Update README.md

README.md

@@ -59,7 +59,7 @@ text_encoder = AutoModel.from_pretrained("hustcw/clap-text", trust_remote_code=T
 
 ```python
 with open("./CaseStudy/bubblesort.json") as fp:
-asm = json.load(fp)
+    asm = json.load(fp)
 ```
 
 2. Define your classification prompts:
@@ -88,7 +88,7 @@ preds = torch.softmax(logits / 0.07, dim=1).squeeze(0).tolist()
 
 # Output predictions
 for i, prompt in enumerate(prompts):
-  print(f"Probability: {preds[i]*100:.3f}%, Text: {prompt}")
+    print(f"Probability: {preds[i]*100:.3f}%, Text: {prompt}")
 ```
 
 Repeat the process for any other classification tasks you want, such as malware classification and cryptographic algorithm identification, by loading the respective datasets and defining the relevant natural language prompts.