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--- |
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language: en |
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license: mit |
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tags: |
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- text-classification |
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- toxicity-detection |
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- cnn |
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- byte-level |
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- ultra-lightweight |
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- cloudflare-optimized |
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datasets: |
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- balanced_toxicity_222k |
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metrics: |
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- f1 |
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- precision |
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- recall |
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model_type: cnn |
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inference: true |
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--- |
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# ByteCNN-10K: Ultra-Lightweight Toxicity Detection |
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Ultra-compact single-layer CNN for toxicity detection, optimized for maximum speed on edge deployment. |
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## Model Details |
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- **Architecture**: Single-layer ByteCNN (Embedding → Conv1D + BatchNorm → Dense → Dense) |
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- **Parameters**: 10,009 (~40KB) |
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- **Input**: Raw byte sequences (max 512 bytes) |
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- **Output**: Toxicity probability (0-1) |
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- **Optimization**: 72% parameter reduction from original 36K model |
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## Performance |
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- **Validation Accuracy**: 78.97% |
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- **Training Dataset**: Full balanced dataset (222,628 samples) |
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- **Efficiency**: 7.89% accuracy per 1K parameters (best efficiency in sweep) |
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- **Inference Speed**: <1ms on Cloudflare Workers |
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- **CPU Limits**: Guaranteed to stay under edge compute constraints |
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## Architecture Configuration |
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- **Embedding**: 256 vocab → 12 dimensions |
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- **Conv Layer**: 12 → 40 filters, kernel=3 |
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- **Dense Layer**: 40 → 128 hidden units |
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- **Output**: 128 → 1 (sigmoid activation) |
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## Training Details |
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- Trained on balanced dataset (50/50 toxic/safe ratio) |
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- 222,628 total samples from multiple sources |
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- AdamW optimizer with weight decay 0.01 |
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- Learning rate: 0.001 with ReduceLROnPlateau |
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- 10 epochs, batch size 128 |
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## Parameter Sweep Results |
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Comparison across different model sizes: |
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| Model | Parameters | Accuracy | Efficiency (Acc/1K params) | Trade-off | |
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|-------|------------|----------|---------------------------|-----------| |
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| Original | 36,257 | 81.20% | 2.24% | Baseline | |
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| **10K** | **10,009** | **78.97%** | **7.89%** | **72% fewer params** | |
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| 15K | 14,985 | 80.98% | 5.40% | 59% fewer params | |
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| 20K | 20,009 | 78.76% | 3.94% | 45% fewer params | |
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The 10K model offers the best parameter efficiency with minimal accuracy loss. |
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## Contextual Understanding |
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Despite its compact size, the model demonstrates sophisticated toxicity detection: |
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- **"fuck you"** → 87.28% toxic (direct personal attack) |
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- **"get fucked!"** → 17.73% safe (potentially playful/dismissive) |
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- **"Hello everyone!"** → 6.65% safe (clearly benign) |
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## Usage |
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Input text is converted to UTF-8 bytes, truncated/padded to 512 bytes, then processed through the CNN layers. |
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## Deployment |
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Optimized for edge deployment with: |
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- BatchNorm folding for inference |
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- Static weight embedding (211KB) |
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- Sub-1ms inference time |
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- Zero CPU timeout issues on Cloudflare Workers |
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## Live Demo |
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- **API**: https://bytecnn-demo.mitch-336.workers.dev/api/classify |
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- **Status**: https://bytecnn-demo.mitch-336.workers.dev/api/status |
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## Limitations |
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- English text optimized |
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- 512 byte context window |
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- Binary classification only (toxic/safe) |
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- 2.23% accuracy trade-off vs original for 72% size reduction |
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## Model Card |
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This model represents the optimal balance of speed and quality for production edge deployment. |
