update normalize method

acge_embedding.py

@@ -1,6 +1,7 @@
 from transformers import AutoModel, AutoTokenizer
 from sklearn.preprocessing import normalize
 import torch
+import torch.nn.functional as F
 
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -43,9 +44,7 @@ def acge_embedding(text: list[str]) -> list[list[float]]:
         ~attention_mask[..., None].bool(), 0.0
     )
     vector = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
-    vector = normalize(
+    # Normalize the output vectors
-        vector.cpu().detach().numpy(),
+    normalized_vector = F.normalize(vector, p=2, dim=1)
-        norm="l2",
+    return normalized_vector.tolist()
-        axis=1,
+
-    )
-    return vector.tolist()