Humanize-Text

**An open-source pipeline for rewriting AI-generated text into natural human prose**
Website · **Humanizer API** · Product Hunt · Try the detector · Discord · X
English | 中文
> 🚀 **New: the Lynote Humanizer API is live.** Need humanization inside your own app, workflow, or SaaS? Call Lynote's production humanizer with one REST request — no pipeline to host. **Get an API key → lynote.ai/ai-humanizer-api**
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Most humanizers are a black box with marketing claims attached. This one is open source, so you can read what it actually does.
The current method is a **structure-first data pipeline** that trains the model to rewrite *structure*, not just launder surface style — the full implementation is in training-data-pipeline/. The original translation-chain approach is kept below as a runnable reference.
How it works
The current method is a **structure-first data pipeline** that builds the SFT + preference (KTO) training data for our own AI→human rewrite model — it teaches the model to rewrite *structure*, not just launder surface style. Full implementation, policies, tests, and 50 demo records per stage live in training-data-pipeline/.
- **SFT → KTO.** A supervised pass learns the rewrite task; a KTO (preference) pass teaches it to prefer genuine structural rewrites over surface edits.
- **Structure-first negatives.** Undesirable responses are selected for under-editing, structural damage, semantic drift, over-rewriting, repetition, and refusal — an exact 60% under-edit / 20% source-copy / 20% severe-failure mix.
- **Controlled prompt diversity.** A pool of equivalent instruction wordings is sampled with a fixed seed, so the model generalizes across phrasings, not one command style.
- **Deterministic & auditable.** SHA-256 identity per pair, seeded splits, versioned YAML policies, two-layer validation, and a human review gate. No weights, endpoints, or performance claims are shipped.
Full method: DATA_CONSTRUCTION.md · ARCHITECTURE.md · PROMPTS.md
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Legacy reference: v1.x translation chain
*The translation chain below is our earlier open exploration. It still runs and is kept as a readable reference — the current method is the structure-first pipeline above.*
Step-by-Step Pipeline
| Step | Engine | From → To | Purpose |
|------|--------|-----------|---------|
| 1 | LLM (temp 1.3) | Input → Chinese (Chinese Rewriting) | LLM humanization rewrite + language shift |
| 2 | LLM (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
1. **Steps 1–2 (LLM Rewrite):** Configurable LLM provider (DeepSeek default, OpenRouter optional) 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.
2. **Steps 3–4 (Multi-Engine Translation):** Two different NMT engines (Google → Niutrans) introduce compounding structural changes. No single-engine fingerprint survives.
3. **Distant Languages:** Chinese → Japanese → Finnish maximizes linguistic distance at each hop, ensuring thorough restructuring before reconstruction to English.
Quick start
Tiers
| Tier | What it does | Best for |
|---|---|---|
| standard | 2 LLM rewrites + 2 MT hops | The default balance |
| advanced | + multi-round LLM rewriting | Deeper restructuring |
| focus | + detection-guided feedback loop | Maximum restructuring |
**Note on intended use.** This toolkit is for improving the readability
and natural cadence of AI-assisted drafts. If you are writing in an