcpv(description):
     encoding = {k: v.to(model.device) for k,v in encoding.items()}
     outputs = model(**encoding)
     sigmoid = torch.nn.Sigmoid()
-    probs = sigmoid(outputs.logits.squeeze().cpu()).cpu().numpy()
+    probs = sigmoid(outputs.logits.squeeze().cpu()).detach().numpy()
     values, indices = torch.topk(probs, k=10)
     # turn predicted id's into actual label names
     # predicted_labels = [id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]