content-moderation-distilbert

🛡️ Content Moderation Model (DistilBERT Fine-Tuned)

Model Description

This model is based on DistilBERT and fine-tuned on the Jigsaw Toxic Comment Classification dataset for multi-label content moderation.
It can detect multiple forms of toxic or harmful language in English comments, including:

• toxic
• severe_toxic
• obscene
• threat
• insult
• identity_hate
• safe (added when no toxic categories apply)

DistilBERT is a smaller, faster version of BERT while retaining most of its performance, making it suitable for real-time moderation tasks.

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🏷️ Intended Use

• Moderating online communities (forums, chat apps, social media).
• Filtering toxic comments in user-generated text.
• Educational and research purposes.

⚠️ Not for production without further evaluation. Models may misclassify sarcasm, slang, or subtle harassment. Always use human moderators for critical cases.

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📊 Training Details

• Base model: distilbert-base-uncased
• Dataset: Jigsaw Toxic Comment Classification
• Task: Multi-label classification (comments can have multiple labels).
• Labels: toxic, severe_toxic, obscene, threat, insult, identity_hate, safe
• Hyperparameters:
  • Batch size: 16 (train), 32 (eval)
  • Learning rate: 1e-5
  • Epochs: 5
  • Weight decay: 0.01
• Metric: Macro-F1

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📈 Evaluation Results

Validation performance (held-out split):

• Macro-F1 Score: ~0.91
• Training loss stabilized around ~0.14
• Validation loss ~0.15

✅ Strong at detecting direct insults and threats.
⚠️ Struggles with sarcasm or subtle negativity.

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🚀 How to Use

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="your-username/content-moderation-distilbert",
    tokenizer="your-username/content-moderation-distilbert",
    return_all_scores=True,
    function_to_apply="sigmoid"   # multi-label classification
)

texts = [
    "You are an amazing person and I respect you.",
    "I hate you, you are disgusting!",
    "I will not kill you tomorrow"
]

for t in texts:
    preds = classifier(t)
    print(f"\nInput: {t}")
    for label in preds[0]:
        print(f"{label['label']}: {label['score']:.3f}")