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modeling_e1.py

@@ -1581,8 +1581,8 @@ class EmbeddingMixin:
                 with torch.no_grad():
                     for i, batch in tqdm(enumerate(range(0, len(to_embed), batch_size)), desc='Embedding batches'):
                         seqs = to_embed[i:i + batch_size]
-                        input_ids, attention_mask = self._embed(seqs, return_attention_mask=True).float() # sql requires float32
-                        embeddings = get_embeddings(input_ids, attention_mask)
+                        input_ids, attention_mask = self._embed(seqs, return_attention_mask=True) 
+                        embeddings = get_embeddings(input_ids, attention_mask).float() # sql requires float32
                         for seq, emb, mask in zip(seqs, embeddings, attention_mask):
                             if full_embeddings:
                                 emb = emb[mask.bool()].reshape(-1, hidden_size)