LorenaYannnnn/contrastive_instruction_tuning
收藏Hugging Face2024-06-02 更新2024-06-12 收录
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---
license: apache-2.0
language:
- en
pretty_name: CoIN
size_categories:
- 10K<n<100K
---
# Dataset for Contrastive Instruction Tuning
This is the official dataset used in our ACL'24 Findings paper [Contrastive Instruction Tuning](https://arxiv.org/abs/2402.11138).
The original data source of our new dataset is the FLAN collection, specifically from [Muennighoff/flan](https://huggingface.co/datasets/Muennighoff/flan) on Huggingface. We follow the steps discussed in section 3.2 of the paper to curate dataset for CoIN.
- Each entry contains:
- The original instruction-input pair (```original_instruction```)
- The paraphrased instruction-input pair (```paraphrased_instruction```)
- Label (```targets```)
- Task name
- Keyword data (a dictionary that contains KV pairs that will be parsed into the instruction templates to get the full input).
- Every entry at the odd index is the hard negative for the entry above it.
# Github
The full implementation of our paper is available at [here](https://github.com/luka-group/CoIN?tab=readme-ov-file).
# Citation
```
@inproceedings{yan2024contrastive,
title={Contrastive Instruction Tuning},
author={Yan, Tianyi and Wang, Fei and Huang, James Y and Zhou, Wenxuan and Yin, Fan and Galstyan, Aram and Yin, Wenpeng and Chen, Muhao},
booktitle={ACL - Findings},
year={2024}
}
```
提供机构:
LorenaYannnnn
原始信息汇总
数据集概述
基本信息
- 许可证: Apache-2.0
- 语言: 英语 (en)
- 数据集大小: 10K<n<100K
数据集详情
- 名称: CoIN
- 用途: 用于ACL24 Findings论文《Contrastive Instruction Tuning》的官方数据集
- 原始数据来源: FLAN collection, 来自Huggingface的Muennighoff/flan
数据结构
- 每条记录包含:
- 原始指令-输入对 (
original_instruction) - 改写后的指令-输入对 (
paraphrased_instruction) - 标签 (
targets) - 任务名称
- 关键词数据 (包含KV对的字典,用于解析指令模板以获取完整输入)
- 原始指令-输入对 (
- 数据组织: 奇数索引的条目为其上方条目的困难负例
引用信息
@inproceedings{yan2024contrastive, title={Contrastive Instruction Tuning}, author={Yan, Tianyi and Wang, Fei and Huang, James Y and Zhou, Wenxuan and Yin, Fan and Galstyan, Aram and Yin, Wenpeng and Chen, Muhao}, booktitle={ACL - Findings}, year={2024} }



