SEACrowd/okapi_m_arc

Okapi M Arc数据集是AI2的Arc Challenge的多语言翻译版本，旨在促进高级问答系统的研究。该数据集包含7,787个小学科学水平的多项选择题，分为挑战集和简单集，挑战集包含那些基于检索和词共现算法都未能正确回答的问题。此外，数据集还包括超过1400万条与任务相关的科学句子和三个神经基线模型的实现。数据集支持印度尼西亚语和越南语，主要用于问答任务。

mARC is a Multilingual translation of AI2s Arc Challenge from the paper Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback (Lai et al., 2023). The original ARC dataset is a multiple-choice question answering dataset of 7,787 genuine grade-school level 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 also include 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.

原始信息汇总

Okapi M Arc 数据集概述

基本信息

• 名称: Okapi M Arc
• 语言: 印尼语 (ind), 越南语 (vie)
• 任务类别: 问答 (Question Answering)
• 标签: 问答 (Question Answering)
• 许可证: Creative Commons Attribution Non Commercial 4.0 (cc-by-nc-4.0)

数据集描述

• 来源: 由Lai et al., 2023 翻译的AI2的Arc Challenge的多语言版本。
• 原始数据集: 包含7,787个真实的小学科学问题，分为挑战集和简单集。挑战集仅包含被检索算法和词共现算法错误回答的问题。
• 附加资源: 包含超过1400万条与任务相关的科学句子语料库，以及三种神经网络基线模型。

使用方法

使用 datasets 库

python from datasets import load_dataset dset = datasets.load_dataset("SEACrowd/okapi_m_arc", trust_remote_code=True)

使用 seacrowd 库

python import seacrowd as sc

使用默认配置加载数据集

dset = sc.load_dataset("okapi_m_arc", schema="seacrowd")

查看数据集的所有可用子集（配置名称）

print(sc.available_config_names("okapi_m_arc"))

使用特定配置加载数据集

dset = sc.load_dataset_by_config_name(config_name="<config_name>")

版本信息

• 源版本: 1.0.0
• SEACrowd 版本: 2024.06.20

引用

plaintext @article{dac2023okapi, title={Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback}, author={Dac Lai, Viet and Van Nguyen, Chien and Ngo, Nghia Trung and Nguyen, Thuat and Dernoncourt, Franck and Rossi, Ryan A and Nguyen, Thien Huu}, journal={arXiv e-prints}, pages={arXiv--2307}, year={2023} }

@article{Clark2018ThinkYH, title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, journal={ArXiv}, year={2018}, volume={abs/1803.05457} }

@article{lovenia2024seacrowd, title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya}, year={2024}, eprint={2406.10118}, journal={arXiv preprint arXiv: 2406.10118} }