Omkar2118/datasets huggingface dataset:
IEEE CIS Fraud Detection
Full name: IEEE CIS Fraud Detection (Vesta)

The Vesta dataset was released for use in the IEEE CIS Fraud Detection competition. It contains 590,540 card transactions, 20,663 of which are fraudulent (3.5%). Each
transaction has 431 features (400 numerical, 31 categorical), along with the relative
timestamp and a label of whether it was fraudulent or legitimate. For anonymization
purposes, the names of the identity features have been masked, along with the names of
the extra features engineered by Vesta.
Tamil Memes

Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes, which in most cases combines an image with a concept or catchphrase. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. To facilitate the computational modelling of trolling in the memes for Indian languages, we created a meme dataset for Tamil (TamilMemes). We annotated and released the dataset containing suspected trolls and not-troll memes. In this paper, we use the a image classification to address the difficulties involved in the classification of troll memes with the existing methods. We found that the identification of a troll meme with such an image classifier is not feasible which has been corroborated with precision, recall and F1-score.

Introducing paper: A Dataset for Troll Classification of TamilMemes