Seq vs Seq: An Open Suite of Paired Encoders and Decoders
Paper • 2507.11412 • Published • 33
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Ettin-nsfw-prompt-stable-diffusion
A highly efficient, optimized text classifier designed to detect NSFW prompts in Stable Diffusion and other text-to-image pipelines. Built with an 8,192-token context window and a lightweight 68M parameter architecture, it delivers faster inference and tighter safety filtering than legacy BERT-based models.
Commercial Use & Licensing: This model is gated under a cc-by-nc-nd-4.0 license. Please see the prompt above to request commercial use rights or API access.
Evaluation Metrics
Evaluated on a held-out test set of 100,000 samples.
| Metric | Ettin-68M (This Model) | DistilRoBERTa V3 (Previous) |
|---|---|---|
| ROC-AUC | 0.9823 | 0.9764 |
| Accuracy | 0.9317 | 0.9198 |
| F1 Score | 0.9308 | 0.9201 |
| Precision | 0.9430 | 0.9179 |
| Recall | 0.9189 | 0.9222 |
(Note: While DistilRoBERTa shows a slightly higher base recall precisely at the 0.5 threshold, Ettin-68M scales far better across the curve, evidenced by its superior ROC-AUC).
TL;DR
Long Context Window (8,192 tokens): A 16x increase over legacy RoBERTa models. Catch malicious keywords hidden at the very end of heavy negative prompts without sequence truncation.
Lighter & Faster (68M Params): A 17% reduction in size compared to DistilRoBERTa, enabling higher API throughput and lower VRAM consumption for real-time edge filtering.
Production-Calibrated Safety: Pre-mapped operational thresholds (e.g., 0.0487) to guarantee a strict 99.0% recall rate in enterprise environments.
Modern Foundations: Built on the JHU-CLSP Ettin architecture, using RoPE and multi-stage context extension.
Overview
Trained on 14.9 million strictly balanced Stable Diffusion-style prompts (with 100K reserved for testing), this model is a strict performance upgrade over our previous DistilRoBERTa classifier.
By replacing legacy BERT-era foundations with Johns Hopkins University’s jhu-clsp/ettin-encoder-68m [1] a state-of-the-art 2025 open-data replication of ModernBERT, we unlock significant modern transformer advancements that directly improve production moderation.
Results on Production Usage & Safety Thresholds
By default, standard Hugging Face pipelines use a default classification threshold of 0.5. However, for production pipelines prioritizing safety (e.g., enterprise APIs or public web UIs), thresholds must be calibrated to hit specific Recall targets.
We ran extensive PR-AUC (Precision-Recall Area Under Curve) evaluations on our 100K test set to map the exact operational threshold required to hit a 99.0% Recall target (ensuring the model catches 99% of all actual NSFW prompts).
- Overall PR-AUC: 0.9844
- Optimal Safety Threshold:
0.0487 - Expected Recall: 99.00%
- Expected Precision: 74.80%
To implement this strict safety filter, configure your pipeline to flag a prompt if the positive NSFW probability exceeds 0.0487 rather than the default 0.5. This rigorous threshold guarantees 99% SFW enforcement, with a manageable ~25% false-positive rate as a trade-off for commercial safety.
Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="AdamCodd/ettin-nsfw-prompt-stable-diffusion", top_k=None)
# Predict
result = classifier("A beautiful landscape painting, mountains, 8k resolution")
# Enforce rigorous commercial safety threshold (99.0% Recall)
nsfw_prob = next(score['score'] for score in result[0] if score['label'] == 'NSFW')
is_flagged = nsfw_prob > 0.0487
Training Details
- Base Model:
jhu-clsp/ettin-encoder-68m - Dataset: 14.9 million Stable Diffusion-style prompts (balanced exactly 50/50 between NSFW and SFW).
- Test Set: 100,000 prompts.
References
[1] Weller et al., 2025. "Ettin: Analyzing Encoders vs Decoders Using the Same Architecture and Data". arXiv:2507.11412.
[2] Hugging Face Documentation. DistilRoBERTa Base Model Specifications (82M Parameters).
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Base model
jhu-clsp/ettin-encoder-68mDataset used to train AdamCodd/ettin-safety-filter-stable-diffusion
Paper for AdamCodd/ettin-safety-filter-stable-diffusion
Evaluation results
- Accuracyself-reported0.932
- F1self-reported0.931
- Precisionself-reported0.943
- Recallself-reported0.919
- AUCself-reported0.982