Omkar2118/datasets huggingface dataset:
TrustLLM dataset

includes train, test and validation set

Introducing paper: Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications
Sieve & Swap - HowTo100M (Cooking)

Procedural videos show step-by-step demonstrations of tasks like recipe preparation. Understanding such videos is challenging, involving the precise localization of steps and the generation of textual instructions. Manually annotating steps and writing instructions is costly, which limits the size of current datasets and hinders effective learning. Leveraging large but noisy video-transcript datasets for pre-training can boost performance, but demands significant computational resources. Furthermore, transcripts contain irrelevant content and exhibit style variation compared to instructions written by human annotators. To mitigate both issues, we propose a technique, Sieve-&-Swap, to automatically curate a smaller dataset: (i) Sieve filters irrelevant transcripts and (ii) Swap enhances the quality of the text instruction by automatically replacing the transcripts with human-written instructions from a text-only recipe dataset. The curated dataset, three orders of magnitude smaller than current webscale datasets, enables efficient training of large-scale models with competitive performance.

Introducing paper: Efficient Pre-training for Localized Instruction Generation of Videos
AntM2C
Full name: Ant-Group Multi-Scenario Multi-Modal CTR dataset

We release  a large-scale Multi-Scenario Multi-Modal CTR dataset
named AntM2C, built from real industrial data from Alipay. This
dataset offers an impressive breadth and depth of information, covering CTR data from four diverse business scenarios, including advertisements, consumer coupons, mini-programs, and videos. Unlike existing datasets, AntM2C provides not only ID-based features
but also five textual features and one image feature for both users
and items, supporting more delicate multi-modal CTR prediction.

This dataset is from 110 million user exposure-click samples in 5 scenarios on the Alipay APP, including 670,000 users and 180,000 items, with 37 features. For more detailed information, please see the homepage: https://www.atecup.cn/dataSetDetailOpen/1