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--- |
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license: mit |
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tags: |
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- biology |
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- genomics |
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- dna-sequence |
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- bacterial-classification |
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- bert |
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- transformers |
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--- |
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# BBERT Pre-trained Models |
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Pre-trained models for [BBERT](https://github.com/AmirErez/BBERT) - BERT for Bacterial DNA Classification. |
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## Models Included |
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### 1. BBERT Transformer (`bbert_checkpoint-32500/`) |
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- Main BERT-based model trained on bacterial DNA sequences |
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- Hidden size: 768 |
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- Trained on diverse bacterial genomes |
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### 2. Bacterial Classifier (`bacterial_classifier/epoch_80.pt`) |
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- Binary classifier for bacterial vs. non-bacterial sequences |
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- Input: BBERT embeddings (768-dim) |
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- Trained for 80 epochs on 3.9M sequences |
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### 3. Reading Frame Classifier (`frame_classifier/classifier_model_2000K_37e.pth`) |
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- 6-way classifier for reading frame prediction |
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- Frames: +1, +2, +3, -1, -2, -3 |
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- Trained for 37 epochs on 2M sequences |
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### 4. Coding Sequence Classifier (`coding_classifier/epoch_46.pt`) |
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- Binary classifier for coding vs. non-coding sequences |
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- Trained for 46 epochs on 3.9M sequences |
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## Usage |
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These models are automatically downloaded when using BBERT: |
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\`\`\`bash |
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# First time setup |
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pip install bbert # or clone from GitHub |
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python source/download_models.py |
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# Then use normally |
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python bbert.py your_sequences.fasta --output_dir results |
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\`\`\` |
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## Citation |
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If you use BBERT, please cite: |
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[Add your citation here] |
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## License |
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MIT License - see LICENSE file for details |
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