arc_challenge-hellaswag-piqa

arc_challenge-hellaswag-piqa
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{"description": "\nHellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019.\n", "citation": "@inproceedings{zellers2019hellaswag,\n title={HellaSwag: Can a Machine Really Finish Your Sentence?},\n author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},\n booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},\n year={2019}\n}\n", "homepage": "https://rowanzellers.com/hellaswag/", "license": "", "features": {"ind": {"dtype": "int32", "_type": "Value"}, "activity_label": {"dtype": "string", "_type": "Value"}, "ctx_a": {"dtype": "string", "_type": "Value"}, "ctx_b": {"dtype": "string", "_type": "Value"}, "ctx": {"dtype": "string", "_type": "Value"}, "endings": {"feature": {"dtype": "string", "_type": "Value"}, "_type": "Sequence"}, "source_id": {"dtype": "string", "_type": "Value"}, "split": {"dtype": "string", "_type": "Value"}, "split_type": {"dtype": "string", "_type": "Value"}, "label": {"dtype": "string", "_type": "Value"}}, "builder_name": "hellaswag", "dataset_name": "hellaswag", "config_name": "default", "version": {"version_str": "0.1.0", "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 43232624, "num_examples": 39905, "dataset_name": "hellaswag"}, "test": {"name": "test", "num_bytes": 10791853, "num_examples": 10003, "dataset_name": "hellaswag"}, "validation": {"name": "validation", "num_bytes": 11175717, "num_examples": 10042, "dataset_name": "hellaswag"}}, "download_checksums": {"https://raw.githubusercontent.com/rowanz/hellaswag/master/data/hellaswag_train.jsonl": {"num_bytes": 47496131, "checksum": null}, "https://raw.githubusercontent.com/rowanz/hellaswag/master/data/hellaswag_test.jsonl": {"num_bytes": 11752147, "checksum": null}, "https://raw.githubusercontent.com/rowanz/hellaswag/master/data/hellaswag_val.jsonl": {"num_bytes": 12246618, "checksum": null}}, "download_size": 71494896, "dataset_size": 65200194, "size_in_bytes": 136695090}
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### ARC挑战集（ARC-Challenge，隶属于ai2_arc数据集）
该数据集无官方描述信息。其数据特征包含：`id`（字符串类型样本标识）、`question`（问题文本，字符串类型）、`choices`（序列类型的选项集合，包含`text`：选项具体文本，字符串类型；`label`：选项标签，字符串类型）、`answerKey`（正确答案键，字符串类型）。数据集构建器采用Parquet格式，版本为0.0.0。数据集划分如下：训练集含1119条样本，占用字节数349760；测试集含1172条样本，占用字节数375511；验证集含299条样本，占用字节数96660。附带下载校验信息，总下载大小为449460字节，数据集原始大小为821931字节，总存储占用1271391字节。

### HellaSwag数据集
该数据集官方描述为："HellaSwag：《机器能否真正补全你的语句？》是一款面向常识自然语言推理（Commonsense Natural Language Inference, NLI）的新型数据集，相关研究论文发表于2019年国际计算语言学协会（ACL）年会"。其引用文献如下：
bibtex
@inproceedings{zellers2019hellaswag,
title={HellaSwag: Can a Machine Really Finish Your Sentence?},
author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year={2019}
}

数据集官方主页为：https://rowanzellers.com/hellaswag/，无公开授权协议。其数据特征包含：`ind`（int32类型索引）、`activity_label`（活动场景标签，字符串类型）、`ctx_a`（上文片段A，字符串类型）、`ctx_b`（上文片段B，字符串类型）、`ctx`（完整上文文本，字符串类型）、`endings`（序列类型的候选结尾集合，每个元素为字符串类型的候选续文）、`source_id`（来源样本ID，字符串类型）、`split`（划分名称，字符串类型）、`split_type`（划分类型，字符串类型）、`label`（正确答案标签，字符串类型）。数据集构建器为hellaswag，版本为0.1.0。数据集划分如下：训练集含39905条样本，占用字节数43232624；测试集含10003条样本，占用字节数10791853；验证集含10042条样本，占用字节数11175717。附带下载校验信息，总下载大小为71494896字节，数据集原始大小为65200194字节，总存储占用136695090字节。

### PIQA数据集
该数据集无官方描述信息。其数据特征包含：`goal`（任务目标描述，字符串类型）、`sol1`（候选解决方案1，字符串类型）、`sol2`（候选解决方案2，字符串类型）、`label`（类别标签，可选值为0、1）。数据集构建器采用Parquet格式，配置为plain_text，版本为1.1.0。数据集划分如下：训练集含16113条样本，占用字节数4106017；测试集含3084条样本，占用字节数761895；验证集含1838条样本，占用字节数464539。附带下载校验信息，总下载大小为3460529字节，数据集原始大小为5332451字节，总存储占用8792980字节。

搜集汇总

背景与挑战

背景概述

该数据集整合了三个子数据集：ARC-Challenge（包含2590个问答样本）、HellaSwag（包含59950个常识推理样本）和PIQA（包含21035个物理常识问答样本）。这些子数据集分别用于评估模型在科学问题回答、句子补全和物理交互理解方面的能力。