Omkar2118/datasets huggingface dataset:
DiscoSense

**DiscoSense** is a benchmark sourced from datasets that contain two sentences connected through a discourse connective. Specifically, it is sourced from two peer reviewed academic datasets, DISCOVERY and DISCOFUSE for commonsense reasoning via understanding a wide variety of discourse connectives.

Source: [DiscoSense: Commonsense Reasoning with Discourse Connectives](https://arxiv.org/pdf/2210.12478v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.12478v1.pdf](https://arxiv.org/pdf/2210.12478v1.pdf)

Introducing paper: DiscoSense: Commonsense Reasoning with Discourse Connectives
Phee

**Phee** is a dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature. It is designed for biomedical event extraction tasks.

Source: [PHEE: A Dataset for Pharmacovigilance Event Extraction from Text](https://arxiv.org/pdf/2210.12560v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.12560v1.pdf](https://arxiv.org/pdf/2210.12560v1.pdf)

Introducing paper: PHEE: A Dataset for Pharmacovigilance Event Extraction from Text
HuPR
Full name: Human Pose with Millimeter Wave Radar

**HuPR** is a human pose estimation benchmark is created using cross-calibrated mmWave radar sensors and a monocular RGB camera for cross-modality training of radar-based human pose estimation. This dataset contains 235 sequences of data in an indoor environment, with each sequence being one-minute long and totalling about 4 hour-long video data.

Source: [HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar](https://arxiv.org/pdf/2210.12564v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.12564v1.pdf](https://arxiv.org/pdf/2210.12564v1.pdf)

Introducing paper: HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar
YouwikiHow

**YouwikiHow** is a dataset for Weakly-Supervised temporal Article Grounding (WSAG). It contains 47K videos and an average of 20.8 query sentences for each video.

Source: [Weakly-Supervised Temporal Article Grounding](https://arxiv.org/pdf/2210.12444v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.12444v1.pdf](https://arxiv.org/pdf/2210.12444v1.pdf)

Introducing paper: Weakly-Supervised Temporal Article Grounding