probs

app.py

@@ -22,9 +22,8 @@ def askcpv(description):
     encoding = {k: v.to(model.device) for k,v in encoding.items()}
     outputs = model(**encoding)
     sigmoid = torch.nn.Sigmoid()
-    probs = sigmoid(logits.squeeze().cpu())
-    probabilites = torch.nn.functional.softmax(out[0], dim=0)
-    values, indices = torch.topk(probabilites, k=10)
+    probs = sigmoid(outputs.logits.squeeze().cpu())
+    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]
     # return predicted_labels