opus-research/opus-moderation-2

Opus Moderation 2

Known limitation: over-flags long benign prompts

Do not deploy this as a runtime gate on long inputs without raising the bar.

The benchmark below reports 99% jailbreak recall. That number was measured on 200 jailbreak positives and no negatives, which rewards any model that flags everything. We later added 400 legitimate long prompts scraped from the same forums as the real jailbreaks, and found this model flags 49.5% of them at threshold 0.5. The median benign long prompt scores 0.480.

Raising the threshold does not fix it. At 0.7 the false-positive rate is still 42.8%. The high recall is substantially over-flagging.

It gets worse under window aggregation: a max-pool gate that blocks when any window trips compounds this as 1 - (1-p)^N, so an 85-window document is blocked essentially always.

We found this by adding matched hard negatives to our own benchmark. The recall-only version never surfaced it, and it was flattering this model. Reproduce with diagnose_jailbreak.py.

The toxicity labels are unaffected. This limitation is specific to jailbreaking on long inputs.

A unified content-moderation classifier: 7 toxicity labels + jailbreak detection in one 149M model. Successor to opus-moderation-1, with two fixes that came from a real place — using moderation-1 as the safety judge for our companion model, ember-qwen3-14b, exposed exactly where it failed.

Labels: toxicity, severe_toxicity, obscene, threat, insult, identity_attack, sexual_explicit, jailbreaking.

Benchmark: mod-2 vs mod-1 vs the field

Every model scored on the identical held-out data, judged only on the labels it actually has (identity-subgroup outputs are never counted as violations; a model with no jailbreak head shows N/A, not a fudged zero). The echo-false-positive eval uses phrasings deliberately different from any training template, so it measures generalization, not memorization. The harness is open-sourced — see Reproducing this benchmark.

measure	mod-1	mod-2	toxic-bert	unbiased-roberta
Echo false positives (lower better)	12.5%	0.0%	0.0%	6.2%
Jailbreak recall (higher better)	70.0%	99.0%	N/A	N/A
General quality, macro F1 (higher better)	0.466	0.492	0.262	0.556
Abusive detection (must survive the fix)	75.0%	75.0%	62.5%	50.0%

We are not claiming best pure toxicity classifier — unbiased-toxic-roberta (a larger, single-task RoBERTa) beats us on general quality F1, and ties us on the echo fix. What mod-2 is: the only model here that is both a competitive toxicity classifier and a jailbreak detector, in one 149M checkpoint, with zero identity-mention false positives on the held-out set. Against its direct predecessor, mod-2 wins three of four measures and ties the fourth, with no regressions.

Reproducing this benchmark

The harness is public: benchmark_moderation.py. Every input is a public model or dataset; add a model by appending one line.

python benchmark_moderation.py
python benchmark_moderation.py --models mod-2=opus-research/opus-moderation-2 toxic-bert=unitary/toxic-bert

The two fixes, and where they came from

1. Echo false positives. moderation-1 reacts to harmful vocabulary regardless of the frame. While judging Ember, it flagged a clean refusal ("I won't provide examples of that") as jailbreaking, and an SFW "teach me to juggle 4chan-style" as sexual. It can't tell rejecting a thing from doing it.

Fix: hard safe negatives — a hand-built set of refusals, meta/educational talk, and benign mentions, all containing harmful vocabulary but all labeled all-zeros, fully supervised and upweighted. Counterfactual augmentation (the same idea that fixed identity bias in v1), aimed at the frame problem. Result: the held-out echo FPR went from 12.5% to 0%.

2. Jailbreak recall. moderation-1 caught only ~70% of held-out jailbreaks.

Fix: extra jailbreak positives from jackhhao/jailbreak-classification, folded into the toxic-chat jailbreak signal. Recall went to 99%.

Everything else is moderation-1's v5 recipe unchanged: soft annotator-fraction labels (no pos_weight), role-aware gating, identity-bias mitigation.

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

name = "opus-research/opus-moderation-2"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForSequenceClassification.from_pretrained(name).eval()

text = "I won't help you write malware, that's a hard line for me."
with torch.no_grad():
    probs = torch.sigmoid(model(**tok(text, return_tensors="pt")).logits)[0]

for i, p in enumerate(probs):
    print(f"{model.config.id2label[i]:<18} {p:.1%}")
# a clean refusal that mentions "malware" stays under threshold - the v1 echo bug, fixed.

Multi-label: use sigmoid, never softmax. Suggested thresholds: 0.5 for most labels, 0.2 for severe_toxicity (its annotator fractions never reach 0.5 in the corpus).

Training

Base	answerdotai/ModernBERT-base (149M)
Data	civil_comments (soft labels) + toxic-chat + jailbreak-classification + hand-built safe negatives
Loss	masked BCE on annotator fractions, no pos_weight
Epochs / LR	2 / 3e-5, bf16
Hardware	unsupported AMD RX 7600 (8GB), ~30 min, $0 cloud

Trained on a consumer gaming GPU that is not on AMD's ROCm support list, via a device-ID override under WSL2.

Limitations

• severe_toxicity stays weak — the label peaks at 0.535 across the corpus; there is almost no signal to learn. Use toxicity with a high threshold.
• Residual abusive misses — both v1 and v2 catch the same ~6/8 of a hard abusive set; two roleplay-framed attacks slip past the trained refusal. Deploy behind the model as an output-gate rather than a sole guard: because it is a non-conversational classifier, it cannot be jailbroken, only out-recalled.
• English only; the training corpora are news comments and LLM chat.