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allenai/ai2_arc

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Hugging Face2023-12-21 更新2024-03-04 收录
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https://hf-mirror.com/datasets/allenai/ai2_arc
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资源简介：
--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa - multiple-choice-qa pretty_name: Ai2Arc language_bcp47: - en-US dataset_info: - config_name: ARC-Challenge features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 349760 num_examples: 1119 - name: test num_bytes: 375511 num_examples: 1172 - name: validation num_bytes: 96660 num_examples: 299 download_size: 449460 dataset_size: 821931 - config_name: ARC-Easy features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 619000 num_examples: 2251 - name: test num_bytes: 657514 num_examples: 2376 - name: validation num_bytes: 157394 num_examples: 570 download_size: 762935 dataset_size: 1433908 configs: - config_name: ARC-Challenge data_files: - split: train path: ARC-Challenge/train-* - split: test path: ARC-Challenge/test-* - split: validation path: ARC-Challenge/validation-* - config_name: ARC-Easy data_files: - split: train path: ARC-Easy/train-* - split: test path: ARC-Easy/test-* - split: validation path: ARC-Easy/validation-* --- # Dataset Card for "ai2_arc" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/arc](https://allenai.org/data/arc) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1361.68 MB - **Size of the generated dataset:** 2.28 MB - **Total amount of disk used:** 1363.96 MB ### Dataset Summary A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### ARC-Challenge - **Size of downloaded dataset files:** 680.84 MB - **Size of the generated dataset:** 0.83 MB - **Total amount of disk used:** 681.67 MB An example of 'train' looks as follows. ``` { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ``` #### ARC-Easy - **Size of downloaded dataset files:** 680.84 MB - **Size of the generated dataset:** 1.45 MB - **Total amount of disk used:** 682.29 MB An example of 'train' looks as follows. ``` { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."] }, "id": "Mercury_SC_405487", "question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?" } ``` ### Data Fields The data fields are the same among all splits. #### ARC-Challenge - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. #### ARC-Easy - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. ### Data Splits | name |train|validation|test| |-------------|----:|---------:|---:| |ARC-Challenge| 1119| 299|1172| |ARC-Easy | 2251| 570|2376| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{allenai:arc, author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, journal = {arXiv:1803.05457v1}, year = {2018}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

