Update README.md

README.md

@@ -4,7 +4,7 @@
 
 ## Dataset Summary
 
-**CompRealVul_LLVM** is the LLVM IR (Intermediate Representation) version of the [CompRealVul](https://huggingface.co/datasets/compAgent/CompRealVul) dataset. This version is designed specifically for **training and evaluating machine learning models** on the task of **binary vulnerability detection** in a setting that closely mimics how models are used in practice — operating on the compiled representation of code rather than raw source code.
+**CompRealVul_LLVM** is the LLVM IR (Intermediate Representation) version of the [CompRealVul](https://huggingface.co/datasets/compAgent/CompRealVul_C) dataset. This version is designed specifically for **training and evaluating machine learning models** on the task of **binary vulnerability detection** in a setting that closely mimics how models are used in practice — operating on the compiled representation of code rather than raw source code.
 
 Each function in this dataset was compiled from C code to LLVM IR, enabling robust training of models on semantically rich, architecture-independent binary representations.
 
@@ -16,7 +16,6 @@ This dataset supports research aligned with the methodology described in our pap
 - ✅ Includes **train**, **validation**, and **test** splits (see below)
 - ✅ Vulnerability labels (`label`) for supervised learning
 - ✅ Metadata about original source (`dataset`, `file`, `fun_name`)
-- ✅ Structured as **Parquet** files for fast loading and processing
 
 ## Dataset Structure
 
@@ -37,9 +36,6 @@ This dataset is split into **train**, **validation**, and **test** sets, followi
 - `validation`: Used for model selection and hyperparameter tuning
 - `test`: Used exclusively for final evaluation and benchmarking
 
-## Format
-
-This dataset is provided in [Apache Parquet](https://parquet.apache.org/) format under the `default` configuration. It follows the [Croissant schema](https://mlcommons.org/croissant/) and includes three predefined splits.
 
 ## Usage
 
@@ -55,10 +51,10 @@ print(train_ds[0])
 ## Example
 ```json
 {
-  "dataset": "Juliet",
-  "file": "CWE121/s01.c",
-  "fun_name": "bad_function",
-  "llvm_ir_function": "define dso_local i32 @bad_function() #0 { ... }",
+  "dataset": "CompRealVul",
+  "file": "app_122.c",
+  "fun_name": "app",
+  "llvm_ir_function": "define dso_local i32 @app() #0 { ... }",
   "label": "1",
   "split": "train"
 }