English | 中文

What is Humanize-Text?

An AI text humanization toolkit. This repo evolved through two stages:

v1.0 — Documented 4 humanization methodologies as reference implementations (translation chain, multi-turn LLM rewriting, detection-guided feedback loop, mixed-engine translation). See docs/techniques.md.
v1.5 (current) — Added the Standard Pipeline: a production-grade integration of Method 1 (Translation Chain) + Method 2 (LLM Rewriting), fixed as a 5-step chain we actually run and recommend.

v1.5.1 — Standard Pipeline (Recommended)

The Standard Pipeline preserves the original writing style while routing text through a 4-step chain: two DeepSeek humanization rewrites followed by two cross-engine translation hops.

Input (EN) → Chinese (DeepSeek) → Japanese (DeepSeek) → Finnish (Google) → English (Niutrans)

See examples/showcase/ for 5 real samples with full intermediate-step outputs and AI-detection verdicts.

Characteristics:

Best original style preservation among all approaches
Fast processing speed
100% key information retention (verified on 50 text pairs)
Expert quality score: 9.1/10

The 4 underlying methodologies live in src/methodologies/ as reference implementations for research and customization. The Standard Pipeline (src/standard/pipeline.py) is the recommended production path.

Want higher bypass rates + all methods combined? Lynote.ai fuses Standard + Advanced + Focus pipelines into one intelligent system — auto-selects the optimal approach for each passage.

Try Lynote.ai Free →

How It Works

Step-by-Step Pipeline

Step	Engine	From → To	Purpose
1	DeepSeek (temp 1.3)	Input → Chinese (Chinese Rewriting)	LLM humanization rewrite + language shift
2	DeepSeek (temp 1.3)	Chinese → Japanese (Japanese Rewriting)	Second LLM humanization, carries Step 1 as history
3	Google Translate	Japanese → Finnish (First Round of Translation)	First translation hop — distant language structural disruption
4	Niutrans	Finnish → English (Second-Round Translation)	Second translation hop — cross-engine reconstruction

Why This Chain Works

Steps 1–2 (LLM Rewrite): DeepSeek at temperature 1.3 rewrites while translating, breaking AI statistical fingerprints with creative variation. Step 2 carries Step 1 as conversation history for coherent humanization.
Steps 3–4 (Multi-Engine Translation): Two different NMT engines (Google → Niutrans) introduce compounding structural changes. No single-engine fingerprint survives.
Distant Languages: Chinese → Japanese → Finnish maximizes linguistic distance at each hop, ensuring thorough restructuring before reconstruction to English.

Lynote.ai — Beyond Standard

The Standard pipeline above is one of three tiers available. Each has different trade-offs:

Tier	Style Preservation	Speed	Approach
Standard (this repo)	Best	Fast	Translation chain
Advanced	Good	Medium	Translation chain + LLM multi-round rewriting
Focus	Moderate	Slower	Translation chain + Detection-guided feedback loop

Lynote.ai combines all three tiers and automatically selects the optimal approach for each text passage:

Intelligent Tier Selection — Analyzes text and picks Standard, Advanced, or Focus per-passage
Adaptive Combination — Can mix tiers within a single document
10+ Languages — English, Chinese, Japanese, Korean, Spanish, French, German, and more
Paste & Go — No setup, no API keys, no configuration

Quick Start

Method	Who It's For	How
Lynote.ai	Everyone — all tiers, zero setup	Visit lynote.ai
n8n Workflow	No-code automation users	Import `n8n/humanize_standard.json`
Python Script	Developers	See below

Python

git clone https://github.com/lynote-ai/humanize-text.git
cd humanize-text
pip install -r requirements.txt
cp config/config.example.toml config/config.toml
# Fill in your API keys in config.toml
python -m src.standard.pipeline --input "Your AI-generated text here"

n8n Workflow

Import n8n/humanize_standard.json into your n8n instance
Configure DeepSeek API key in the HTTP Request nodes
Run — input text goes in, humanized text comes out

Showcase — 5 Real Examples with Step-by-Step Outputs

We ran the pipeline end-to-end on 5 real input texts and saved every intermediate step. All 5 final outputs were classified as human by the AI detector.

#	Topic	Detection	Confidence
01	Quantum Computing	`human`	0.9997
02	Quantum Readiness Strategy	`human`	0.9982
03	Sustainable Supply Chains	`human`	0.7810
04	Financial Literacy	`human`	0.9924
05	Peer Review in Science	`human`	0.7218

Each example shows: original input → Step 1 (中文改写) → Step 2 (日语改写) → Step 3 (一轮翻译) → Step 4 (二轮翻译, final). See examples/showcase/ for full traces.

Quality Metrics

Tested on 50 text pairs with expert evaluation:

Dimension	Score (out of 10)
Information Completeness	10.0
Language Fluency	9.0
Style Adaptability	8.8
Readability	9.2
Creativity & Impact	8.5
Overall	9.1

Key Information Retention: 100% (50/50 pairs)
All texts preserved original key information without distortion

Comparison with Other Tiers

	Standard (this repo)	Lynote.ai
Tiers Available	Standard only	Standard + Advanced + Focus
Tier Selection	Manual	Automatic per-passage
Style Preservation	Best	Adaptive — best possible per passage
Setup	Python + API keys	Zero setup
Best For	Style-sensitive content	Any content type

Documentation

Standard Pipeline Technical Details — v1.5 production pipeline
4 Methodologies Reference — v1.0 underlying methods
Configuration Guide
n8n Workflow Guide
Lynote.ai vs Open Source Comparison
FAQ

Repo Structure

src/
├── standard/                # ★ v1.5.1 production Standard Pipeline (recommended)
│   ├── pipeline.py          # 4-step chain, CLI entry
│   ├── llm_rewriter.py      # DeepSeek humanization rewrite
│   └── translators.py       # Google + Niutrans engines
│
└── methodologies/           # v1.0 four-methodology reference implementations
    ├── humanizer.py         # v1.0 dispatcher + FastAPI app
    ├── translation_chain.py # Method 1
    ├── llm_rewriter.py      # Method 2
    ├── detection_pipeline.py# Method 3
    ├── mixed_engine.py      # Method 4
    ├── postprocess.py
    ├── detectors/           # Method 3 detectors
    └── utils/

examples/
├── example_usage.py         # ★ v1.5.1 minimal entry
├── showcase/                # ★ 5 real samples with intermediate-step outputs
└── legacy/                  # v1.0 examples + 4-method comparison outputs

License

MIT License. See LICENSE for details.

Star History

If this project helps you, please give it a ⭐!