README.md

---
language:
- en
license: mit
size_categories:
- 1M<n<10M
task_categories:
- text-generation
paperswithcode_id: disl
pretty_name: 'DISL: Fueling Research with A Large Dataset of Solidity Smart Contracts'
configs:
- config_name: decomposed
  data_files: data/decomposed/*
- config_name: raw
  data_files: data/raw/*
tags:
- code
- solidity
- smart contracts
- webdataset
---

# DISL

The DISL dataset features a collection of 514506 unique Solidity files that have been deployed to Ethereum mainnet. It caters to the need for a large and diverse dataset of real-world smart contracts. DISL serves as a resource for developing machine learning systems and for benchmarking software engineering tools designed for smart contracts.

## Content

* the *raw* subset has full contracts source code and it's not deduplicated, it has 3,298,271 smart contracts
* the *decomposed* subset contains Solidity files, it is derived from raw, it is deduplicated using Jaccard similarity with a threshold of 0.9, it has 514,506 Solidity files
* the cuttoff date is January 15, 2024, approx at block [19010000](https://etherscan.io/block/19010000)

If you use DISL, please cite the following tech report:

```bibtex
@techreport{disl2403.16861,
 title = {DISL: Fueling Research with A Large Dataset of Solidity Smart Contracts},
 year = {2024},
 author = {Gabriele Morello and Mojtaba Eshghie and Sofia Bobadilla and Martin Monperrus},
 url = {http://arxiv.org/pdf/2403.16861},
 number = {2403.16861},
 institution = {arXiv},
}
```

- **Curated by:** Gabriele Morello



## Instructions to explore the dataset

```python
from datasets import load_dataset

# Load the raw dataset
dataset = load_dataset("ASSERT-KTH/DISL", "raw")

# OR

# Load the decomposed dataset
dataset = load_dataset("ASSERT-KTH/DISL", "decomposed")

# number of rows and columns
num_rows = len(dataset["train"])
num_columns = len(dataset["train"].column_names)

# random row
import random
random_row = random.choice(dataset["train"])

# random source code
random_sc = random.choice(dataset["train"])['source_code']
print(random_sc)
```