Omkar2118/datasets huggingface dataset:
Verified Smart Contracts

**Verified Smart Contracts** is a dataset of real Ethereum smart contracts, containing both Solidity and Vyper source code. It consists of every deployed Ethereum smart contract as of 1st of April 2022, whose been verified on [Etherscan](https://etherscan.io/) and has a least one transaction. A total of 186,397 unique smart contracts are provided, filtered down from 2,217,692 smart contracts. The dataset contains 53,843,305 lines of code.

Introducing paper: Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding
Verified Smart Contract Code Comments

**Verified Smart Contracts Code Comments** is a dataset of real Ethereum smart contract functions, containing "code, comment" pairs of both Solidity and Vyper source code. The dataset is based on every deployed Ethereum smart contract as of 1st of April 2022, whose been verified on [Etherscan](https://etherscan.io/) and has a least one transaction. A total of 1,541,370 smart contract functions are provided, parsed from 186,397 unique smart contracts, filtered down from 2,217,692 smart contracts.

Introducing paper: Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding
Vulnerable Verified Smart Contracts

**Vulnerable Verified Smart Contracts** is a dataset of real vulnerable Ethereum smart contracts. Based on the manually labeled [Benchmark dataset of Solidity smart contracts](https://doi.org/10.5281/zenodo.7744053). A total of 609 vulnerable contracts are provided, containing 1,117 vulnerabilities.

Introducing paper: Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding
maze-dataset

This package provides utilities for generation, filtering, solving, visualizing, and processing of mazes for training ML systems. Primarily built for the [maze-transformer interpretability](https://github.com/understanding-search/maze-transformer) project. You can find our paper on it here: http://arxiv.org/abs/2309.10498

This package includes a variety of [maze generation algorithms](maze_dataset/generation/generators.py), including randomized depth first search, Wilson's algorithm for uniform spanning trees, and percolation. Datasets can be filtered to select mazes of a certain length or complexity, remove duplicates, and satisfy custom properties. A variety of output formats for visualization and training ML models are provided.

Introducing paper: A Configurable Library for Generating and Manipulating Maze Datasets