Fill-Mask
Transformers
Safetensors
English
roberta
genomics
population-genetics
axial-attention
self-supervised
natural-selection
haplotype
Instructions to use leonzong/popf-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leonzong/popf-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="leonzong/popf-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("leonzong/popf-small") model = AutoModelForMaskedLM.from_pretrained("leonzong/popf-small") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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---
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language:
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- en
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license: mit
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tags:
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- genomics
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- population-genetics
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- transformers
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- axial-attention
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- self-supervised
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- natural-selection
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- haplotype
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---
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# Popformer
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An axial attention transformer for haplotype matrices, pre-trained with self-supervised masked haplotype reconstruction.
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**Paper:** [Popformer: Learning general signatures of positive selection with a self-supervised transformer](https://www.biorxiv.org/content/10.64898/2026.03.06.710163v1)
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## Model Description
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Popformer is pre-trained on masked haplotype reconstruction and evaluated on:
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- Natural selection detection
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- Genotype imputation
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- Population classification
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## Usage
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See the [repository README](https://github.com/zongleon/popformer) for full preprocessing and inference examples, including VCF/HDF5 input and genome-wide selection scans.
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## Citation
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```bibtex
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@article{popformer2026,
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title = {Popformer: Learning general signatures of positive selection with a self-supervised transformer},
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url = {https://www.biorxiv.org/content/10.64898/2026.03.06.710163v1}
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}
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```
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