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zjunlp/ChineseHarm-bench

published by @zjunlp

Alignment dataset tracked in the /datasets sub-catalog.

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Created on HF
2025-05-31
Age
1 years ago

Description

ChineseHarm-bench

A Chinese Harmful Content Detection Benchmark

⚠️ WARNING: This project and associated data contain content that may be toxic, offensive, or disturbing. Use responsibly and with discretion.

Project •

Paper •

Hugging Face

🌟Benchmark

This folder contains the ChineseHarm-Bench.

bench.json is the full benchmark combining all categories.

The other files (e.g., 低俗色情.json, 欺诈.json) are category-specific subsets.

Each file is a list of… See the full description on the dataset page: https://huggingface.co/datasets/zjunlp/ChineseHarm-bench.

Tags

task_categories:text-classificationlanguage:zhlicense:cc-by-nc-4.0size_categories:10K<n<100Kformat:jsonmodality:textlibrary:datasetslibrary:dasklibrary:mlcroissantarxiv:2506.10960region:us

README current version from Hugging Face


language:

  • zh
    license: cc-by-nc-4.0
    size_categories:
  • 10K<n<100K
    task_categories:
  • text-classification

ChineseHarm-bench

A Chinese Harmful Content Detection Benchmark

⚠️ WARNING: This project and associated data contain content that may be toxic, offensive, or disturbing. Use responsibly and with discretion.

Project • Paper • Hugging Face

🌟Benchmark

This folder contains the ChineseHarm-Bench.

  • bench.json is the full benchmark combining all categories.
  • The other files (e.g., 低俗色情.json, 欺诈.json) are category-specific subsets.

Each file is a list of examples with:

  • "文本": the input Chinese text
  • "标签": the ground-truth label

🚩 Ethics Statement

We obtain all data with proper authorization from the respective data-owning organizations and signed the necessary agreements.

The benchmark is released under the CC BY-NC 4.0 license.
All datasets have been anonymized and reviewed by the Institutional Review Board (IRB) of the data provider to ensure privacy protection.

Moreover, we categorically denounce any malicious misuse of this benchmark and are committed to ensuring that its development and use consistently align with human ethical principles.

Acknowledgement

We gratefully acknowledge Tencent for providing the dataset and LLaMA-Factory for the training codebase.

🚩Citation

Please cite our repository if you use ChineseHarm-bench in your work. Thanks!

@misc{liu2025chineseharmbenchchineseharmfulcontent,
      title={ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark}, 
      author={Kangwei Liu and Siyuan Cheng and Bozhong Tian and Xiaozhuan Liang and Yuyang Yin and Meng Han and Ningyu Zhang and Bryan Hooi and Xi Chen and Shumin Deng},
      year={2025},
      eprint={2506.10960},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.10960}, 
}

README history 8 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2025-06-25Add link to paper, Github repository, ethics statement and acknowledgement. (#2)d8ac7d02.3 KB
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  2. 2025-06-13Update README.mda2584411.6 KB
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  3. 2025-06-02Update README.mdcc1581b1.1 KB
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  4. 2025-06-02Upload chineseharm_case.png20bd1631 KB
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  5. 2025-06-02Update README.mde32c7671 KB
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  6. 2025-06-02Update README.md341bb6d1 KB
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  7. 2025-05-31Upload 7 files48676fc33 B
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  8. 2025-05-31initial commit174ea0a33 B
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 3 threads · 1 open · 3 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2025-08-06PRImprove ChineseHarm-Bench dataset card with overview, usage, results, and detai…open1 💬#3
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  2. 2025-06-13PRAdd link to paper, Github repository, ethics statement and acknowledgement.merged1 💬#2
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  3. 2025-06-13[bot] Conversion to Parquetclosed1 💬#1
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Metadata

License
cc-by-nc-4.0

Repository

Download the dataset

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Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("zjunlp/ChineseHarm-bench")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="zjunlp/ChineseHarm-bench", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/zjunlp/ChineseHarm-bench
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