Datasets:

henny52
/

chatmod-bench

license: cc-by-4.0
language:
  - de
  - en
task_categories:
  - text-classification
tags:
  - content-moderation
  - toxicity
  - benchmark
pretty_name: ChatMod Bench
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: test.jsonl

ChatMod Bench

600 German and English chat messages, half toxic. One question per message: would a moderator in a normal online community remove it or warn the author?

chat (300): gaming and community chat, including harmless look-alikes such as game banter, quotes, reports to moderators and negation
hatecheck (300): a sample of HateCheck and German HateCheck

{"id": "cmb-chat-de-1a2b3c4d5e", "text": "...", "label": 1, "lang": "de", "source": "chat", "category": "implied"}

Results

Model	Accuracy	Macro-F1
OpenAI omni-moderation-latest	75.7 %	0.754
axo-native-small-preview	75.0 %	0.746
Qwen3Guard-Gen-8B	70.2 %	0.684
Qwen3Guard-Gen-4B	69.5 %	0.678
Qwen3Guard-Gen-0.6B	66.2 %	0.644

Default decision rule of each model. Full numbers in results.json.

Run

pip install -r requirements.txt
python run_benchmark.py --model omni                  # or axo-native-small-preview, qwen3guard-0.6b/4b/8b
python run_benchmark.py --predictions my_model.jsonl  # {"id": ..., "flagged": true, "score": 0.93}

License

CC BY 4.0. The hatecheck part is from HateCheck (Röttger et al., ACL 2021) and Multilingual HateCheck (Röttger et al., WOAH 2022), both CC BY 4.0. Contains offensive language by design.