| license: mit | |
| pipeline_tag: text-classification | |
| # BarcodeMamba+: Advancing State-Space Models for Fungal Biodiversity Research | |
| BarcodeMamba+ is a foundation model for fungal barcode classification, built on a powerful and efficient state-space model architecture. It addresses critical challenges in fungal taxonomic classification, such as sparse labelling and long-tailed taxa distributions, by employing a pretrain and fine-tune paradigm. The model integrates various enhancements, including hierarchical label smoothing, a weighted loss function, and a multi-head output layer, to achieve significant performance gains over traditional supervised methods. | |
| - Check out our [paper](https://huggingface.co/papers/2512.15931) | |
| - Check out our [code](https://github.com/bioscan-ml/BarcodeMamba) | |
| - Check out our [poster](https://neurips.cc/media/PosterPDFs/NeurIPS%202024/105938.png) | |
| # Usage | |
| The pretrained models can be used for both taxonomic classification on seen species (fine-tune & linear probe) and making genus-level predictions on unseen species (1-NN probe). The instructions for using our models can be found at our [GitHub repository](https://github.com/bioscan-ml/BarcodeMamba). | |
| # Citation | |
| If you find BarcodeMamba useful, please consider citing: | |
| ``` | |
| @inproceedings{ | |
| gao2024barcodemamba, | |
| title={BarcodeMamba: State Space Models for Biodiversity Analysis}, | |
| author={Tiancheng Gao and Graham W.~Taylor}, | |
| booktitle={{NeurIPS} 2024 Workshop on Foundation Models for Science: Progress, Opportunities, and Challenges}, | |
| year={2024}, | |
| url={https://openreview.net/forum?id=6ohFEFTr10} | |
| } | |
| ``` |