Datasets:
humanizer v2 training data: SFT, DPO and RL rows used to train v2, split by license
506cf00 verified metadata
license: other
license_name: mixed-per-row
license_link: https://github.com/sgaofen/humanizer-local-model/blob/main/docs/DATA.md
language:
- en
- zh
pretty_name: humanizer training data (v2)
size_categories:
- 10K<n<100K
task_categories:
- text-generation
tags:
- rewriting
- paraphrase
- style-transfer
- dpo
- rlhf
configs:
- config_name: rewrite_sft
data_files:
- split: redistributable
path: rewrite_sft/redistributable.jsonl
- split: noncommercial
path: rewrite_sft/noncommercial.jsonl
- split: reference_only
path: rewrite_sft/reference_only.jsonl
- config_name: dpo
data_files:
- split: redistributable
path: dpo/redistributable.jsonl
- split: noncommercial
path: dpo/noncommercial.jsonl
- split: reference_only
path: dpo/reference_only.jsonl
- config_name: rl_prompts
data_files:
- split: redistributable
path: rl_prompts/redistributable.jsonl
- split: noncommercial
path: rl_prompts/noncommercial.jsonl
- split: reference_only
path: rl_prompts/reference_only.jsonl
- config_name: eval_drafts
data_files:
- split: test
path: eval_drafts/test.jsonl
humanizer-data
Training data for humanizer v2, a 12B model that rewrites AI-written drafts (English and Chinese) so they read like a person wrote them, keeping every fact. Code and app: GitHub. How the data was built, step by step: docs/DATA.md (中文).
Only rows that were actually used to train v2 are here, one config per training step:
| Config | Training step | Used in training | With text | Reference only | Removed before release |
|---|---|---|---|---|---|
rewrite_sft |
SFT: AI draft → human original | 28,560 | 5,471 | 21,632 | 1,457 |
dpo |
DPO: draft, chosen rewrite, rejected rewrite | 4,124 | 1,068 | 2,619 | 437 |
rl_prompts |
RL, three rounds: drafts only (the reward came from an LLM judge, no reference answer) | 5,299 | 1,264 | 3,744 | 291 |
eval_drafts |
Not trained on: the evaluation drafts from the model card, written from scratch by models | — | 311 | — | — |
Splits are licenses
| Split | What | Rows (SFT / DPO / RL) |
|---|---|---|
redistributable |
Public-domain or openly licensed human text (U.S. government reports, CC BY, ODC-BY, Apache-2.0), plus drafts an AI wrote from a scenario with no human original. Full text. | 4,932 / 821 / 965 |
noncommercial |
CC BY-NC-SA human text (PERSUADE 2.0, wikiHow, OpenStax). Full text; non-commercial, share-alike. | 539 / 247 / 299 |
reference_only |
Sources whose license does not let us pass the text on. No text: source dataset, record ID/URL where we could recover it, SHA-256 and length of the exact text we trained on. The draft is withheld too (it is a close rewrite of the original). Rebuild with scripts/rebuild_reference_only.py. |
21,632 / 2,619 / 3,744 |
Every row has a license and tier column; follow the license of each row. In rewrite_sft and dpo, split = train or validation (validation rows only measured loss to pick the checkpoint).
from datasets import load_dataset
sft = load_dataset("jialinyyzz/humanizer-data", "rewrite_sft", split="redistributable")
print(sft[0]["draft"][:300], "\n---\n", sft[0]["original"][:300])
Sources
| Source | License | Tier | Rows published (SFT / DPO / RL) |
|---|---|---|---|
| Congressional Research Service reports | Public domain (U.S. federal government work) | A · redistributable | 3,490 / 216 / 58 |
| eLife peer reviews and decision letters | CC BY 4.0 (eLife) | A · redistributable | 1,268 / 52 / 12 |
| NTSB aviation accident narratives | Public domain (U.S. federal government work) | A · redistributable | – / 95 / 147 |
| Apache Software Foundation board minutes | Apache-2.0 (The Apache Software Foundation) | A · redistributable | – / 99 / 142 |
| Project Gutenberg | Public domain in the U.S. (Project Gutenberg, headers removed) | A · redistributable | – / – / 194 |
| PeerRead (ACL 2017 / CoNLL 2016 reviews) | CC BY 4.0 | A · redistributable | 94 / 32 / – |
| peS2o (s2orc open-access subset) | ODC-BY 1.0 | A · redistributable | 80 / 2 / 7 |
| NIH RePORTER project abstracts | ODbL 1.0 (NIH RePORTER on data.gov) | A · redistributable | – / – / 46 |
| NSF award abstracts | Public domain (U.S. federal government work); NSF award abstracts | A · redistributable | – / – / 19 |
| AI-written from a scenario prompt (no human original) | AI-generated draft; see model terms | A · AI-written | – / 325 / 340 |
| PERSUADE 2.0 | CC BY-NC-SA 4.0 | B · non-commercial | 539 / 219 / 232 |
| wikiHow | CC BY-NC-SA 3.0 (wikiHow) | B · non-commercial | – / 28 / 66 |
| OpenStax textbooks | CC BY-NC-SA 4.0 (OpenStax) | B · non-commercial | – / – / 1 |
| PubMed abstracts (via MedRAG/pubmed) | Publisher/author copyright (NLM does not grant rights) | C · reference only | 4,933 / 327 / 115 |
| python-dev mailing list archive | No license (each sender holds copyright) | C · reference only | 4,289 / 507 / 28 |
| Zhihu-KOL (Zhihu answers) | No license (Zhihu user content) | C · reference only | 2,161 / 182 / 143 |
