redmadrobot-rnd/nsfw_benchmark

Guardrail Model Evaluation Dataset

Dataset Description

This dataset is designed for evaluating AI safety systems (guardrails), NSFW (Not Safe For Work) content classification, and toxicity detection systems.

The test set includes challenging real-world examples, collected and labeled manually. The dataset is available in two versions: the original Russian version and an English translation. It allows you to evaluate models on complex and borderline examples. We position this dataset as a robust benchmark, and a comparative evaluation of several prominent safeguard systems is presented below.

Content Categories

The dataset is annotated across 7 main categories of harmful content:

1. Violence

• Physical violence and aggression: Injury, use of weapons, fighting, intimidation, and vandalism intended to intimidate.
• Murder and cruelty: Deprivation of life, mention of corpses, sadism, torture, dismemberment, and dehumanization.
• Mass violence: War crimes, terrorist attacks, genocide, ethnic cleansing, and mass shootings.
• Justification of violence: Normalization of cruelty, victim blaming, and glorification of criminals.

2. Erotic and Pornography

• Sexual scenarios: Descriptions of sexual intercourse with the explicit purpose of arousal.
• Artistic erotica: Detailed descriptions of foreplay, caresses, kisses, and erotic tension.
• Pornography (Explicit): Graphic descriptions of sexual intercourse, genitals, bodily secretions, and the mechanics of orgasm.

3. Animal Cruelty

• Sadism and violence: Intentional infliction of pain, injury, torture, and abuse of animals.
• Abandonment in danger: Deprivation of food/water, denial of treatment, keeping in critical conditions, and abandonment.
• Bloodfights and baiting: Organizing dog/cockfights, baiting animals with live bait for the sake of spectacle.

4. Drug Abuse

• Use and Consequences: Description of the drug use process, states of euphoria/hallucination, withdrawal symptoms, and overdoses.
• Production and Distribution: Instructions for manufacturing, diagrams of drug drop locations, couriers, and darknet traffic.
• Storage and Paraphernalia: Display of substances, paraphernalia, and evidence related to distribution.

5. Self-Harm/Suicide

• Self-Harm: Direct physical injury to oneself (cuts, burns, blows).
• Eating Disorders and Exhaustion: Promotion of anorexia, bulimia, dangerous diets, and sleep deprivation.
• Health Sabotage: Intentional non-fatal overdoses, poisoning, or refusing life-saving treatment.
• Destructive Risk: Victimizing behavior or intentionally seeking extreme trauma.

6. Politics / Hate Speech

• Disinformation: Spreading election fakes, deepfakes of politicians, and slander against institutions.
• Campaigning: Political PR, direct calls to vote, and promoting partisanship.
• Hate Speech: Dehumanizing opponents, inciting civil hatred, and calling for politically motivated repression.

7. Extremism

• Political/Social Extremism: Calls for uprisings, coups, and the elimination of opponents.
• Racism and Nazism: Propaganda of neo-Nazism, ethnic cleansing, and the use of prohibited symbols.
• Radicalism and Terrorism: Justification of terrorist attacks, activities of destructive sects, calls for violent jihad and eco-sabotage.
• Cyberterrorism: Instructions for hacking critical infrastructure, creating weapons from blueprints, and online recruitment.

Test Sample Composition:

Table 1. Distribution of examples by category and safety label

Category	Total	Unsafe	Safe
animal_cruelty	934	344	590
drug_abuse	936	420	516
erotic	996	476	520
extremism	888	434	454
politics	842	482	360
self_harm	976	464	512
violence	1034	496	538
TOTAL	6606	3116	3490

The dataset is fully bilingual. All original examples were collected in Russian and subsequently translated into English.

Table 2. Distribution of examples by category and language

Category	Total	RU	EN
animal_cruelty	934	467	467
drug_abuse	936	468	468
erotic	996	498	498
extremism	888	444	444
politics	842	421	421
self_harm	976	488	488
violence	1034	517	517
TOTAL	6606	3303	3303

The dataset consists of three main types of data designed to rigorously evaluate model capabilities:

1. Original unsafe texts (Unsafe / Original): Real user queries, questions, and requests that were collected, verified, and manually labeled across all 7 categories.
2. Contrastive safe texts (Contrastive / Safe): Created directly from the unsafe examples from the first point. Using the gemini-2.5-flash model, key details were changed in the original texts, turning the query into a safe one. These paired examples were added specifically to evaluate the models for vulnerability to shortcut learning (memorizing false features). All generated texts have undergone rigorous manual verification and correction.
3. Hard Negatives / Safe Texts: "Borderline" examples that were found and filtered entirely manually.

Table 3. Breakdown of safe examples by type

Type	Description	Count
contrastive	Modified unsafe examples, safe in content. Designed to evaluate the model's robustness against shortcut learning.	2680
hard_negative	Manually filtered borderline cases close to the safe/unsafe boundary.	810

Data Structure

(Note: Each text is assigned only one, most relevant category)

Column	Type	Description
text	string	Request or message text
label	int	0 — safe content, 1 — unsafe
type	string	Example type: unsafe, contrastive, hard_negative
category	string	Content category: violence, erotic, drug_abuse, animal_cruelty, self_harm, extremism, politics
language	string	Text language: ru — Russian, en — English

Benchmarks

This dataset serves as a robust benchmark for guardrail model evaluation. Below are the baseline evaluation results for several prominent open-weight models.

Evaluation Metric: F1-score (macro)

Evaluated Models & Hardware Setup:

• Llama Guard 3 8B (Q8_0)
• Qwen3Guard Gen 8B (Q8_0)
• gpt-oss-120b: via a custom safeguard prompt

Taxonomy Mapping

To ensure a fair comparison, we mapped our dataset's 7 categories to the specific safety taxonomies defined by the tested models:

Dataset Category	Llama Guard 3 8B (Q8_0)	Qwen3Guard Gen 8B (Q8_0)
violence	S1, S9	Violent
erotic	S3, S4, S12	Sexual Content, Sexual Acts
drug_abuse	S2	Non-violent Illegal Acts
animal_cruelty	S1	Violent, Unethical Acts
self_harm	S11	Suicide & Self-Harm
extremism	S1, S10	Unethical Acts
politics	S13	Politically Sensitive Topics

(Note: For detailed descriptions of these classes, please refer to the official documentation of the respective models).

Results: F1-score Macro (%)

English Subset (EN)

Category	Llama Guard 3 8B (Q8_0)	Qwen3Guard Gen 8B (Q8_0)	gpt-oss-120b
violence	67	66	87
erotic	82	34	96
drug_abuse	61	61	81
animal_cruelty	67	67	81
self_harm	74	60	82
extremism	68	52	75
politics	33	38	85

Russian Subset (RU)

Category	Llama Guard 3 8B (Q8_0)	Qwen3Guard Gen 8B (Q8_0)	gpt-oss-120b
violence	68	69	86
erotic	81	34	95
drug_abuse	70	68	86
animal_cruelty	67	72	81
self_harm	80	65	86
extremism	69	51	77
politics	34	42	88