easy-dataset by ConardLi - RepoMind Architecture & Analysis

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.

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, 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, and Turkish 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

<table style="width: 100%"> <tr> <td width="20%" align="center"> <b>Windows</b> </td> <td width="30%" align="center" colspan="2"> <b>MacOS</b> </td> <td width="20%" align="center"> <b>Linux</b> </td> </tr> <tr style="text-align: center"> <td align="center" valign="middle"> <a href='https://github.com/ConardLi/easy-dataset/releases/latest'> <img src='./public/imgs/windows.png' style="height:24px; width: 24px" /> <br /> <b>Setup.exe</b> </a> </td> <td align="center" valign="middle"> <a href='https://github.com/ConardLi/easy-dataset/releases/latest'> <img src='./public/imgs/mac.png' style="height:24px; width: 24px" /> <br /> <b>Intel</b> </a> </td> <td align="center" valign="middle"> <a href='https://github.com/ConardLi/easy-dataset/releases/latest'> <img src='./public/imgs/mac.png' style="height:24px; width: 24px" /> <br /> <b>M</b> </a> </td> <td align="center" valign="middle"> <a href='https://github.com/ConardLi/easy-dataset/releases/latest'> <img src='./public/imgs/linux.png' style="height:24px; width: 24px" /> <br /> <b>AppImage</b> </a> </td> </tr> </table>

Install with NPM

Clone the repository:

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

Install dependencies:

   npm install

Start the development server:

   npm run build

   npm run start

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

Using the Official Docker Image

Clone the repository:

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

Modify the docker-compose.yml file:

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.

Start with docker-compose:

docker-compose up -d

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:

Clone the repository:

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

Build the Docker image:

docker build -t easy-dataset .

Run the container:

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.

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

Contributing

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

Fork the repository
Create a new branch (git checkout -b feature/amazing-feature)
Make your changes
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
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:

@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

<div align="center"> <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> </div>

ConardLi / easy-dataset

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