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
File size: 1,067 Bytes
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language:
- en
license: mit
tags:
- genomics
- population-genetics
- transformers
- axial-attention
- self-supervised
- natural-selection
- haplotype
---
# Popformer
An axial attention transformer for haplotype matrices, pre-trained with self-supervised masked haplotype reconstruction.
**Paper:** [Popformer: Learning general signatures of positive selection with a self-supervised transformer](https://www.biorxiv.org/content/10.64898/2026.03.06.710163v1)
## Model Description
Popformer is pre-trained on masked haplotype reconstruction and evaluated on:
- Natural selection detection
- Genotype imputation
- Population classification
## Usage
See the [repository README](https://github.com/zongleon/popformer) for full preprocessing and inference examples, including VCF/HDF5 input and genome-wide selection scans.
## Citation
```bibtex
@article{popformer2026,
title = {Popformer: Learning general signatures of positive selection with a self-supervised transformer},
url = {https://www.biorxiv.org/content/10.64898/2026.03.06.710163v1}
}
``` |