Turkish Safety - Content Moderation Classifier v5.0

Multi-label classification model for Turkish content moderation

Developed by SiriusAI Tech Brain Team

Mission

Empowering digital platforms with AI-driven content safety solutions.

Turkish Safety is an advanced NLP model that analyzes Turkish content in real-time and detects harmful content across 7 different categories. It provides comprehensive content moderation for social media platforms, messaging applications, in-game chats, and community forums.

Why This Model Matters

7 Risk Categories: Detects SAFE, GROOMING, SEXUAL, OFFENSIVE, BULLYING, SELF_HARM, and THREAT
Turkish-First Design: Optimized for Turkish linguistics and cultural context using BERTurk
Production-Ready: <50ms inference, battle-tested architecture, enterprise-grade reliability
Multi-Label Intelligence: Smart classification that understands content can belong to multiple categories
Expert Validation: Curated training data with clear category boundaries and edge case handling

Model Overview

Property	Value
Architecture	BERT (Bidirectional Encoder Representations from Transformers)
Base Model	`dbmdz/bert-base-turkish-uncased` (BERTurk)
Task	Multi-label Text Classification
Language	Turkish (tr)
Categories	7 content safety labels
Model Size	443 MB (FP32)
Inference Time	~10-15ms (GPU) / ~40-50ms (CPU)

Performance Metrics

Final Evaluation Results (Epoch 2)

Metric	Score	Description
Macro F1	0.9165	Harmonic mean of precision and recall across all categories
MCC	0.9045	Matthews Correlation Coefficient (robust multi-class metric)
Eval Loss	0.0268	Focal loss on validation set

Training Progress

Epoch	Train Loss	Eval Loss	Macro F1	MCC
1	0.038	0.0282	0.9085	0.8957
2	0.038	0.0268	0.9165	0.9045

Validation Test Results (86.4% Accuracy)

Category	Test Cases	Correct	Notes
SAFE	5	4	One false positive (compliment → offensive)
GROOMING	4	2	Boundary cases with SEXUAL/THREAT
SEXUAL	3	3	Perfect detection
OFFENSIVE	3	3	Perfect detection
THREAT	3	3	Perfect detection
SELF_HARM	2	2	Perfect detection
BULLYING	2	2	Perfect detection

Dataset

Dataset Statistics

Split	Samples	Purpose
Train	68,128	Model training
Test	17,033	Model evaluation
Total	85,161	Complete dataset

Category Distribution (Full Dataset)

Category	Samples	Percentage	Description
SAFE	25,488	29.9%	Benign, normal communication
SELF_HARM	14,234	16.7%	Self-harm ideation, suicidal thoughts
BULLYING	13,259	15.6%	Harassment, exclusion, cyberbullying
THREAT	9,193	10.8%	Physical threats, violence, blackmail
SEXUAL	8,642	10.1%	Sexual content, body comments
GROOMING	7,517	8.8%	Manipulation, trust-building tactics
OFFENSIVE	6,849	8.0%	Profanity, slurs, offensive language

Subcategory Breakdown

Category	Subcategories
SAFE	greetings (1,958), farewells (1,485), wellbeing_questions (2,900), daily_conversation (2,435), weather_talk (1,445), food_drink (1,481), normal_questions (1,861), school_talk (1,961), family_talk (1,487), hobbies_games (1,455), sports_talk (1,000), tech_internet (994), genuine_compliments (1,000), encouragement (1,000), appreciation (1,000), apology_understanding (998), help_cooperation (1,000)
GROOMING	secrecy (953), isolation (729), trust_manipulation (792), meeting_private (701), gift_promise (565), age_questioning (688), private_communication (628), emotional_manipulation (654), normalization (655), excessive_flattery (559), testing_boundaries (583)
THREAT	physical_violence (1,307), weapon_threat (936), blackmail (1,168), family_threat (1,071), implicit_threat (906), revenge (947), death_threat (886), social_threat (930), stalking_threat (532), property_threat (500)
OFFENSIVE	insults (1,286), cursing_sik (1,535), cursing_am (1,398), cursing_ana_orospu (1,383), derogatory (849), mockery (398)
SEXUAL	explicit_content (1,085), sexual_body_focus (1,612), sexual_invitation (1,237), pornographic (1,060), sexual_questions (1,232), romantic_pressure (1,030), inappropriate_comments (856), sexual_fantasy (530)
BULLYING	exclusion (1,904), mockery_repeated (1,690), emotional_abuse (1,678), appearance_attack (1,490), public_humiliation (1,091), intimidation (979), cyberbullying (1,138), name_calling (1,178), spreading_rumors (1,000), academic_bullying (1,111)
SELF_HARM	hopelessness (1,923), giving_up (1,690), not_waking_up (1,435), suicide_ideation (1,413), self_harm_plan (1,532), burden_feeling (1,018), worthlessness (1,037), isolation_feeling (1,025), goodbye_messages (807), self_blame (894), depression_signs (1,452)

