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@@ -106,15 +106,8 @@ outputs = session.run(None, {
     "attention_mask": inputs["attention_mask"].astype(np.int64),
 })
 
-# Mean pooling
-hidden_states = outputs[0]  # (1, seq_len, 256)
-attention_mask = inputs["attention_mask"]
-mask_expanded = np.expand_dims(attention_mask, -1)
-sum_embeddings = np.sum(hidden_states * mask_expanded, axis=1)
-sum_mask = np.sum(mask_expanded, axis=1)
-embedding = sum_embeddings / sum_mask  # (1, 256)
-
-# L2 normalize
+# Model outputs pooled embeddings, just L2 normalize
+embedding = outputs[0]  # (1, 256)
 embedding = embedding / np.linalg.norm(embedding, axis=1, keepdims=True)
 ```