Update README.md

README.md

@@ -35,7 +35,12 @@ from transformers import AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("ddmitov/bge_m3_dense_colbert_onnx")
 ort_session = ort.InferenceSession("model.onnx")
 
-inputs = tokenizer("BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", padding="longest", return_tensors="np")
+inputs = tokenizer(
+  "BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
+  padding="longest",
+  return_tensors="np"
+)
+
 inputs_onnx = {k: ort.OrtValue.ortvalue_from_numpy(v) for k, v in inputs.items()}
 
 outputs = ort_session.run(None, inputs_onnx)