# Easy Dataset

> Open-source application that converts domain documents into structured datasets for LLM fine-tuning, RAG, and model evaluation.

Easy Dataset is an application for building large language model datasets from domain-specific documents. It combines document parsing, text segmentation, question and answer generation, dataset management, and export in one project-based workflow. It is available as desktop clients for Windows, macOS, and Linux, via Docker, or by running from source with NPM. The latest release listed on GitHub is 1.7.3.

## What It Is

Easy Dataset is a dataset-creation tool for teams fine-tuning or evaluating LLMs. Its documentation says the hard part of fine-tuning is usually dataset preparation, and the tool targets a closed loop from document parsing through dataset construction, annotation, export, and evaluation. The README says the resulting datasets suit fine-tuning, retrieval-augmented generation (RAG), and model performance evaluation.

## How the Workflow Runs

Work is organized by project. Documents in PDF, Markdown, DOCX, TXT, EPUB and other formats are parsed and split with several algorithms: Markdown structure, recursive separators, fixed length, and code-aware chunking. The tool then extracts questions from text segments, generates answers (including Chain of Thought answers) through an LLM API, and organizes data under an AI-generated domain label tree that can be edited by hand. Questions and answers can be deleted, edited, or refined by AI. Data cleaning and AI quality scoring help filter output.

## Dataset Types and Export

Besides single-turn QA, it supports multi-turn dialogue datasets, image QA datasets, and data distillation from a topic without uploading documents. Exports use Alpaca, ShareGPT, or Multilingual-Thinking formats as JSON or JSONL, with balanced export per tag. It can generate LLaMA Factory configuration files and upload datasets to Hugging Face Hub.

## Evaluation and Model Support

Version 1.7.0 added evaluation: generating test sets (true/false, single choice, multiple choice, short answer, open-ended), automated judging with a Judge Model, and a double-blind human Arena comparison. It works with any LLM API following the OpenAI format, plus local models through Ollama. Vision models can be used for PDF parsing and image QA. A playground compares up to three models side by side.

## Deployment Notes

The source is licensed under AGPL-3.0 with additional terms covering branding and alternative licensing. The Docker setup mounts local-db and prisma folders, and the app is served at localhost:1717.

## Features
- Document parsing for PDF, Markdown, DOCX, TXT, EPUB
- Multiple text splitting algorithms
- Question and answer generation with Chain of Thought
- Domain label tree with auto-tagging
- Multi-turn dialogue and image QA datasets
- Data distillation without documents
- Evaluation datasets and automated Judge Model scoring
- Human blind test Arena
- Custom prompt templates
- Background task management
- Token and API usage monitoring dashboard
- Model testing playground
- Export in Alpaca, ShareGPT, Multilingual-Thinking formats
- LLaMA Factory config generation
- Hugging Face Hub upload

## Integrations
OpenAI, Ollama, DeepSeek, Zhipu AI, Alibaba Bailian, OpenRouter, MiniMax, Gemini, Claude, LLaMA Factory, Hugging Face, Kaggle

## Platforms
WINDOWS, MACOS, LINUX, WEB, API

## Pricing
Open Source

## Version
1.7.3

## Links
- Website: https://docs.easy-dataset.com
- Documentation: https://docs.easy-dataset.com
- Repository: https://github.com/ConardLi/easy-dataset
- EveryDev.ai: https://www.everydev.ai/tools/easy-dataset