tanaos/synthetic-guardrail-dataset-german

Tanaos Guardrail German Training Dataset

This dataset was created synthetically by Tanaos with the Artifex Python library.

The dataset is designed to train and evaluate guardrail systems — models that detect, classify, or filter unsafe, harmful or potentially dangerous content — in German. It can be used to train moderation models or integrate LLM safety filters for applications like chatbots, content generation, and user-facing AI systems.

Our german guardrail model, tanaos-guardrail-german, was trained on this dataset.

Dataset Summary

The dataset contains text samples associated with arrays of 14 binary labels each. A label of 1 indicates that the text falls into the corresponding unsafe category, while a label of 0 indicates safety. The categories are the following:

Label Index	Category	Description
0	violence	Content describing or encouraging violent acts
1	non_violent_unethical	Content containing hateful or discriminatory language
2	financial_crime	Content related to financial fraud or scams
3	discrimination	Content promoting discrimination against individuals or groups
4	drug_weapons	Content related to illegal drugs or weapons
5	self_harm	Content encouraging self-harm or suicide
7	privacy	Content that invades personal privacy or shares private information
8	sexual_content	Content that is sexually explicit or inappropriate
9	child_abuse	Content involving the exploitation or abuse of children
10	terrorism_organized_crime	Content related to terrorism or organized crime
11	hacking	Content related to unauthorized computer access or cyberattacks
12	animal_abuse	Content involving the abuse or mistreatment of animals
13	jailbreak_prompt_inj	Content attempting to bypass or manipulate system instructions or safeguards

For instance, the following label: [0 0 0 0 0 0 0 0 0 0 0 0 0 0] means the the corresponding text is safe; the following label: [0 1 0 0 0 0 0 0 0 0 0 0 0 1] means that the text is unsafe, due to thge presence of non_violent_unethical as well as jailbreak_prompt_inj content.

How to Use

from datasets import load_dataset

dataset = load_dataset("tanaos/synthetic-guardrail-dataset-german")

print(dataset["train"][0])

Intended Use

This dataset is meant for training, fine-tuning, and evaluating models that act as guardrails for AI systems, if the content is in German.

Common use cases:

• Detecting and filtering toxic or policy-violating user input
• Reinforcing LLMs with content safety constraints
• Improving safety layers in production AI assistants or chatbots