[fix] Ensure that the embeddings from hard negative mining are norm…

…alized (#2944)

sentence_transformers/util.py

@@ -714,8 +714,12 @@ def mine_hard_negatives(
         except Exception:
             pass
 
-        corpus_embeddings = model.encode(corpus, batch_size=batch_size, convert_to_numpy=True, show_progress_bar=True)
-        query_embeddings = model.encode(queries, batch_size=batch_size, convert_to_numpy=True, show_progress_bar=True)
+        corpus_embeddings = model.encode(
+            corpus, batch_size=batch_size, normalize_embeddings=True, convert_to_numpy=True, show_progress_bar=True
+        )
+        query_embeddings = model.encode(
+            queries, batch_size=batch_size, normalize_embeddings=True, convert_to_numpy=True, show_progress_bar=True
+        )
         index.add(corpus_embeddings)
 
         scores_list = []
@@ -731,8 +735,12 @@ def mine_hard_negatives(
 
     else:
         # Embed the corpus and the queries
-        corpus_embeddings = model.encode(corpus, batch_size=batch_size, convert_to_numpy=True, show_progress_bar=True)
-        query_embeddings = model.encode(queries, batch_size=batch_size, convert_to_numpy=True, show_progress_bar=True)
+        corpus_embeddings = model.encode(
+            corpus, batch_size=batch_size, normalize_embeddings=True, convert_to_numpy=True, show_progress_bar=True
+        )
+        query_embeddings = model.encode(
+            queries, batch_size=batch_size, normalize_embeddings=True, convert_to_numpy=True, show_progress_bar=True
+        )
         scores = model.similarity(query_embeddings, corpus_embeddings).to(device)
 
         # Keep only the range_max + max_positives highest scores. We offset by 1 to potentially include the positive pair