Question Answer
t5-large-generation-squad-QuestionAnswert5-base-finetuned-question-answeringmaheshhuggingface-Medical-Data-Question-Answers-finetuned-gpt2-GGUF-smashedaisha-partha_-_medical-question-and-answer-gpt2-ggufMedical-Data-Question-Answers-finetuned-gpt2-i1-GGUFquestion_answering_v2Medical-Data-Question-Answers-finetuned-gpt2-GGUFquestion-answering-roberta-base-s-v2
CEH_question_answermedical-question-answering-datasetsNLU-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.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.Financial_Question_Answeringcybersecurity_full_question_answers