annotations_creators: - 外部获取 language_creators: - 外部获取 language: - 英语 license: - CC BY-SA 4.0（知识共享署名-相同方式共享4.0协议） multilinguality: - 单语言 size_categories: - 1000 < 样本量 < 10000 source_datasets: - 原始数据集 task_categories: - 问答任务 task_ids: - 开放域问答 - 多项选择问答 pretty_name: Ai2Arc language_bcp47: - en-US（美式英语） dataset_info: - config_name: ARC挑战集 features: - name: id dtype: 字符串 - name: question dtype: 字符串 - name: choices sequence: - name: text dtype: 字符串 - name: label dtype: 字符串 - name: answerKey dtype: 字符串 splits: - name: 训练集 num_bytes: 349760 num_examples: 1119 - name: 测试集 num_bytes: 375511 num_examples: 1172 - name: 验证集 num_bytes: 96660 num_examples: 299 download_size: 449460 dataset_size: 821931 - config_name: ARC简单集 features: - name: id dtype: 字符串 - name: question dtype: 字符串 - name: choices sequence: - name: text dtype: 字符串 - name: label dtype: 字符串 - name: answerKey dtype: 字符串 splits: - name: 训练集 num_bytes: 619000 num_examples: 2251 - name: 测试集 num_bytes: 657514 num_examples: 2376 - name: 验证集 num_bytes: 157394 num_examples: 570 download_size: 762935 dataset_size: 1433908 configs: - config_name: ARC挑战集 data_files: - split: 训练集 path: ARC-Challenge/train-* - split: 测试集 path: ARC-Challenge/test-* - split: 验证集 path: ARC-Challenge/validation-* - config_name: ARC简单集 data_files: - split: 训练集 path: ARC-Easy/train-* - split: 测试集 path: ARC-Easy/test-* - split: 验证集 path: ARC-Easy/validation-* # 数据集卡片：ai2_arc ## 目录 - [数据集描述](#dataset-description) - [数据集概述](#dataset-summary) - [支持任务与排行榜](#supported-tasks-and-leaderboards) - [语言](#languages) - [数据集结构](#dataset-structure) - [数据样例](#data-instances) - [数据字段](#data-fields) - [数据划分](#data-splits) - [数据集构建](#dataset-creation) - [构建初衷](#curation-rationale) - [源数据](#source-data) - [注释](#annotations) - [个人与敏感信息](#personal-and-sensitive-information) - [数据集使用注意事项](#considerations-for-using-the-data) - [数据集的社会影响](#social-impact-of-dataset) - [偏差讨论](#discussion-of-biases) - [其他已知局限性](#other-known-limitations) - [附加信息](#additional-information) - [数据集维护者](#dataset-curators) - [许可信息](#licensing-information) - [引用信息](#citation-information) - [贡献](#contributions) ## 数据集描述 - **主页:** [https://allenai.org/data/arc](https://allenai.org/data/arc) - **代码仓库:** [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **相关论文:** [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) - **联系方式:** [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **下载数据集文件大小:** 1361.68 MB - **生成后数据集大小:** 2.28 MB - **总磁盘占用量:** 1363.96 MB ### 数据集概述 本数据集包含7787道真实的中小学阶段多项选择科学问题，旨在推动高级问答任务的研究。该数据集被划分为ARC挑战集与ARC简单集，其中挑战集仅包含那些基于检索的算法与词共现算法均无法正确解答的问题。本数据集还附带了与该任务相关的超过1400万条科学语句语料库，以及针对该数据集的三种神经网络基线模型实现。我们将ARC（AI2推理挑战）作为一项社区挑战任务向学界提出。 ### 支持任务与排行榜 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 语言 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## 数据集结构 ### 数据样例 #### ARC挑战集 - **下载数据集文件大小:** 680.84 MB - **生成后数据集大小:** 0.83 MB - **总磁盘占用量:** 681.67 MB 训练集的一条样例如下： { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["树荫区域增加。", "食物来源增加。", "氧气水平提升。", "可用水源增加。"] }, "id": "Mercury_SC_405487", "question": "某年，某公园内的橡树开始结出比往年更多的橡果。次年，该公园内的花栗鼠种群数量有所增长。以下哪一项最能解释次年花栗鼠数量增多的原因？" } #### ARC简单集 - **下载数据集文件大小:** 680.84 MB - **生成后数据集大小:** 1.45 MB - **总磁盘占用量:** 682.29 MB 训练集的一条样例如下： { "answerKey": "B", "choices": { "label": ["A", "B", "C", "D"], "text": ["树荫区域增加。", "食物来源增加。", "氧气水平提升。", "可用水源增加。"] }, "id": "Mercury_SC_405487", "question": "某年，某公园内的橡树开始结出比往年更多的橡果。次年，该公园内的花栗鼠种群数量有所增长。以下哪一项最能解释次年花栗鼠数量增多的原因？" } ### 数据字段 所有划分的数据字段均保持一致。 #### ARC挑战集 - `id`: 字符串类型特征。 - `question`: 字符串类型特征。 - `choices`: 包含以下字段的字典特征： - `text`: 字符串类型特征。 - `label`: 字符串类型特征。 - `answerKey`: 字符串类型特征。 #### ARC简单集 - `id`: 字符串类型特征。 - `question`: 字符串类型特征。 - `choices`: 包含以下字段的字典特征： - `text`: 字符串类型特征。 - `label`: 字符串类型特征。 - `answerKey`: 字符串类型特征。 ### 数据划分 | 数据集配置 | 训练集样本数 | 验证集样本数 | 测试集样本数 | |-------------|----:|---------:|---:| | ARC挑战集 | 1119 | 299 | 1172 | | ARC简单集 | 2251 | 570 | 2376 | ## 数据集构建 ### 构建初衷 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 源数据 #### 初始数据收集与标准化 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### 源语言生产者是谁？ [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 注释 #### 注释流程 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### 注释者是谁？ [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 个人与敏感信息 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## 数据集使用注意事项 ### 数据集的社会影响 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 偏差讨论 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 其他已知局限性 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## 附加信息 ### 数据集维护者 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 许可信息 [更多信息待补充](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### 引用信息 @article{allenai:arc, author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, journal = {arXiv:1803.05457v1}, year = {2018}, } ### 贡献 感谢 [@lewtun](https://github.com/lewtun)、[@patrickvonplaten](https://github.com/patrickvonplaten)、[@thomwolf](https://github.com/thomwolf) 为本数据集的收录提供的贡献。
提供机构：
allenai
原始信息汇总