Data Generation Methodology

Synthetic Generation: LLM-based generation with expert-defined category boundaries
Hard Negative Mining: Difficult edge cases for boundary discrimination
Quality Filtering: Duplicate detection, minimum word count, forbidden token filtering
Parallel Processing: 20 concurrent workers with batch size of 50
Pass Rate: 97.5% average acceptance rate across all categories

Label Definitions

The model classifies text into 7 mutually non-exclusive categories:

Label	ID	Description	Turkish Examples
`SAFE`	0	Benign, normal communication	"Bugün hava güzel", "Oyun oynayalım mı?"
`OFFENSIVE`	1	Profanity, slurs, offensive language	"Aptal mısın", "Salak herif"
`SELF_HARM`	2	Self-harm ideation, suicidal thoughts	"Ölmek istiyorum", "Kendimi kesmek istiyorum"
`GROOMING`	3	Manipulation, trust-building, isolation tactics	"Kimseye söyleme", "Sen özelsin", "Evime gel"
`BULLYING`	4	Harassment, exclusion, cyberbullying	"Kimse seninle oynamak istemiyor", "Çirkinsin"
`SEXUAL`	5	Sexual content, body comments, inappropriate questions	"Vücudun güzel", "Hiç öpüştün mü?", "Ne giyiyorsun?"
`THREAT`	6	Physical threats, violence, blackmail	"Seni döverim", "Fotoğrafını yayarım"

Important: Category Boundaries

GROOMING vs SEXUAL Distinction:

GROOMING: Non-sexual manipulation tactics (trust-building, secrecy, gift promises, meeting requests)
SEXUAL: Any body-related comments, physical compliments, sexual questions, explicit content

"Kimseye söyleme tamam mı?"  → GROOMING (secrecy/isolation)
"Vücudun çok güzel"          → SEXUAL (body comment)
"Telefon alırım sana"        → GROOMING (gift promise)
"Dudakların çok güzel"       → SEXUAL (body-focused compliment)
"Gel evime yalnızım"         → GROOMING (meeting request/isolation)
"Hiç öpüştün mü?"            → SEXUAL (sexual experience question)

Training Procedure

Hyperparameters

Parameter	Value
Base Model	`dbmdz/bert-base-turkish-uncased`
Max Sequence Length	64 tokens
Batch Size	16 (effective 32 with gradient accumulation)
Gradient Accumulation	2 steps
Learning Rate	2e-5 (with cosine restarts)
Epochs	2
Optimizer	AdamW
Weight Decay	0.01
Warmup Ratio	0.1
Loss Function	Focal Loss (gamma=1.2)
Label Smoothing	0.05
Problem Type	Multi-label Classification
Evaluation Strategy	Per epoch

Training Environment

Resource	Specification
Hardware	Apple M1 Pro (MPS)
Framework	PyTorch 2.x + Transformers 4.37+
Training Time	~14 minutes (864 seconds)
Throughput	157.8 samples/second
Steps	4,258 total

Learning Rate Schedule

Peak LR: 2e-5 (after warmup)
Schedule: Cosine with restarts
Final LR: ~1.1e-8

Usage

Installation

pip install transformers torch

Quick Start

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model
model_name = "hayatiali/turkish-safety"
tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-uncased")
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

# Label mapping (MUST match model's id2label)
LABELS = ["SAFE", "OFFENSIVE", "SELF_HARM", "GROOMING", "BULLYING", "SEXUAL", "THREAT"]

