question answer
t5-base-finetuned-question-answeringt5-large-generation-squad-QuestionAnsweraisha-partha_-_medical-question-and-answer-gpt2-ggufmaheshhuggingface-Medical-Data-Question-Answers-finetuned-gpt2-GGUF-smashedMedical-Data-Question-Answers-finetuned-gpt2-i1-GGUFBabitak18_-_medical-question-answer-ggufquestion_answering_v2Medical-Data-Question-Answers-finetuned-gpt2-GGUF
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.stackexchange-question-answering
SYNTHETIC-1
This is a subset of the task data used to construct SYNTHETIC-1. You can find the full collection here
reddit_question_best_answersQuestion & question body together with the best answers to that question from Reddit.
The score for the question / answer is the upvote count (i.e. positive-negative upvotes).
Only questions / answers that have these properties were extracted:
min_score = 3
min_title_len = 20
min_body_len = 100
cybersecurity_full_question_answers