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---
license: mit
base_model: microsoft/deberta-v3-xsmall
tags:
- safety
- content-moderation
- text-classification
- deberta
- guardreasoner
datasets:
- GuardReasoner
language:
- en
metrics:
- f1
- recall
- precision
- accuracy
library_name: transformers
pipeline_tag: text-classification
---
# L0 Bouncer (l0_bouncer_mega) - DeBERTa Safety Classifier
A fast, lightweight safety classifier based on DeBERTa-v3-xsmall (22M parameters) that serves as the first tier (L0) in a multi-tier safety cascade system.
**Variant**: Mega dataset iteration (2.5K samples)
## Performance Metrics
| Metric | Value |
|--------|-------|
| **F1 Score** | 85.6% |
| **Recall** | 91% |
| **Precision** | 81% |
| **Accuracy** | 87.8% |
| **Training Samples** | 2,500 |
| **Training Steps** | ~750 |
| **Mean Latency** | ~5.7ms |
## Model Description
The L0 Bouncer is designed for **high-throughput, low-latency safety screening** of text inputs. It provides binary classification (safe vs. harmful) with a focus on maximizing recall to catch potentially harmful content.
### Key Features
- **Ultra-fast inference**: ~5.7ms per input
- **Lightweight**: Only 22M parameters
- **Production-ready**: Designed for real-time content moderation
## Training Data
Trained on the GuardReasoner dataset, which contains diverse examples of safe and harmful content with reasoning annotations.
### Training Details
- **Base Model**: microsoft/deberta-v3-xsmall
- **Learning Rate**: 2e-5
- **Batch Size**: 32 (effective, with gradient accumulation)
- **Max Sequence Length**: 256 tokens
- **Class Weighting**: Higher weight on harmful class for better recall
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
model_name = "vincentoh/deberta-v3-xsmall-l0-bouncer-mega"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Classify text
text = "What is the capital of France?"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=-1)
# Labels: 0 = safe, 1 = harmful
safe_prob = probs[0][0].item()
harmful_prob = probs[0][1].item()
label = "safe" if safe_prob > harmful_prob else "harmful"
confidence = max(safe_prob, harmful_prob)
print(f"Label: {label}, Confidence: {confidence:.2%}")
```
## Model Variants
| Variant | Samples | F1 | Recall | Best For |
|---------|---------|----|----|----------|
| [l0-bouncer-12k](https://huggingface.co/vincentoh/deberta-v3-xsmall-l0-bouncer) | 12K | 93% | 99% | Balanced performance |
| [l0-bouncer-full](https://huggingface.co/vincentoh/deberta-v3-xsmall-l0-bouncer-full) | 124K | 95.2% | 97% | Maximum accuracy |
| [l0-bouncer-mega](https://huggingface.co/vincentoh/deberta-v3-xsmall-l0-bouncer-mega) | 2.5K | 85.6% | 91% | Lightweight/iterative |
## Cascade Architecture
This model is designed to work as the first tier (L0) in a multi-tier safety cascade:
```
Input → L0 Bouncer (6ms) → 70% pass through
↓ 30% escalate
L1 Analyst (50ms) → Deeper reasoning
↓
L2 Gauntlet (200ms) → Expert ensemble
```
## License
MIT License - Free for commercial and non-commercial use.
## Citation
```bibtex
@misc{l0-bouncer-2024,
author = {Vincent Oh},
title = {L0 Bouncer: A Fast Safety Classifier for Content Moderation},
year = {2024},
publisher = {HuggingFace},
url = {https://huggingface.co/vincentoh/deberta-v3-xsmall-l0-bouncer-mega}
}
```
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