Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+- time-series-forecasting
+- tabular-classification
+tags:
+- finance
+- defi
+- amm
+- ethereum
+- cryptocurrency
+- transaction
+- microstructure
+pretty_name: AMM-Events (Event-Aware DeFi Dataset)
+size_categories:
+- 100M<n<1B
+---
+
+# AMM-Events: A Multi-Protocol DeFi Event Dataset
+
+## Dataset Description
+
+**AMM-Events** is a high-fidelity, block-level dataset capturing **8.9 million on-chain events** from the Ethereum mainnet, specifically designed for event-aware forecasting and market microstructure analysis in Decentralized Finance (DeFi). 
+
+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
+- **Total Events:** 8,917,353
+- **Time Span:** Jan 1, 2024 – Sep 16, 2025
+- **Block Range:** 18,908,896 – 23,374,292
+- **Protocols:** Uniswap V3, Aave, Morpho, Pendle
+- **Granularity:** Block-level timestamps & transaction-level event types
+
+### Supported Tasks
+- **Event Forecasting:** Predicting the next event type (classification/TPP) and time-to-next-event (regression/TPP).
+- **Market Microstructure Analysis:** Analyzing causal synchronization between liquidity events and price shocks.
+- **Anomaly Detection:** Identifying "Black Swan" traffic surges or congestion events.
+
+---
+
+## Dataset Structure
+
+The data is organized into a standardized JSON format. Each entry decouples complex smart contract logic into interpretable metrics.
+
+### Data Fields
+
+- `block_number` (int): The Ethereum block height where the event occurred.
+- `timestamp` (int): Unix timestamp of the block.
+- `transaction_hash` (string): Unique identifier for the transaction.
+- `protocol` (string): Origin protocol (`Uniswap V3`, `Aave`, `Morpho`, or `Pendle`).
+- `event_type` (string): The category of the event (`Swap`, `Mint`, `Burn`, `UpdateImpliedRate`, etc.).
+- `payload` (dict): Protocol-specific metrics (e.g., `amount0`, `amount1`, `liquidity`, `tick` for Uniswap).
+
+### Data Splits
+
+The dataset covers **359 liquidity pools** selected for high activity and representativeness:
+- **Pendle:** 296 pools (Yield Trading)
+- **Aave:** 53 pools (Lending)
+- **Uniswap V3:** 5 pools (Spot Trading)
+- **Morpho:** 5 pools (Lending Optimization)
+
+---
+
+## Usage
+
+### Loading the Data
+You can load this dataset directly using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Jackson668/AMM-Events")
+
+# Example: Accessing the first train example
+print(dataset['train'])