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

    with torch.no_grad():
        outputs = model(**inputs)
        # Multi-label: use sigmoid (NOT softmax!)
        probs = torch.sigmoid(outputs.logits)[0].numpy()

    scores = {label: float(prob) for label, prob in zip(LABELS, probs)}
    primary = max(scores, key=scores.get)

    return {"category": primary, "confidence": scores[primary], "all_scores": scores}

# Examples
print(predict("Vücudun çok güzel"))       # → SEXUAL
print(predict("Kimseye söyleme tamam mı")) # → GROOMING
print(predict("Ölmek istiyorum"))          # → SELF_HARM
print(predict("Bugün hava güzel"))         # → SAFE

Production Class

class TurkishSafetyClassifier:
    LABELS = ["SAFE", "OFFENSIVE", "SELF_HARM", "GROOMING", "BULLYING", "SEXUAL", "THREAT"]

    def __init__(self, model_path="hayatiali/turkish-safety"):
        self.tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-uncased")
        self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
        self.device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
        self.model.to(self.device).eval()

    def predict(self, text: str) -> dict:
        inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
        inputs = {k: v.to(self.device) for k, v in inputs.items()}

        with torch.no_grad():
            logits = self.model(**inputs).logits
            probs = torch.sigmoid(logits)[0].cpu().numpy()

        scores = dict(zip(self.LABELS, probs))
        primary = max(scores, key=scores.get)

        return {
            "category": primary,
            "confidence": scores[primary],
            "scores": scores,
            "action": self._get_action(scores[primary], primary)
        }

    def _get_action(self, score: float, category: str) -> str:
        # Critical categories have lower thresholds
        if category in ["GROOMING", "SEXUAL", "SELF_HARM", "THREAT"]:
            if score > 0.5: return "hard_block"
            if score > 0.3: return "soft_block"

        if score > 0.75: return "hard_block"
        if score > 0.60: return "soft_block"
        if score > 0.45: return "flag"
        if score > 0.30: return "allow_log"
        return "allow"

Batch Inference

def predict_batch(texts: list, batch_size: int = 32) -> list:
    results = []
    for i in range(0, len(texts), batch_size):
        batch = texts[i:i + batch_size]
        inputs = tokenizer(batch, return_tensors="pt", truncation=True, max_length=128, padding=True)
        inputs = {k: v.to(device) for k, v in inputs.items()}

        with torch.no_grad():
            probs = torch.sigmoid(model(**inputs).logits).cpu().numpy()

        for prob in probs:
            scores = dict(zip(LABELS, prob))
            results.append(scores)

    return results

Limitations & Known Issues

⚠️ Evaluation Limitations

Note: Two separate evaluation sets exist:

Automated Test Set: 17,033 samples from test.csv → Macro F1: 0.9165, MCC: 0.9045
Manual Edge Case Test: 22 hand-picked samples → 86.4% accuracy (19/22 correct)

Limitation	Details	Impact
Small Manual Test Set	Edge case validation on only 22 samples (86.4%)	Manual test not statistically significant; automated metrics (17K samples) more reliable
No Per-Class Metrics	Only Macro F1 and MCC reported for 17K test set	Cannot assess individual category performance (e.g., SELF_HARM Precision/Recall vs SAFE)
No Confusion Matrix	Category confusion patterns not documented	Unclear which categories are most confused beyond GROOMING/SEXUAL boundary
No PR/ROC Curves	Precision-Recall and ROC analysis not performed	Optimal threshold selection methodology not documented
No Calibration Analysis	Model confidence calibration not tested	Unknown if 0.7 confidence truly represents 70% probability

⚠️ Architectural Limitations

Limitation	Details	Impact
Short Context Window	Max sequence length: 64 tokens	Long messages may lose critical information; truncation may remove key context
Single-Turn Only	No conversation history analysis	GROOMING patterns often emerge across multiple messages ("Kaç yaşındasın?", "Nerelisin?", "Fotoğraf atar mısın?" may each appear SAFE individually)
No Temporal Patterns	No escalation detection capability	Cannot detect behavior changes over time; user history not considered
Static Analysis	Each message analyzed independently	Contextual red flags from message sequences not captured

