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README.md

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+---
+license: mit
+language:
+- en
+tags:
+- text-classification
+- onnx
+- bert
+- torrent
+- content-classification
+base_model: prajjwal1/bert-tiny
+pipeline_tag: text-classification
+---
+
+# BERT Torrent Classifier
+
+A fine-tuned BERT-tiny model for classifying torrent content into media types.
+
+## Model Details
+
+- **Base model:** [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny)
+- **Task:** Multi-class text classification
+- **Labels:** audio, video, software, book, other
+- **Format:** ONNX (with embedded weights)
+- **Size:** ~17MB
+
+## Training
+
+- **Training data:** ~10k torrent names with 4-LLM consensus voting
+- **LLM ensemble:** qwen2.5:3b, gemma3:4b, mistral:7b, qwen3-coder:30b
+- **Consensus rules:** 4-agree = high confidence, 3v1 = majority vote, 2v2 = discarded
+- **Accuracy:** ~92% on held-out test set
+
+## Usage
+
+This model is designed for use with [mimmo](https://github.com/lelloman/mimmo), a Rust library for torrent content classification. The ONNX model is embedded directly in the binary at compile time.
+
+```rust
+// Model is automatically downloaded during build
+const MODEL_BYTES: &[u8] = include_bytes!("../models/bert/model_embedded.onnx");
+const TOKENIZER_JSON: &str = include_str!("../models/bert/tokenizer.json");
+```
+
+## Performance
+
+- Inference: <10ms per sample (CPU)
+- Used as ML fallback when pattern matching is inconclusive
+
+## Files
+
+- `model_embedded.onnx` - ONNX model with embedded weights
+- `tokenizer.json` - HuggingFace tokenizer
+- `vocab.txt` - Vocabulary file
+- `config.json` - Model configuration
+
+## License
+
+MIT