Update README.md

README.md

@@ -48,7 +48,7 @@ You can easily load this dataset using the Hugging Face `datasets` library:
 from datasets import load_dataset
 
 # Load the statute corpus
-dataset = load_dataset("vistec-AI/nitibench-statute", split="train")
+dataset = load_dataset("vistec-AI/nitibench-statute", split="ccl")
 
 # Example: Print the first section
 print(dataset[0])
@@ -65,7 +65,7 @@ for row in dataset:
     # Use 'section_content' as the text chunk to be indexed
     documents.append(row['section_content'])
     # Use 'law_code' or a combination of name+section as ID
-    ids.append(row['law_code'])
+    ids.append(f"row['law_code']-row['section_num']")
 
 # ... Proceed to pass `documents` to your VectorDB or Retriever (e.g., FAISS, ChromaDB, BM25)
 ```