| COIG-CQIA | No license stated (mixed platform content) | C · reference only | 1,953 / 160 / 126 |
| Blog Authorship Corpus | Non-commercial research use; redistribution not granted | C · reference only | 1,791 / 135 / 254 |
| AESLC / Enron email | No license stated | C · reference only | 1,641 / 336 / 83 |
| Enron email corpus (CMU) | No license stated | C · reference only | 1,657 / 231 / 82 |
| Hacker News comments | Hacker News terms (no redistribution) | C · reference only | 1,260 / 159 / 5 |
| Webis-TLDR-17 (Reddit) | CC BY 4.0 from the aggregator over Reddit user content (not cleared with authors) | C · reference only | 923 / 67 / 173 |
| CSL (Chinese scientific abstracts) | Journal/author copyright | C · reference only | 491 / 53 / 315 |
| Webis-CMV-20 (Reddit r/changemyview) | CC BY 4.0 from the aggregator over Reddit user content (not cleared with authors) | C · reference only | – / 38 / 439 |
| IMDb Large Movie Review Dataset | No license stated (IMDb user content) | C · reference only | 286 / 24 / – |
| Listed-company announcements (Duxiaoman-DI/FinCorpus) | Aggregator license; content not cleared | C · reference only | – / – / 308 |
| Stack Exchange data dump (2021) | CC BY-SA, but author attribution not kept in our copy | C · reference only | – / 90 / 207 |
| Amazon Reviews 2023 (McAuley Lab) | No license stated (user content) | C · reference only | – / 57 / 222 |
| Paul Graham essays | All rights reserved | C · reference only | 246 / 20 / – |
| NASA ASRS report narratives | No written license for reporter narratives | C · reference only | – / 101 / 156 |
| ASAP-AES (Kaggle) | Kaggle competition data (no redistribution) | C · reference only | – / 70 / 161 |
| MNBVC (Tianya forum) | Aggregator license; content not cleared | C · reference only | – / 3 / 227 |
| Tweets (enryu43/twitter100m_tweets) | X/Twitter terms (post IDs only) | C · reference only | – / – / 156 |
| LinkedIn posts (Kaggle) | Platform terms (no redistribution) | C · reference only | – / – / 139 |
| People's Daily commentaries (Papersnake/people_daily_news) | People's Daily copyright | C · reference only | – / 2 / 117 |
| THUCNews (Sina News) | Sina News copyright; commercial use needs a license | C · reference only | – / 8 / 94 |
| Dianping reviews (yf_dianping) | No license (user content) | C · reference only | – / 1 / 75 |
| Yelp reviews (Yelp/yelp_review_full) | Yelp dataset agreement (no redistribution) | C · reference only | – / – / 76 |
| CNN/DailyMail | Article copyright retained by CNN / Daily Mail | C · reference only | – / 47 / 4 |
| nlp_chinese_corpus (brightmart) | Aggregator license; content not cleared | C · reference only | – / – / 14 |
| NUS Corpus of Learner English | NUS license (non-sublicensable) | C · reference only | – / – / 13 |
| WritingPrompts (Reddit) | Aggregator license over Reddit user content | C · reference only | – / – / 10 |
| Mixed academic text | Mixed source; not redistributed | C · reference only | 1 / 1 / 2 |
AI-written side: check the model terms
Drafts were written by GLM-5.3, GPT-5.6 Luna, Claude Opus, Claude Sonnet and Muse Spark (draft_model column); the DPO rewrites came from earlier checkpoints of humanizer and were judged by GLM-5.3 or GPT-5.6 Luna. These providers restrict using outputs to train other models (Anthropic and OpenAI: no competing models; Zhipu's mainland-China platform: no training of other models at all). Links in DATA.md. You are responsible for complying with them.
Privacy
All text fields were scanned before release. Rows mentioning the project author were removed everywhere; rows with any email address, phone number or ID number were removed from the two splits that carry text. Some openly licensed sources (U.S. government reports, Apache board minutes) name public officials and contributors, as in the originals.
Dates
v2 was trained on 178 rows (SFT / DPO / RL: 167 / 8 / 3) whose Zhihu answer is dated after 2022-11-30 (for the Zhihu subset of COIG-CQIA the date is inferred from the answer ID), 1,426 rows (1,148 / 169 / 109) whose peS2o paper is dated after 2022-11-30 (1,396 of them in December 2022), and 11 rows from CRS reports whose latest version is dated after 2022-11-30. All of these are left out of this release. 1,879 human texts have no date we could check, mostly the non-Zhihu parts of COIG-CQIA (1,817); they are kept.
Limitations
- Most human text is reference-only; rebuilding it depends on upstream sources staying online, and our texts are often tidied excerpts, so a rebuild may differ slightly.
- The judges are LLMs: strict, sometimes flagging harmless rewording, and they miss some errors. No AI detector was used anywhere.
- The SFT side leans formal (abstracts, reports, email).
Citation
@misc{humanizer2026data,
title = {humanizer-data: training data for the humanizer rewriting model (v2)},
author = {Stephen Yu},
year = {2026},
url = {https://huggingface.co/datasets/jialinyyzz/humanizer-data}
}
Please also cite the upstream datasets you use.