Omkar2118/datasets huggingface dataset:
Gambling Address Dataset

**Gambling Address Dataset** is a collection of 10,423 gambling addresses that have transactions with gambling contracts. Moreover, 51,004 non-gambling addresses are also selected (such as exchanges, wallet addresses, etc.), making the gambling address dataset more complete. In the dataset, accounts are used to refer to addresses (e.g. 0xd1ce...edec95), where 1, 0, and -1 represent the gamble, non-gamble, and other types, respectively.

Source: [Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods](https://arxiv.org/pdf/2211.14779v1.pdf)

Image Source: [https://arxiv.org/pdf/2211.14779v1.pdf](https://arxiv.org/pdf/2211.14779v1.pdf)

Introducing paper: Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
Gambling Contract Dataset

**Gambling Contract Dataset** is a collection of 260 gambling smart contracts from decentralized gambling websites, such as Dicether, Degens. At the same time, in order to construct the negative samples required for training, 1040 smart contracts that are not involved in gambling (e.g., erc20, erc721, mixer, etc.) are selected . In the dataset, accounts are used to refer to contracts (e.g. 0x3fe2b...f8a33f), where 1, 0, and -1 to represent the gamble, non-gamble, and other types, respectively.

Source: [Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods](https://arxiv.org/pdf/2211.14779v1.pdf)

Image Source: [https://arxiv.org/pdf/2211.14779v1.pdf](https://arxiv.org/pdf/2211.14779v1.pdf)

Introducing paper: Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
DRTiD

**DRTiD** is a benchmark dataset for DR grading, consisting of 3,100 two-field fundus images.

Source: [Cross-Field Transformer for Diabetic Retinopathy Grading on Two-feld Fundus Images](https://arxiv.org/pdf/2211.14552v1.pdf)

Image Source: [https://github.com/fdu-vts/drtid](https://github.com/fdu-vts/drtid)

Introducing paper: Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images