question-answering
NLU-Question-Answering
SEA Question Answering
SEA Question Answering evaluates a model's ability to predict a contiguous span of characters that answers the question about a given passage. It is sampled from TyDi QA-GoldP for Indonesian, IndicQA for Tamil, and XQuaD for Thai and Vietnamese.
Supported Tasks and Leaderboards
SEA Question Answering is designed for evaluating chat or instruction-tuned large language models (LLMs). It is part of the SEA-HELM leaderboard from AI Singapore.… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/NLU-Question-Answering.medical-question-answering-datasetsstackexchange-question-answering
SYNTHETIC-1
This is a subset of the task data used to construct SYNTHETIC-1. You can find the full collection here
Art-Vision-Question-Answering-Dataset
Art Vision Question Answering Dataset
🎨 A curated dataset for training AI models on digital artwork analysis and visual question answering.
Dataset Overview
This dataset contains 577 question-answer pairs extracted from artwork conversations, designed for training multimodal AI models on art analysis tasks.
✨ Key Features
🖼️ Visual Thumbnails: Artwork images displayed directly in the dataset viewer
💬 Rich Q&A: Expert-level questions and answers… See the full description on the dataset page: https://huggingface.co/datasets/OneEyeDJ/Art-Vision-Question-Answering-Dataset.extractive_qa_question_answering_hr
Dataset Card
HR-Multiwoz is a fully-labeled dataset of 5980 extractive qa spanning 10 HR domains to evaluate LLM Agent. It is the first labeled open-sourced conversation dataset in the HR domain for NLP research.
Please refer to HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent for details about the dataset construction.
Dataset Sources
Repository: xwjzds/extractive_qa_question_answering_hr
Paper: HR-MultiWOZ: A Task Oriented Dialogue (TOD)… See the full description on the dataset page: https://huggingface.co/datasets/xwjzds/extractive_qa_question_answering_hr.medical-question-answering-split