Repository: ConardLi/easy-dataset

Stars: 13975

README.md

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A powerful tool for creating fine-tuning datasets for Large Language Models

简体中文 | English | Türkçe

Features • Quick Start • Documentation • Contributing • License

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Overview

Easy Dataset is an application specifically designed for building large language model (LLM) datasets. It features an intuitive interface, along with built-in powerful document parsing tools, intelligent segmentation algorithms, data cleaning and augmentation capabilities. The application can convert domain-specific documents in various formats into high-quality structured datasets, which are applicable to scenarios such as model fine-tuning, retrieval-augmented generation (RAG), and model performance evaluation.

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News

🎉🎉 Easy Dataset Version 1.7.0 launches brand-new evaluation capabilities! You can effortlessly convert domain-specific documents into evaluation datasets (test sets) and automatically run multi-dimensional evaluation tasks. Additionally, it comes with a human blind test system, enabling you to easily meet needs such as vertical domain model evaluation, post-fine-tuning model performance assessment, and RAG recall rate evaluation. Tutorial: https://www.bilibili.com/video/BV1CRrVB7Eb4/

Features

📄 Document Processing & Data Generation

- Intelligent Document Processing: Supports PDF, Markdown, DOCX, TXT, EPUB and more formats with intelligent recognition
- Intelligent Text Splitting: Multiple splitting algorithms (Markdown structure, recursive separators, fixed length, code-aware chunking), with customizable visual segmentation
- Intelligent Question Generation: Auto-extract relevant questions from text segments, with question templates and batch generation
- Domain Label Tree: Intelligently builds global domain label trees based on document structure, with auto-tagging capabilities
- Answer Generation: Uses LLM API to generate comprehensive answers and Chain of Thought (COT), with AI optimization
- Data Cleaning: Intelligent text cleaning to remove noise and improve data quality

🔄 Multiple Dataset Types

- Single-Turn QA Datasets: Standard question-answer pairs for basic fine-tuning
- Multi-Turn Dialogue Datasets: Customizable roles and scenarios for conversational format
- Image QA Datasets: Generate visual QA data from images, with multiple import methods (directory, PDF, ZIP)
- Data Distillation: Generate label trees and questions directly from domain topics without uploading documents

📊 Model Evaluation System

- Evaluation Datasets: Generate true/false, single-choice, multiple-choice, short-answer, and open-ended questions
- Automated Model Evaluation: Use Judge Model to automatically evaluate model answer quality with customizable scoring rules
- Human Blind Test (Arena): Double-blind comparison of two models' answers for unbiased evaluation
- AI Quality Assessment: Automatic quality scoring and filtering of generated datasets

🛠️ Advanced Features

- Custom Prompts: Project-level customization of all prompt templates (question generation, answer generation, data cleaning, etc.)
- GA Pair Generation: Genre-Audience pair generation to enrich data diversity
- Task Management Center: Background batch task processing with monitoring and interruption support
- Resource Monitoring Dashboard: Token consumption statistics, API call tracking, model performance analysis
- Model Testing Playground: Compare up to 3 models simultaneously

📤 Export & Integration

- Multiple Export Formats: Alpaca, ShareGPT, Multilingual-Thinking formats with JSON/JSONL file types
- Balanced Export: Configure export counts per tag for dataset balancing
- LLaMA Factory Integration: One-click LLaMA Factory configuration file generation
- Hugging Face Upload: Direct upload datasets to Hugging Face Hub

🤖 Model Support

- Wide Model Compatibility: Compatible with all LLM APIs that follow the OpenAI format
- Multi-Provider Support: OpenAI, MiniMax, Ollama (local models), Zhipu AI, Alibaba Bailian, OpenRouter, and more
- Vision Models: Support Gemini, Claude, etc. for PDF parsing and image QA

🌐 User Experience

- User-Friendly Interface: Modern, intuitive UI designed for both technical and non-technical users
- Multi-Language Support: Complete Chinese, English, Turkish and Portuguese language support 🇹🇷
- Dataset Square: Discover and explore public dataset resources
- Desktop Clients: Available for Windows, macOS, and Linux

Quick Demo

https://github.com/user-attachments/assets/6ddb1225-3d1b-4695-90cd-aa4cb01376a8

Local Run

Download Client

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<b>Windows</b>
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<b>Linux</b>
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<a href='https://github.com/ConardLi/easy-dataset/releases/latest'>
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<b>Setup.exe</b>
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<a href='https://github.com/ConardLi/easy-dataset/releases/latest'>
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<b>Intel</b>
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<a href='https://github.com/ConardLi/easy-dataset/releases/latest'>
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<b>AppImage</b>
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Install with NPM

1. Clone the repository:

bash

git clone https://github.com/ConardLi/easy-dataset.git
   cd easy-dataset

2. Install dependencies:

bash

npm install

3. Start the development server:

bash

npm run build

npm run start

4. Open your browser and visit http://localhost:1717

Using the Official Docker Image

1. Clone the repository:

bash

git clone https://github.com/ConardLi/easy-dataset.git
cd easy-dataset

2. Modify the docker-compose.yml file:

yml

services:
  easy-dataset:
    image: ghcr.io/conardli/easy-dataset
    container_name: easy-dataset
    ports:
      - '1717:1717'
    volumes:
      - ./local-db:/app/local-db
      - ./prisma:/app/prisma
    restart: unless-stopped

Note: It is recommended to use the local-db and prisma folders in the current code repository directory as mount paths to maintain consistency with the database paths when starting via NPM.

