Omkar2118/datasets huggingface dataset:
AGB-DE

AGB-DE is a legal NLP corpus for the automated detection of potentially void clauses in German standard form consumer contracts. It consists of 3,764 clauses that have been legally assessed by experts and annotated as potentially void (1) or valid (0). Additionally, each clause is annotated with a topic label.

Introducing paper: AGB-DE: A Corpus for the Automated Legal Assessment of Clauses in German Consumer Contracts
CAsT-answerability

CAsT-answerability dataset contains binary answerability labels on three levels: sentence, passage, and ranking. It contains around 1.8k answerable and 1.9k unanswerable
question-passage pairs. Sentence- and passage-level answerability is divided into train (90%), and test (10%) portions; the splitting is done on the question level to avoid information leakage. Ranking-level answerability has only a test set.
CAsT-snippets

CAsT-snippets is a high-quality dataset for conversational information seeking containing snippet-level annotations for all queries in the TREC CAsT 2020 and 2022 datasets. It enables the development of answer generation methods that are grounded in relevant snippets in paragraphs as well as allows for the automatic evaluation of the generated answers in terms of completeness; a training/test split is provided for such use.
bhinneka-korpus

A Collection of Multilingual Parallel Datasets for 5 Indonesian Local Languages

Introducing paper: Constructing and Expanding Low-Resource and Underrepresented Parallel Datasets for Indonesian Local Languages
CharXiv

CharXiv is a comprehensive evaluation suite for testing the chart understanding capabilities of Multimodal Large Language Models (MLLMs)¹². It was proposed to address the limitations of existing datasets that often focus on oversimplified and homogeneous charts with template-based questions¹².

Here are some key features of CharXiv:
- It includes **2,323 natural, challenging, and diverse charts** from arXiv papers¹².
- CharXiv includes two types of questions¹²:
    1. **Descriptive questions** about examining basic chart elements.
    2. **Reasoning questions** that require synthesizing information across complex visual elements in the chart.
- All charts and questions are **handpicked, curated, and verified by human experts**¹².

The results from CharXiv reveal a substantial gap between the reasoning skills of the strongest proprietary model (i.e., GPT-4o), which achieves 47.1% accuracy, and the strongest open-source model (i.e., InternVL Chat V1.5), which achieves 29.2%². All models lag far behind human performance of 80.5%, underscoring weaknesses in the chart understanding capabilities of existing MLLMs².

(1) [2406.18521] CharXiv: Charting Gaps in Realistic Chart Understanding in .... https://arxiv.org/abs/2406.18521.
(2) CharXiv. https://charxiv.github.io/.
(3) ChinaXiv.org 中国科学院科技论文预发布平台. https://chinaxiv.org/home.htm.
(4) undefined. https://doi.org/10.48550/arXiv.2406.18521.

Introducing paper: CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
f5C Dataset
Full name: 5-Formylcytidine Modifications on mRNA Dataset

This is a dataset for predicting 5-Formylcytidine Modifications on mRNA.

Introducing paper: F5C-finder: An Explainable and Ensemble Biological Language Model for Predicting 5-Formylcytidine Modifications on mRNA