⚠️ Data & Coverage Limitations

Limitation	Details	Impact
Dialect/Slang Gaps	Regional dialects and internet slang underrepresented	Performance may degrade on: "napıon", "nbr", "slm", "mrb", regional variations
No Adversarial Testing	Evasion techniques not systematically tested	Unknown robustness against: "S 3 x" instead of "sex", character substitution, unicode tricks
Synthetic Data Bias	97.5% of training data is LLM-generated	May not capture real-world linguistic patterns; potential distribution shift
Spelling Error Tolerance	Not explicitly tested	Common typos and intentional misspellings may bypass detection

⚠️ Production Deployment Considerations

Consideration	Details	Recommendation
Threshold Selection	Current thresholds (0.3, 0.5, 0.75) are heuristic	Perform PR curve analysis for your specific use case; adjust based on FP/FN tolerance
Confidence Calibration	Model may be over/under-confident	Consider temperature scaling or Platt calibration before production
Category Boundaries	GROOMING ↔ SEXUAL boundary is known issue	Review flagged content in these categories; implement human review for edge cases
Real-Time Context	No session-level analysis	Consider implementing sliding window or conversation aggregation layer

Not Suitable For

Languages other than Turkish
Adult content moderation (requires different domain expertise)
Sole decision-making without human review for high-stakes situations
Legal evidence or court proceedings
Detection of sophisticated, multi-turn grooming attempts without additional context layer
Highly informal/slang-heavy communications without additional preprocessing

Ethical Considerations

Intended Use

Social media content moderation
Messaging platform safety filters
Gaming chat moderation
Community forum monitoring
Parental control applications
Research and educational purposes

Risks

False Negatives: May miss sophisticated grooming attempts
False Positives: May flag benign content incorrectly
Automation Bias: Over-reliance on model predictions

Recommendations

Human Oversight: Always combine with human review for critical decisions
Threshold Calibration: Adjust thresholds based on your risk tolerance
Monitoring: Track performance metrics in production
Regular Updates: Retrain with new data periodically
Transparency: Inform users about automated moderation

Technical Specifications

Model Architecture

BertForSequenceClassification(
  (bert): BertModel(
    (embeddings): BertEmbeddings
    (encoder): BertEncoder (12 layers)
    (pooler): BertPooler
  )
  (dropout): Dropout(p=0.1)
  (classifier): Linear(in_features=768, out_features=7)
)

Total Parameters: ~110M
Trainable Parameters: ~110M

Input/Output

Input: Turkish text (max 128 tokens)
Output: 7-dimensional probability vector (sigmoid activated)
Tokenizer: BERTurk WordPiece (32k vocab)

Citation

@misc{turkish-safety-2025,
  title={Turkish Safety - Content Moderation Classifier},
  author={SiriusAI Tech Brain Team},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/hayatiali/turkish-safety}},
  note={Fine-tuned from dbmdz/bert-base-turkish-uncased, Macro F1: 0.9076}
}

Model Card Authors

SiriusAI Tech Brain Team

Contact

Issues: GitHub Issues
Repository: Omni-Moderation-API

Changelog

v5.0 (Current)

Major dataset expansion: 85,161 samples (68,128 train / 17,033 test)
Improved metrics: Macro F1: 0.9165, MCC: 0.9045
Optimized hyperparameters for large dataset (Focal Loss, cosine restarts)
67 subcategories across 7 main categories
86.4% validation accuracy on edge cases

v4.0

Initial production release
7-category multi-label content safety classification
Macro F1: 0.9076, MCC: 0.8931
Training on 30,596 samples
Clear category boundary definitions (GROOMING vs SEXUAL)
Optimized for real-time inference (<50ms)

License: SiriusAI Tech Premium License v1.0

Commercial Use: Requires Premium License. Contact: [email protected]

Free Use Allowed For:

Academic research and education
Non-profit organizations (with approval)
Evaluation (30 days)

Disclaimer: This model is designed for content moderation and safety applications. Always implement with appropriate safeguards and human oversight. Model predictions should inform decisions, not replace human judgment.

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for hayatiali/turkish-safety

Base model

dbmdz/bert-base-turkish-uncased

Finetuned

(31)

this model

Evaluation results

Macro F1
self-reported

0.916
Matthews Correlation Coefficient
self-reported

0.904