🚸 NSFK Detection (yasserrmd/nsfk-detection)

NSFK Detection is a robust transformer-based text classification model designed to identify content that is Not Suitable for Kids (NSFK), built with a three-category system:

• ✅ suitable_for_kids
• 🚫 not_suitable_for_kids
• ❓ uncertain (confidence-based)

Fine-tuned on 60K examples and evaluated on a 1000-sample test set with high accuracy and safety guarantees, this model is ideal for content moderation in educational platforms, video platforms, and chatbot systems.

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🔧 Usage Example

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import json

model_name = "yasserrmd/nsfk-detection"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Load label map
with open('./results/checkpoint-4467/label_map.json', 'r') as f:
    label_map = json.load(f)
id_to_label = {i: label for label, i in label_map.items()}

threshold = 0.7  # Confidence threshold for classification

def classify(text):
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)[0]
    pred_id = torch.argmax(probs).item()
    confidence = probs[pred_id].item()
    return (id_to_label[pred_id] if confidence >= threshold else "uncertain", confidence)

text = "The movie contained graphic violence."
label, confidence = classify(text)
print(f"Label: {label}, Confidence: {confidence:.2f}")

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📊 Performance Summary

Evaluation Dataset: 1,000 samples (500 per class)
Confidence Threshold: 0.7

Metric	Value
Accuracy (excluding uncertain)	92.91%
Precision (NSFK)	99.00%
Recall (NSFK)	85.00%
F1 Score (NSFK)	92.00%
Uncertain Predictions	11.20%

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🔎 Uncertainty Distribution

Among 112 uncertain cases:

• 🔥 Conflict/War: 36%
• ⚖️ Legal/Crime: 11%
• 🏛️ Political: 6%
• 🧪 Educational (Borderline): 6%
• 🧠 Other Sensitive/Controversial Topics: 38%

These cases are ideal for manual review pipelines.

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✅ Key Benefits

• Three-label output prevents overconfident mistakes
• High recall and precision on critical unsafe content
• Safe defaults — never misclassifies safe content as unsafe
• Adaptable threshold based on domain risk (e.g., 0.75 for children-only platforms)

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🧠 Learn More

See the Large-Scale Analysis Report (PDF) for detailed metrics, sample predictions, and category-wise breakdowns.

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👨‍💻 Author

Mohamed Yasser

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