datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
coqa_mcCoQA is a large-scale dataset for building Conversational Question Answering
systems. The goal of the CoQA challenge is to measure the ability of machines to
understand a text passage and answer a series of interconnected questions that
appear in a conversation.
NOTE: this is the reformulated multiple choice version of the CoQA task, with downsampling.
Defects4JCoq-Changelog-QA
Coq Changelog Q&A Dataset
Dataset Description
The Coq Changelog Q&A Dataset is an extension of the original Coq Changelog Dataset, transforming each changelog entry into two Question–Answer pairs via two distinct prompts. One focuses on a straightforward query about the change itself, while the other aims at the rationale or motivation behind it. By applying both prompts to every changelog entry, the dataset approximately doubles in size compared to the source.… See the full description on the dataset page: https://huggingface.co/datasets/phanerozoic/Coq-Changelog-QA.Coq-HoTT-QA
Coq-HoTT Q&A Dataset
Dataset Description
The Coq-HoTT Q&A Dataset is a conversational extension of the Coq-HoTT Dataset, derived directly from the Coq-HoTT GitHub repository (https://github.com/HoTT/Coq-HoTT). This dataset transforms Homotopy Type Theory (HoTT) content into structured Q&A pairs, bridging the gap between formal mathematics and conversational AI.
Each entry in the dataset represents a mathematical statement, such as a definition or theorem, converted into a… See the full description on the dataset page: https://huggingface.co/datasets/phanerozoic/Coq-HoTT-QA.Coq-UniMath-QA
UniMath Q&A Dataset
Dataset Description
The UniMath Q&A Dataset is a conversational extension of the UniMath Dataset, derived from the UniMath formalization of mathematics (https://github.com/UniMath/UniMath). This dataset transforms Univalent Mathematics content into structured Q&A pairs, making formal mathematical content more accessible through natural language interactions.
Each entry represents a mathematical statement from UniMath (definition, theorem, lemma, etc.)… See the full description on the dataset page: https://huggingface.co/datasets/phanerozoic/Coq-UniMath-QA.coqar-s-uncertainty-passageCodeNet-BCoq-MetaCoq-QA
MetaCoq Q&A Dataset
Dataset Description
The MetaCoq Q&A Dataset is a conversational extension of the MetaCoq Dataset, derived from the MetaCoq formalization of Coq's meta-theory (https://github.com/MetaCoq/metacoq). This dataset transforms meta-theoretical content into structured Q&A pairs, making formal meta-programming and verification concepts more accessible through natural language interactions.
Each entry represents a mathematical statement from MetaCoq (definition… See the full description on the dataset page: https://huggingface.co/datasets/phanerozoic/Coq-MetaCoq-QA.coqar-SEA-answer-span-diversity
Fields
Top-level fields
id — Unique example identifier.
split — Original CoQAR split, e.g. dev.
conversation_id — Identifier of the source CoQAR conversation.
turn_id — Turn index within the conversation.
question — Original conversational question for the current turn.
history — Previous dialogue context stored as a flat sequence:
[question_0, answer_0, question_1, answer_1, ...].
all_standalone_questions — Human-written standalone rewrites of the current… See the full description on the dataset page: https://huggingface.co/datasets/zykov/coqar-SEA-answer-span-diversity.CodeNet-ECVEFixescoqar-s-uncertainty-passage-sentenceSQLR2coqa_chat
Dataset Card for CoQA_Chat
Dataset Description
A data set for training LLMs for in-context or Document Question-Answering conversations.
Point of Contact: info@alderley.ai
Dataset Summary
This dataset is an amended version of the CoQA dataset, with the question responses amended to be more conversational in nature, with a greater emphasis on returning contextually relervant infomration with the answer.
CoQA is a large-scale dataset for building Conversational… See the full description on the dataset page: https://huggingface.co/datasets/AlderleyAI/coqa_chat.CoQASafeCoderKG-CoQA
KG-Driven Ambiguity Generation (Abg-CoQA Extension)
This project focuses on generating single-turn ambiguous and unambiguous questions from Knowledge Graphs (KGs) extracted from 1000 distinct stories in the CoQA dataset.
The core objective is to programmatically create ambiguous questions (where a relationship edge has multiple valid parents) and unambiguous questions (where a relationship edge is unique).
Project Overview
Source Data: 1k distinct stories from CoQA… See the full description on the dataset page: https://huggingface.co/datasets/vikash0132/KG-CoQA.coq_codedata for finetuning using the coq framework for mathematical formalization
DepreAPIcoq_datasetV1Luotuo-QA-A-CoQA-Chinese
Dataset Card for luotuo-QA-A
Dataset Summary
CoQA(Conversational Question Answering)数据集是一个用于对话式问答任务的大规模数据集，包含超过127,000个问题及其对应的答案。这些文本来自七个不同领域的段落：儿童故事、文学作品、中学和高中英语考试、新闻、维基百科、Reddit和Science。
CoQA数据集经过简单清洗，共有7012个story，我们在此基础上将整个数据集翻译成了中文并进行了增广，其中每个story中包含5个左右的问题，每个问题进行了5次增广。
由于此数据集是我们Luotuo-QA项目的一部分，我们将它叫做luotuo-QA-A,旨在促进对话式问答在中文语境下的研究和应用。
您可以在这里查看Luotuo-QA项目：https://github.com/LC1332/Luotuo-QA… See the full description on the dataset page: https://huggingface.co/datasets/silk-road/Luotuo-QA-A-CoQA-Chinese.coqacoq_datasetV2llm4docq-coq