Update README.md

README.md

@@ -10,6 +10,7 @@ size_categories:
 
 # TAPAS: Datasets for Learning the Learning with Errors Problem
 
+## About this Data
 AI-powered attacks on Learning with Errors (LWE)—an important hard math problem in post-quantum cryptography—rival or outperform "classical" attacks on LWE under certain parameter settings. Despite the promise of this approach, a dearth of accessible data limits AI practitioners' ability to study and improve these attacks. Creating LWE data for AI model training is time- and compute-intensive and requires significant domain expertise. To fill this gap and accelerate AI research on LWE attacks, we propose the TAPAS datasets, a **t**oolkit for **a**nalysis of **p**ost-quantum cryptography using **A**I **s**ystems. These datasets cover several LWE settings and can be used off-the-shelf by AI practitioners to prototype new approaches to cracking LWE. 
 
 The table below gives an overview of the datasets provided in this work:
@@ -19,4 +20,12 @@ The table below gives an overview of the datasets provided in this work:
 | 512  | 12      | 10       | 0.9036 | 40M        |
 | 512  | 28      | 10       | 0.6740 | 40M        |
 | 512  | 41      | 10       | 0.3992 | 40M        |
-| 1024 | 26      | 10       | 0.8600 | 40M        |
+| 1024 | 26      | 10       | 0.8600 | 40M        |
+
+## Usage
+
+These datasets are intended to be used in conjunction with the code at: https://github.com/facebookresearch/LWE-benchmarking
+
+Download and unzip the .tar.gz files into a directory with enough storage. For the datasets split into different chunks, concatenate all the files into one data.prefix file after unzipping.
+
+Then, follow the instructions in this [README](https://github.com/facebookresearch/LWE-benchmarking/blob/main/README.md) to generate the full sets of LWE pairs and train AI models on this data.