数据集概述

基本信息

  • 名称: Ai2Arc
  • 语言: 英语（en-US）
  • 许可证: CC-BY-SA-4.0
  • 多语言性: 单语种
  • 数据集大小: 1K<n<10K
  • 源数据: 原始数据
  • 任务类别: 问答（question-answering）
  • 任务ID:
    • open-domain-qa
    • multiple-choice-qa

数据集结构

  • 配置名称:
    • ARC-Challenge
    • ARC-Easy
  • 特征:
    • id: 字符串类型
    • question: 字符串类型
    • choices: 序列类型，包含：
      • text: 字符串类型
      • label: 字符串类型
    • answerKey: 字符串类型
  • 数据分割:
    • ARC-Challenge:
      • 训练集: 1119个样本，349760字节
      • 测试集: 1172个样本，375511字节
      • 验证集: 299个样本，96660字节
    • ARC-Easy:
      • 训练集: 2251个样本，619000字节
      • 测试集: 2376个样本，657514字节
      • 验证集: 570个样本，157394字节

数据集下载与大小

  • 下载大小: 449460字节（ARC-Challenge），762935字节（ARC-Easy）
  • 数据集大小: 821931字节（ARC-Challenge），1433908字节（ARC-Easy）
搜集汇总
数据集介绍
main_image_url
构建方式
AllenAI的ARC数据集是针对高级问答任务构建的，旨在推动研究者在问题解答领域的深入探索。该数据集分为挑战集和简单集两部分，挑战集包含了既不能被基于检索的算法也不能被基于词共现的算法正确回答的问题。数据集的构建从原始数据收集、标准化到标注过程，每一步都经过精心设计，以确保数据的质量和适用性。
特点
ARC数据集的特点在于其问题均源于真实的小学科学问题，具有高度的真实性和多样性。挑战集和简单集的划分使得研究者在不同难度级别上都可以进行模型训练和评估。此外，数据集还提供了超过1400万句与任务相关的科学句子，以及三种神经基线模型的实现，为研究者提供了丰富的资源和基准。
使用方法
使用ARC数据集时，研究者可以根据自己的需求选择挑战集或简单集。数据集以JSON格式存储，包含了问题ID、问题文本、选项（包括文本和标签）以及答案关键字等字段。用户可以通过HuggingFace的datasets库轻松加载和利用这些数据进行模型训练、验证和测试。
背景与挑战
背景概述
ARC数据集，全称为AI2 Reasoning Challenge，是由AI2（艾伦人工智能研究所）的研究团队于2018年创建的。该数据集包含7777个真实的、小学水平的、多项选择的科学问题，旨在推动高级问答研究的发展。ARC数据集分为挑战集和简单集，其中挑战集仅包含被检索基础算法和词共现算法同时错误回答的问题。ARC数据集的构建，对于促进自然语言处理领域中的推理能力研究具有重要的意义，为研究人员提供了一个新的、具有挑战性的测试平台。
当前挑战
ARC数据集在构建过程中遇到的挑战主要包括：确保问题具有足够的难度以区分不同的算法性能；问题的多样性和覆盖的科学领域广泛性；以及数据标注的准确性和一致性。此外，ARC数据集所解决的领域问题是开放域问答中的推理问题，这对于当前的自然语言处理系统来说是一个难点，需要系统具备深层次的逻辑推理和背景知识理解能力。
常用场景
经典使用场景
在科学问答领域，allenai/ai2_arc数据集被广泛用于评估和提升模型在理解复杂科学问题和推理方面的能力。该数据集包含大量真实的、适合小学生水平的、多选科学问题，特别设计的Challenge Set更是筛选出了对现有算法具有挑战性的问题，使得它成为检验模型性能的一个经典场景。
实际应用
在实际应用中，ARC数据集可以被用于教育和训练人工智能助手，特别是在科学教育和科普领域，它可以帮助AI更好地理解和回答用户的科学相关问题，提升人工智能在教育和咨询服务中的应用价值。
衍生相关工作
基于ARC数据集，学术界衍生出了一系列相关工作，如对数据集进行分析、提出新的模型结构、以及开展针对特定类型科学问题的研究。这些工作进一步扩展了数据集的应用范围，推动了科学问答领域的研究进展，并促进了相关技术的实际应用。
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