question-answer
t5-base-finetuned-question-answeringaisha-partha_-_medical-question-and-answer-gpt2-gguft5-large-generation-squad-QuestionAnswermaheshhuggingface-Medical-Data-Question-Answers-finetuned-gpt2-GGUF-smashedBabitak18_-_medical-question-answer-ggufMedical-Data-Question-Answers-finetuned-gpt2-i1-GGUFnlp-toolkit-question_answering-baseMedical-Data-Question-Answers-finetuned-gpt2-GGUF
CEH_question_answerNLU-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-datasetsreddit_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
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
cybersecurity_full_question_answers