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, 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
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
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.
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
Easy Dataset 是一款应用，可以把领域文档（PDF、Markdown、DOCX、TXT、EPUB）转换为结构化数据集，用于 LLM 微调、RAG 和模型评测。它会从你的文本中提取问题，借助 LLM 生成答案，并导出可直接使用的训练数据。