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docs: update readme #60

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12 changes: 8 additions & 4 deletions README.md
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<div align="center">
<a href="https://discord.gg/Jhgb2eKWh8" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="Discord" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="8%" alt="Discord" /></a>
<a href="https://huggingface.co/spaces/DataEval/dingo" style="text-decoration:none;">
<img src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.png" width="3%" alt="Hugging Face" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<img src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.png" width="8%" alt="Hugging Face" /></a>
</div>


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}
```

# Research & Publications

- **"Comprehensive Data Quality Assessment for Multilingual WebData"** : [WanJuanSiLu: A High-Quality Open-Source Webtext
Dataset for Low-Resource Languages](https://arxiv.org/pdf/2501.14506)


# Future Plans

- [ ] Richer graphic and text evaluation indicators
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35 changes: 24 additions & 11 deletions README_zh-CN.md
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<a href="https://github.com/DataEval/dingo/issues"><img src="https://img.shields.io/github/issues/DataEval/dingo" alt="GitHub 问题"></a>
</p>


<div align="center">
<a href="https://discord.gg/Jhgb2eKWh8" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="8%" alt="Discord" /></a>
<a href="https://huggingface.co/spaces/DataEval/dingo" style="text-decoration:none;">
<img src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.png" width="8%" alt="Hugging Face" /></a>
</div>

</div>

# Changelog

- 2024/12/27: Project Initialization

# 一、介绍
# 介绍

Dingo是一款数据质量评估工具，帮助你自动化检测数据集中的数据质量问题。Dingo提供了多种内置的规则和模型评估方法，同时也支持自定义评估方法。Dingo支持常用的文本数据集和多模态数据集，包括预训练数据集、微调数据集和评测数据集。此外，Dingo支持多种使用方式，包括本地CLI和SDK，便于集成到各种评测平台，如[OpenCompass](https://github.com/open-compass/opencompass)等。

Expand All @@ -30,7 +38,7 @@ Dingo是一款数据质量评估工具，帮助你自动化检测数据集中的
![Architecture of dingo](./docs/assets/architeture.png)


# 二、快速启动
# 快速启动

## 1. 安装

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## 5. 在线演示
尝试我们的在线演示: [(Hugging Face)🤗](https://huggingface.co/spaces/DataEval/dingo)

# 三、数据质量指标
# 数据质量指标

Dingo将数据质量问题分为7个维度的质量指标。每个维度可以通过基于规则的方法和基于LLM的prompt进行评估：

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每条规则都针对文本质量的特定方面进行检查，并映射到这些指标之一。运行评估时，Dingo将提供每个维度的分数并识别触发了哪些规则。

# 四、规则组
# 规则组

Dingo为不同类型的数据集提供预配置的规则组：

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}
```

# 五、功能亮点
# 功能亮点

## 1. 多源和多模态支持

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- **质量指标**：7维质量评估
- **可追溯性**：异常追踪的详细报告

# 六、使用指南
# 使用指南

## 1. 自定义规则、Prompt和模型

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}
```

# 七、未来计划
# 研究与学术成果


- **"多语言网页数据的数据质量评估"** : [WanJuanSiLu: A High-Quality Open-Source Webtext Dataset for Low-Resource Languages](https://arxiv.org/pdf/2501.14506)

# 未来计划

- [ ] 更丰富的图文评测指标
- [ ] 音频和视频数据模态评测
- [ ] 小模型评测（如fasttext、Qurating）
- [ ] 数据多样性评测

# 八、局限性
# 局限性

当前内置的检测规则和模型方法主要关注常见的数据质量问题。对于特殊评估需求，我们建议定制化检测规则。

# 九、致谢
# 致谢

- [RedPajama-Data](https://github.com/togethercomputer/RedPajama-Data)
- [mlflow](https://github.com/mlflow/mlflow)

# 十、贡献
# 贡献

我们感谢所有的贡献者为改进和提升 `Dingo` 所作出的努力。请参考[贡献指南](docs/en/CONTRIBUTING.md)来了解参与项目贡献的相关指引。

# 十一、开源许可证
# 开源许可证

该项目采用 [Apache 2.0 开源许可证](LICENSE)。

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