Bielik Guard: Efficient Polish Language Safety Classifiers for LLM Content Moderation
As Large Language Models (LLMs) become increasingly deployed in Polish language applications, the need for efficient and accurate content safety classifiers has become paramount. We present Bielik Guard, a family of compact Polish language safety classifiers comprising two model variants: a 0.1B parameter model based on MMLW-RoBERTa-base and a 0.5B parameter model based on PKOBP/polish-roberta-8k. Fine-tuned on a community-annotated dataset of 6,885 Polish texts, these models classify content across five safety categories: Hate/Aggression, Vulgarities, Sexual Content, Crime, and Self-Harm. Our evaluation demonstrates that both models achieve strong performance on multiple benchmarks. The 0.5B variant offers the best overall discrimination capability with F1 scores of 0.791 (micro) and 0.785 (macro) on the test set, while the 0.1B variant demonstrates exceptional efficiency. Notably, Bielik Guard 0.1B v1.1 achieves superior precision (77.65%) and very low false positive rate (0.63%) on real user prompts, outperforming HerBERT-PL-Guard (31.55% precision, 4.70% FPR) despite identical model size. The models are publicly available and designed to provide appropriate responses rather than simple content blocking, particularly for sensitive categories like self-harm.
698d864f65c0d15a6d1622b2
Krzysztof Wróbel
claimed_verified
2026-02-12T13:26:03.384Z
5e47d3eb178ca95365287400
https://cdn-avatars.huggingface.co/v1/production/uploads/1633554153914-5e47d3eb178ca95365287400.png
Krzysztof Wróbel
djstrong
user
djstrong
698d864f65c0d15a6d1622b3
Jan Maria Kowalski
698d864f65c0d15a6d1622b4
Jerzy Surma
698d864f65c0d15a6d1622b5
Igor Ciuciura
698d864f65c0d15a6d1622b6
Maciej Szymański
Bielik Guard: Efficient Polish Language Safety Classifiers for LLM Content Moderation
As Large Language Models (LLMs) become increasingly deployed in Polish language applications, the need for efficient and accurate content safety classifiers has become paramount. We present Bielik Guard, a family of compact Polish language safety classifiers comprising two model variants: a 0.1B parameter model based on MMLW-RoBERTa-base and a 0.5B parameter model based on PKOBP/polish-roberta-8k. Fine-tuned on a community-annotated dataset of 6,885 Polish texts, these models classify content across five safety categories: Hate/Aggression, Vulgarities, Sexual Content, Crime, and Self-Harm. Our evaluation demonstrates that both models achieve strong performance on multiple benchmarks. The 0.5B variant offers the best overall discrimination capability with F1 scores of 0.791 (micro) and 0.785 (macro) on the test set, while the 0.1B variant demonstrates exceptional efficiency. Notably, Bielik Guard 0.1B v1.1 achieves superior precision (77.65%) and very low false positive rate (0.63%) on real user prompts, outperforming HerBERT-PL-Guard (31.55% precision, 4.70% FPR) despite identical model size. The models are publicly available and designed to provide appropriate responses rather than simple content blocking, particularly for sensitive categories like self-harm.
Bielik Guard is a compact Polish language safety classifier family with two variants that effectively categorize content across five safety domains while maintaining high efficiency and accuracy.
Large Language Models content safety classifiers MMLW-RoBERTa-base PKOBP/polish-roberta-8k fine-tuned models F1 scores precision false positive rate