lmqg/qa_squadshifts_synthetic
收藏资源简介：
--- license: cc-by-4.0 pretty_name: Synthetic QA dataset on SQuADShifts. language: en multilinguality: monolingual size_categories: 10K<n<100K source_datasets: - extended|wikipedia task_categories: - question-answering task_ids: - extractive-qa --- # Dataset Card for "lmqg/qa_squadshifts_synthetic" ## Dataset Description - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) - **Point of Contact:** [Asahi Ushio](http://asahiushio.com/) ### Dataset Summary This is a synthetic QA dataset generated with fine-tuned QG models over [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts), made for question-answering based evaluation (QAE) for question generation model proposed by [Zhang and Bansal, 2019](https://aclanthology.org/D19-1253/). The test split is the original validation set of [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts), where the model should be evaluate on. ### Supported Tasks and Leaderboards * `question-answering` ### Languages English (en) ## Dataset Structure ### Data Fields The data fields are the same among all splits. #### plain_text - `id`: a `string` feature of id - `title`: a `string` feature of title of the paragraph - `context`: a `string` feature of paragraph - `question`: a `string` feature of question - `answers`: a `json` feature of answers ### Data Splits TBA ## Citation Information ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
--- 许可证：CC BY 4.0 规范名称：基于SQuADShifts的合成问答（QA）数据集 语言：英语 多语言属性：单语种 规模区间：1万至10万条数据 源数据集： - 扩展维基百科数据集 任务类别： - 问答（Question Answering） 任务子类型： - 抽取式问答（Extractive QA） --- # 「lmqg/qa_squadshifts_synthetic」数据集卡片 ## 数据集描述 - **仓库地址**：[https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **论文地址**：[https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) - **联络人**：[Asahi Ushio](http://asahiushio.com/) ### 数据集概述 本数据集基于 [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts) 微调后的问答生成（Question Generation, QG）模型生成，专为Zhang与Bansal于2019年提出的问答生成模型的基于问答的评估（Question-Answering based Evaluation, QAE）任务打造。 该数据集的测试划分集即为 [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts) 的原始验证集，模型应在此划分集上完成评估。 ### 支持任务与排行榜 * `问答（Question Answering）` ### 语言 英语（en） ## 数据集结构 ### 数据字段 所有数据划分的字段格式均保持一致。 #### 纯文本字段 - `id`：字符串类型的数据集唯一标识 - `title`：字符串类型的段落标题 - `context`：字符串类型的段落正文 - `question`：字符串类型的问题文本 - `answers`：存储答案的JSON格式字段 ### 数据划分 待公布 ## 引用信息 @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", }
数据集概述
基本信息
- 名称: Synthetic QA dataset on SQuADShifts
- 许可证: cc-by-4.0
- 语言: 英语 (en)
- 多语言性: 单语种
- 规模: 10K<n<100K
数据来源
- 源数据集: 扩展自wikipedia
任务类型
- 任务类别: 问答
- 任务ID: extractive-qa
数据集结构
- 数据字段:
id: 字符串类型，标识符title: 字符串类型，段落标题context: 字符串类型，段落内容question: 字符串类型，问题answers: JSON格式，答案
数据分割
- 分割详情: 待定 (TBA)
引用信息
@inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", }
