Omkar2118/datasets huggingface dataset:
CiteSum

CiteSum is a large-scale scientific extreme summarization benchmark.

Introducing paper: CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision
Nakdimon-train

A collection of diacritized Hebrew text in a variety of registers and from different sources.

Introducing paper: Restoring Hebrew Diacritics Without a Dictionary
FiNER-139

FiNER-139 is comprised of 1.1M sentences annotated with eXtensive Business Reporting Language (XBRL) tags extracted from annual and quarterly reports of publicly-traded companies in the US. Unlike other entity extraction tasks, like named entity recognition (NER) or contract element extraction, which typically require identifying entities of a small set of common types (e.g., persons, organizations), FiNER-139 uses a much larger label set of 139 entity types. Another important difference from typical entity extraction is that FiNER focuses on numeric tokens, with the correct tag depending mostly on context, not the token itself.

Introducing paper: FiNER: Financial Numeric Entity Recognition for XBRL Tagging
AVCAffe
Full name: A Large Scale Audio-Visual Dataset of Cognitive Load and Affect for Remote Work

We introduce AVCAffe, the first Audio-Visual dataset consisting of Cognitive load and Affect attributes. We record AVCAffe by simulating remote work scenarios over a video-conferencing platform, where subjects collaborate to complete a number of cognitively engaging tasks. AVCAffe is the largest originally collected (not collected from the Internet) affective dataset in English language. We recruit 106 participants from 18 different countries of origin, spanning an age range of 18 to 57 years old, with a balanced male-female ratio. AVCAffe comprises a total of 108 hours of video, equivalent to more than 58,000 clips along with task-based self-reported ground truth labels for arousal, valence, and cognitive load attributes such as mental demand, temporal demand, effort, and a few others. We believe AVCAffe would be a challenging benchmark for the deep learning research community given the inherent difficulty of classifying affect and cognitive load in particular. Moreover, our dataset fills an existing timely gap by facilitating the creation of learning systems for better self-management of remote work meetings, and further study of hypotheses regarding the impact of remote work on cognitive load and affective states.

Introducing paper: AVCAffe: A Large Scale Audio-Visual Dataset of Cognitive Load and Affect for Remote Work
MAG-Scholar-C

MAG-Scholar-C is constructed by Bojchevski et al. based on Microsoft Academic Graph (MAG), in which nodes refer to papers, edges represent citation relations among papers and features are bag-of-words  of paper abstracts.

Introducing paper: Scaling Graph Neural Networks with Approximate PageRank