Note: The database file will be automatically initialized on first startup, no need to manually run npm run db:push.

3. Start with docker-compose:

bash

docker-compose up -d

4. Open a browser and visit http://localhost:1717

Building with a Local Dockerfile

If you want to build the image yourself, use the Dockerfile in the project root directory:

1. Clone the repository:

bash

git clone https://github.com/ConardLi/easy-dataset.git
cd easy-dataset

2. Build the Docker image:

bash

docker build -t easy-dataset .

3. Run the container:

bash

docker run -d \
  -p 1717:1717 \
  -v ./local-db:/app/local-db \
  -v ./prisma:/app/prisma \
  --name easy-dataset \
  easy-dataset

Note: It is recommended to use the local-db and prisma folders in the current code repository directory as mount paths to maintain consistency with the database paths when starting via NPM.

Note: The database file will be automatically initialized on first startup, no need to manually run npm run db:push.

4. Open a browser and visit http://localhost:1717

Documentation

- View the demo video of this project: Easy Dataset Demo Video
- For detailed documentation on all features and APIs, visit our Documentation Site
- View the paper of this project: Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

Community Practice

- Complete test set generation and model evaluation with Easy Dataset
- Easy Dataset × LLaMA Factory: Enabling LLMs to Efficiently Learn Domain Knowledge
- Easy Dataset Practical Guide: How to Build High-Quality Datasets?
- Interpretation of Key Feature Updates in Easy Dataset
- Foundation Models Fine-tuning Datasets: Basic Knowledge Popularization

Contributing

We welcome contributions from the community! If you'd like to contribute to Easy Dataset, please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Commit your changes (git commit -m 'Add some amazing feature')
5. Push to the branch (git push origin feature/amazing-feature)
6. Open a Pull Request (submit to the DEV branch)

Please ensure that tests are appropriately updated and adhere to the existing coding style.

Join Discussion Group & Contact the Author

https://docs.easy-dataset.com/geng-duo/lian-xi-wo-men

License

This project is licensed under the AGPL 3.0 License - see the LICENSE file for details.

Citation

If this work is helpful, please kindly cite as:

bibtex

@misc{miao2025easydataset,
  title={Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents},
  author={Ziyang Miao and Qiyu Sun and Jingyuan Wang and Yuchen Gong and Yaowei Zheng and Shiqi Li and Richong Zhang},
  year={2025},
  eprint={2507.04009},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2507.04009}
}

Star History

![Star History Chart](https://www.star-history.com/#ConardLi/easy-dataset&Date)

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<sub>Built with ❤️ by <a href="https://github.com/ConardLi">ConardLi</a> • Follow me: <a href="./public/imgs/weichat.jpg">WeChat Official Account</a>｜<a href="https://space.bilibili.com/474921808">Bilibili</a>｜<a href="https://juejin.cn/user/3949101466785709">Juejin</a>｜<a href="https://www.zhihu.com/people/wen-ti-chao-ji-duo-de-xiao-qi">Zhihu</a>｜<a href="https://www.youtube.com/@garden-conard">Youtube</a></sub>
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Repository: ConardLi/easy-dataset

Stars: 13975

README.md Copy Chapter

Overview Copy Chapter

News Copy Chapter

Features Copy Chapter

📄 Document Processing & Data Generation Copy Section

🔄 Multiple Dataset Types Copy Section

📊 Model Evaluation System Copy Section

🛠️ Advanced Features Copy Section

📤 Export & Integration Copy Section

🤖 Model Support Copy Section

🌐 User Experience Copy Section

Quick Demo Copy Chapter

Local Run Copy Chapter

Download Client Copy Section

Install with NPM Copy Section

Using the Official Docker Image Copy Section

Building with a Local Dockerfile Copy Section

Documentation Copy Chapter

Community Practice Copy Chapter

Contributing Copy Chapter

Join Discussion Group & Contact the Author Copy Chapter

License Copy Chapter

Citation Copy Chapter

Star History Copy Chapter

Explore Related Technologies & Topics

README.md

Overview

News

Features

📄 Document Processing & Data Generation

🔄 Multiple Dataset Types

📊 Model Evaluation System

🛠️ Advanced Features

📤 Export & Integration

🤖 Model Support

🌐 User Experience

Quick Demo

Local Run

Download Client

Install with NPM

Using the Official Docker Image

Building with a Local Dockerfile

Documentation

Community Practice

Contributing

Join Discussion Group & Contact the Author

License

Citation

Star History