allenai/metaicl-data

---
license: cc-by-nc-4.0
---

This is the downloaded and processed data from Meta's [MetaICL](https://github.com/facebookresearch/MetaICL).

We follow their ["How to Download and Preprocess"](https://github.com/facebookresearch/MetaICL#how-to-download-and-preprocess) instructions to obtain their modified versions of [CrossFit](https://github.com/INK-USC/CrossFit) and [UnifiedQA](https://arxiv.org/abs/2005.00700).

## Citation information
```
@inproceedings{ min2022metaicl,
title={ Meta{ICL}: Learning to Learn In Context },
author={ Min, Sewon and Lewis, Mike and Zettlemoyer, Luke and Hajishirzi, Hannaneh },
booktitle={ NAACL-HLT },
year={ 2022 }
}
@inproceedings{ ye2021crossfit,
title={ {C}ross{F}it: A Few-shot Learning Challenge for Cross-task Generalization in NLP },
author={ Ye, Qinyuan and Lin, Bill Yuchen and Ren, Xiang },
booktitle={ EMNLP },
year={ 2021 }
}
@inproceedings{ khashabi2020unifiedqa,
title={ {U}nified{QA}: Crossing Format Boundaries With a Single QA System },
author={ Khashabi, Daniel and Min, Sewon and Khot, Tushar and Sabharwal, Ashish and Tafjord, Oyvind and Clark, Peter and Hajishirzi, Hannaneh },
booktitle={ Findings of EMNLP },
year={ 2020 }
}
```

原始信息汇总

数据集概述

该数据集是从Meta的MetaICL项目下载并处理得到的。具体包括MetaICL修改版的CrossFit和UnifiedQA数据。

引用信息

@inproceedings{min2022metaicl, title={Meta{ICL}: Learning to Learn In Context}, author={Min, Sewon and Lewis, Mike and Zettlemoyer, Luke and Hajishirzi, Hannaneh}, booktitle={NAACL-HLT}, year={2022} } @inproceedings{ye2021crossfit, title={{C}ross{F}it: A Few-shot Learning Challenge for Cross-task Generalization in NLP}, author={Ye, Qinyuan and Lin, Bill Yuchen and Ren, Xiang}, booktitle={EMNLP}, year={2021} } @inproceedings{khashabi2020unifiedqa, title={{U}nified{QA}: Crossing Format Boundaries With a Single QA System}, author={Khashabi, Daniel and Min, Sewon and Khot, Tushar and Sabharwal, Ashish and Tafjord, Oyvind and Clark, Peter and Hajishirzi, Hannaneh}, booktitle={Findings of EMNLP}, year={2020} }

搜集汇总

背景与挑战

背景概述

allenai/metaicl-data是一个由AllenAI发布的文本数据集，基于UnifiedQA论文，用于支持少样本学习和跨任务泛化的研究。数据集包含约119k行数据，仅提供测试分割，覆盖多种任务如tweet_eval-emoji，涉及文本到表情符号的分类，适用于上下文学习（MetaICL）和自然语言处理的多任务评估。