Add link to paper and GitHub repository

README.md

@@ -1,8 +1,11 @@
 ---
 license: mit
+size_categories:
+- 100M<n<1B
 task_categories:
 - time-series-forecasting
 - tabular-classification
+pretty_name: AMM-Events (Event-Aware DeFi Dataset)
 tags:
 - finance
 - defi
@@ -11,9 +14,6 @@ tags:
 - cryptocurrency
 - transaction
 - microstructure
-pretty_name: AMM-Events (Event-Aware DeFi Dataset)
-size_categories:
-- 100M<n<1B
 ---
 
 # AMM-Events: A Multi-Protocol DeFi Event Dataset
@@ -24,7 +24,8 @@ size_categories:
 
 Unlike traditional financial datasets based on Limit Order Books (LOB), this dataset focuses on **Automated Market Makers (AMMs)**, where price dynamics are triggered exclusively by discrete on-chain events (e.g., swaps, mints, burns) rather than continuous off-chain information.
 
-- **Paper Title:** Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols
+- **Paper:** [Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols](https://huggingface.co/papers/2604.20374)
+- **Code:** [https://github.com/yosen-king/Deep-AMM-Events](https://github.com/yosen-king/Deep-AMM-Events)
 - **Total Events:** 8,917,353
 - **Time Span:** Jan 1, 2024 – Sep 16, 2025
 - **Block Range:** 18,908,896 – 23,374,292
@@ -72,4 +73,5 @@ from datasets import load_dataset
 dataset = load_dataset("Jackson668/AMM-Events")
 
 # Example: Accessing the first train example
-print(dataset['train'])
+print(dataset['train'][0])
+```