Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: hash
-    dtype: string
-  - name: failure_reason
-    dtype: string
-  - name: block_number
-    dtype: int64
-  - name: from_address
-    dtype: string
-  - name: to_address
-    dtype: string
-  - name: tx_input
-    dtype: string
-  - name: gas_used
-    dtype: int64
-  - name: gas_price
-    dtype: int64
-  - name: gas_limit
-    dtype: int64
-  - name: value
-    dtype: int64
-  - name: tx_index
-    dtype: int64
-  - name: failure_message
-    dtype: string
-  - name: failure_invariant
-    dtype: string
-  - name: tenderly_src
-    dtype: bool
-  - name: etherscan_src
-    dtype: bool
-  - name: failure_file
-    dtype: string
-  - name: failure_function
-    dtype: string
-  - name: failure_contract
-    dtype: string
-  - name: timestamp
-    dtype: string
-  splits:
-  - name: evaluation
-    num_bytes: 36121089
-    num_examples: 20000
-  - name: finetuning
-    num_bytes: 177872231
-    num_examples: 99999
-  download_size: 56060290
-  dataset_size: 213993320
-configs:
-- config_name: default
-  data_files:
-  - split: evaluation
-    path: data/evaluation-*
-  - split: finetuning
-    path: data/finetuning-*
+pretty_name: "Dataset for the paper: ``RAVEN: Analyzing Ethereum’s Reverted Transactions via Semantic Clustering of Failure Invariants``"
+license: "cc-by-4.0"
+language:
+  - "en"
+tags:
+  - smart-contracts
+  - ethereum
+  - blockchain
+  - transaction-failures
+  - invariants
+task_categories:
+  - tabular-classification  # Changed from anomaly-detection and classification
+size_categories:
+  - 10K<n<100K
+  - 100K<n<1M
+source_datasets:
+  - ethereum-blockchain-transactions
 ---
+
+
+# Dataset Card for **RAVEN: Analyzing Ethereum’s Reverted Transactions via Semantic Clustering of Failure Invariants**
+
+## Dataset Description  
+This dataset comprises two collections (splits) of failed transactions on the Ethereum blockchain, annotated with extracted *business‑logic invariants*. The dataset was created within the research project titled HighGuard: Cross‑Chain Business Logic Monitoring of Smart Contracts, by Mojtaba Eshghie.
+
+- **Finetuning collection**: ~100,000 failed Ethereum transactions annotated with 1,932 unique invariants.  
+- **Evaluation collection**: ~20,000 sampled failed transactions annotated with 727 unique invariants, used for clustering and categorization evaluation.
+
+Each record corresponds to a failed transaction, along with metadata such as transaction hash, block number, sender/receiver, gas used/limit, failure message, and extracted invariant condition that caused the failure.
+
+### Key features  
+- Focused on **business‐logic vulnerabilities**, not only low‑level errors (e.g., out‑of‑gas) but semantic violations captured via invariants.  
+- Two distinct collections (finetuning + evaluation) for training and benchmarking.  
+- Designed for anomaly‑detection and classification tasks in the smart‑contract security domain.
+
+### Recommended uses  
+- Training supervised or unsupervised models to detect business‑logic failures in smart contracts.  
+- Clustering sampled invariants to categorize common failure types.  
+- Benchmarking research on smart‐contract verification, transaction analysis, and runtime monitoring.
+
+### Out‑of‑Scope uses  
+- This dataset is **not** suitable for general cryptocurrency transaction modelling (e.g., normal transfers), since **only failed transactions** are included.  
+- It is **not** a comprehensive dataset of all Ethereum transactions — only those with business‐logic failure annotations.
+
+---
+
+## Dataset Structure  
+The dataset is provided as a `DatasetDict` with two splits/collections:
+
+| Split           | Description                                           | Approx. Size     |
+|-----------------|-------------------------------------------------------|------------------|
+| `finetuning`    | 100 000 failed transactions annotated with 1 932 invariants | ~100k rows       |
+| `evaluation`    | 20 000 failed transactions annotated with 727 invariants     | ~20k rows        |
+
+Each record has the following columns:
+
+- `tx_hash` (string): Transaction hash.  
+- `block_number` (int64): Block number in which the transaction was included.  
+- `from_address` (string): Sender Ethereum address.  
+- `to_address` (string): Receiver Ethereum address.  
+- `gas_limit` (int64): Gas limit specified for the transaction.  
+- `gas_used` (int64): Gas used by the transaction before failure.  
+- `failure_message` (string): The revert or failure message (if available).  
+- `invariant_condition` (string): A high‐level invariant representing the business‐logic violation.  
+- `invariant_id` (int64): An internal identifier for the extracted invariant cluster/category.  
+- `timestamp` (int64): Unix timestamp of the block (optional).  
+
+**File format:** The repository provides Parquet files for each split (`finetuning.parquet`, `evaluation.parquet`) and can be loaded via the `datasets` library as:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("MojtabaEshghie/raven‑dataset", split="finetuning")
+````
+
+---
+
+## Dataset Creation
+
+### Curation Rationale
+
+Business‐logic failures in smart contracts are harder to detect than low‐level exceptions (e.g., out‑of‑gas) but are critically important for security. The goal of this dataset is to provide a curated collection of failed transactions with extracted invariants to enable anomaly detection, clustering, and classification research in the smart‐contract domain.
+
+
+### Data Processing
+
+* Filtering of failed transactions with revert/failure messages.
+* Extraction of business‑logic invariants via the tool SoliDiffy and other analysis pipelines.
+* Deduplication of similar invariant texts and clustering of invariants to create `invariant_id`.
+* Serialization into Parquet format; conversion to Arrow format by the `datasets` library during upload.
+
+### Who/When/Where
+
+* Curated by: Mojtaba Eshghie
+* Affiliation: KTH Royal Institute of Technology, Umeå University.
+* Date: Nov 2025
+
+---
+
+## Considerations for Using the Data
+
+### Limitations
+
+* **Bias toward failures only**: The dataset contains only failed transactions, so models trained on it might not generalize to normal transactions.
+* **Time cutoff**: Transactions are up to a certain block number.
+
+### Ethical and Privacy Considerations
+
+* The data is sourced from a public blockchain (Ethereum), so transaction data is publicly available.
+* Addresses are included (sender/receiver), which are pseudonymous but publicly traceable; users should be aware of potential linking to identities through external sources.
+* Use responsibly: do not attempt to de‑anonymize addresses or misuse user data.
+
+### Recommendations
+
+* If using for supervised classification, consider balancing via sampling or weighting due to potentially unbalanced invariant categories.
+* For anomaly detection, consider using the `finetuning` split for training and `evaluation` for benchmarking.
+* Always cite the dataset and the associated paper when using it in publications.
+
+---
+
+## Citation
+
+```bibtex
+tbd
+```