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--- |
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license: apache-2.0 |
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language: |
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- it |
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- en |
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pipeline_tag: text-classification |
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--- |
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# 🛡️ Sentinella: Lightweight Content Safety Guardian |
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## 🎯 Model Overview |
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Sentinella is a compact yet powerful content safety classifier designed specifically for Italian language moderation. This model serves as your efficient first line of defense against harmful content. |
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### 📊 Key Metrics |
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- **Size**: 32M parameters |
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- **Accuracy**: 93% on test set |
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- **Max Input Length**: 8,192 tokens |
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- **Training Data**: more than 100,000 balanced examples (harmful/safe) |
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## 🔧 Technical Specifications |
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### Base Architecture |
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- **Base Model**: jinaai/jina-embeddings-v2-small-en |
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- **Model Adaptation**: |
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- Enhanced with a custom classifier head using a two-layer architecture |
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- Optimized dropout rate of 0.1 for regularization |
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- CLS token pooling strategy for sequence representation |
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- Implemented with cross-entropy loss for binary classification |
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### Classification Details |
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- **Output Labels**: |
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- NEGATIVE (0): Harmful content |
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- POSITIVE (1): Safe content |
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## 💫 Key Features |
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- **Lightweight**: At just 32M parameters, Sentinella is designed for efficiency |
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- **Long Context**: Handles up to 8k tokens of input text |
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- **High Performance**: 93% accuracy in content safety classification |
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- **Optimized Architecture**: Custom classification head with dimensionality reduction for improved efficiency |
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## 🚀 Use Cases |
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- Content moderation for Italian text |
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- Safe content filtering |
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- Automated content screening |
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- Real-time text analysis |
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## 🎓 Training Details |
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- **Training Dataset**: more than 100,000 examples |
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- Balanced distribution of safe and harmful content |
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- Focused on Italian language text |
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- **Training Strategy**: |
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- Fine-tuned embedding representation |
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- Intermediate layer dimensionality reduction |
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- ReLU activation for non-linearity |
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- Optimized dropout for regularization |
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## 📈 Performance Considerations |
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- Optimized for real-time classification |
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- Low memory footprint |
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- Efficient inference time |
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- Suitable for both CPU and GPU deployment |
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## 📝 Citation |
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If you use Sentinella in your research or application, please cite this work as: |
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``` |
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@model{sentinella, |
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title={Sentinella: Lightweight Italian Content Safety Classifier}, |
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year={2024}, |
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publisher={[Michele Montebovi]}, |
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note={32M parameter content safety model} |
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} |
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``` |
