diff --git a/dvc.yaml b/dvc.yaml
index 4dbf7e1..cfe7a72 100644
--- a/dvc.yaml
+++ b/dvc.yaml
@@ -37,7 +37,7 @@ stages:
     outs:
     - ${files.chunked}
   create-embeddings:
-    cmd: python scripts/create_embeddings.py ${files.chunked} ${files.embeddings}
+    cmd: python scripts/create_embeddings.py ${files.chunked} ${files.embeddings} -m ${hp.embeddings-model}
     deps:
     - ${files.chunked}
     - scripts/create_embeddings.py
diff --git a/scripts/create_embeddings.py b/scripts/create_embeddings.py
index 1ae255e..945c79b 100644
--- a/scripts/create_embeddings.py
+++ b/scripts/create_embeddings.py
@@ -1,23 +1,25 @@
 import gc
 import json
 from argparse import ArgumentParser
+from itertools import batched
 
 import torch
 from sentence_transformers import SentenceTransformer
-from torch import Tensor
 from tqdm import tqdm
 
 
-def create_embedding(text: str) -> Tensor:
-    model = SentenceTransformer("all-MiniLM-L6-v2")
-    return model.encode(text)
-
-
-def main(input_file: str, output_file: str) -> None:
+def main(input_file: str, output_file: str, model_name: str) -> None:
+    model = SentenceTransformer(model_name)
     with open(input_file) as input, open(output_file, "w") as output:
         data = json.load(input)
-        for chunk in tqdm(data):
-            chunk["embedding"] = create_embedding(chunk["chunk"]).tolist()
+        batches = list(batched(data, 500))
+        position = 0
+        for batch in tqdm(batches):
+            texts = [chunk["chunk"] for chunk in batch]
+            embeddings = model.encode(texts)
+            for embedding in embeddings:
+                data[position]["embedding"] = embedding.tolist()
+                position += 1
             gc.collect()
             torch.cuda.empty_cache()
         json.dump(data, output)
@@ -27,5 +29,8 @@ def main(input_file: str, output_file: str) -> None:
     parser = ArgumentParser("prepare_data.py")
     parser.add_argument("input", help="The file to be used as input.")
     parser.add_argument("output", help="The path to save the processed result.")
+    parser.add_argument(
+        "-m", "--model", help="Embedding model to use.", default="all-MiniLM-L6-v2"
+    )
     args = parser.parse_args()
-    main(args.input, args.output)
+    main(args.input, args.output, args.model)