diff --git a/examples/bge/bge-small-en-v1.5.json b/examples/bge/bge-small-en-v1.5_ptq_qnn.json
similarity index 100%
rename from examples/bge/bge-small-en-v1.5.json
rename to examples/bge/bge-small-en-v1.5_ptq_qnn.json
diff --git a/examples/bge/user_script.py b/examples/bge/user_script.py
index b16baff02..fa4dee622 100644
--- a/examples/bge/user_script.py
+++ b/examples/bge/user_script.py
@@ -18,8 +18,6 @@ def __init__(self, model, session):
         self.model = model
         self.session = session
         self.tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-small-en-v1.5")
-        self.total = 0
-        self.max_len = 0
 
     def encode(self, corpus: List, **kwargs):
         if self.model.framework == Framework.ONNX:
@@ -44,28 +42,30 @@ def encode(self, corpus: List, **kwargs):
                 "attention_mask": encoded_input.attention_mask,
                 "token_type_ids": encoded_input.token_type_ids,
             }
-            self.max_len = max(self.max_len, model_inputs["input_ids"].shape[1])
-            print(self.max_len)
             with torch.no_grad():
                 model_output = self.model.run_session(self.session, model_inputs)
             model_output = model_output.last_hidden_state.numpy()
         # select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
-        model_output = model_output[:, 0, :]
-        self.total += len(corpus)
-        print(self.total)
-        return model_output
+        return model_output[:, 0, :]
 
 
 def eval_accuracy(model: OliveModelHandler, device, execution_providers, tasks):
     sess = model.prepare_session(inference_settings=None, device=device, execution_providers=execution_providers)
 
     evaluation = mteb.MTEB(tasks=tasks)
-    oliveEncoder = OliveEncoder(model, sess)
-    results = evaluation.run(oliveEncoder, output_folder=None)
+    olive_encoder = OliveEncoder(model, sess)
+    results = evaluation.run(olive_encoder, output_folder=None)
     return results[0].scores["test"][0]["main_score"]
 
 
 if __name__ == "__main__":
+    import logging
+    import sys
+
+    logger = logging.getLogger("bge")
+    logger.addHandler(logging.StreamHandler(sys.stdout))
+    logger.setLevel(logging.INFO)
+
     # Greedy search for the best combination of ops to quantize
     all_ops = [
         "Mul",
@@ -87,7 +87,7 @@ def eval_accuracy(model: OliveModelHandler, device, execution_providers, tasks):
     target_accuracy = 0.8
     with Path("bge-small-en-v1.5.json").open() as fin:
         olive_config = json.load(fin)
-    for i, op in enumerate(all_ops):
+    for op in all_ops:
         if op in olive_config["passes"]["OnnxQuantization"]["op_types_to_quantize"]:
             continue
         olive_config["passes"]["OnnxQuantization"]["op_types_to_quantize"].append(op)
@@ -95,7 +95,9 @@ def eval_accuracy(model: OliveModelHandler, device, execution_providers, tasks):
         footprint: Footprint = next(iter(result.values()))
         node: FootprintNode = next(iter(footprint.nodes.values()))
         accuracy = node.metrics.value["accuracy-accuracy_custom"].value
-        print(f"Ops: {olive_config["passes"]["OnnxQuantization"]["op_types_to_quantize"]} Accuracy: {accuracy}")
+        logger.info(
+            "Ops: %s Accuracy: %f", olive_config["passes"]["OnnxQuantization"]["op_types_to_quantize"], accuracy
+        )
         if accuracy < target_accuracy:
             olive_config["passes"]["OnnxQuantization"]["op_types_to_quantize"].remove(op)
-    print(f"Used Ops: {olive_config["passes"]["OnnxQuantization"]["op_types_to_quantize"]}")
+    logger.info("Final Ops: %s", olive_config["passes"]["OnnxQuantization"]["op_types_to_quantize"])