Upload L0 Bouncer l0_bouncer_full: Full GuardReasoner dataset (124K samples), 10K+ training steps

README.md

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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_full) - 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**: Full GuardReasoner dataset (124K samples), 10K+ training steps
+
+## Performance Metrics
+
+| Metric | Value |
+|--------|-------|
+| **F1 Score** | 95.2% |
+| **Recall** | 97% |
+| **Precision** | 93.5% |
+| **Accuracy** | 95.2% |
+| **Training Samples** | 124,000 |
+| **Training Steps** | 10,719 |
+| **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-full"
+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-full}
+}
+```

added_tokens.json

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config.json

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+}

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