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---
tags:
- dna
---
# GENA-LM Athaliana 🌱 (gena-lm-bert-base-athaliana)
GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
`gena-lm-bert-base-athaliana` is trained on Arabidopsis thaliana genome.
## Model description
GENA-LM (`gena-lm-bert-base-athaliana`) model is trained with a masked language model (MLM) objective, following data preprocessing methods pipeline in the BigBird paper and by masking 15% of tokens. Model config for `gena-lm-bert-base-athaliana` is similar to the bert-base:
- 512 Maximum sequence length
- 12 Layers, 12 Attention heads
- 768 Hidden size
- 32k Vocabulary size
We pre-trained `gena-lm-bert-base-athaliana` on data obtained from [Kang et al.](https://doi.org/10.1038/s41467-023-42029-4), using this download [link](https://figshare.com/ndownloader/files/41661786) and contains chromosome-level genomes of 32 A. thaliana ecotypes.
Pre-training was performed for 1,700,000 iterations with batch size 256 and sequence length was equal to 512 tokens. We modified Transformer to use [Pre-Layer normalization](https://arxiv.org/abs/2002.04745). We upload the checkpoint with the best loss on validation set (iteration 425000) to `main` branch and the latest checkpoint to `step_1700000` branch.
Source code and data: https://github.com/AIRI-Institute/GENA_LM
Paper: https://academic.oup.com/nar/article/53/2/gkae1310/7954523
## Examples
### How to load pre-trained model for Masked Language Modeling
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bert-base-athaliana')
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bert-base-athaliana', trust_remote_code=True)
```
### How to load pre-trained model to fine-tune it on classification task
Get model class from GENA-LM repository:
```bash
git clone https://github.com/AIRI-Institute/GENA_LM.git
```
```python
from GENA_LM.src.gena_lm.modeling_bert import BertForSequenceClassification
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bert-base-athaliana')
model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bert-base-athaliana')
```
or you can just download [modeling_bert.py](https://github.com/AIRI-Institute/GENA_LM/tree/main/src/gena_lm) and put it close to your code.
OR you can get model class from HuggingFace AutoModel:
```python
from transformers import AutoTokenizer, AutoModel
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bert-base-athaliana', trust_remote_code=True)
gena_module_name = model.__class__.__module__
print(gena_module_name)
import importlib
# available class names:
# - BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction,
# - BertForSequenceClassification, BertForMultipleChoice, BertForTokenClassification,
# - BertForQuestionAnswering
# check https://huggingface.co/docs/transformers/model_doc/bert
cls = getattr(importlib.import_module(gena_module_name), 'BertForSequenceClassification')
print(cls)
model = cls.from_pretrained('AIRI-Institute/gena-lm-bert-base-athaliana', num_labels=2)
```
## Evaluation
For evaluation results, see our paper: https://academic.oup.com/nar/article/53/2/gkae1310/7954523
## Citation
```bibtex
@article{GENA_LM,
author = {Fishman, Veniamin and Kuratov, Yuri and Shmelev, Aleksei and Petrov, Maxim and Penzar, Dmitry and Shepelin, Denis and Chekanov, Nikolay and Kardymon, Olga and Burtsev, Mikhail},
title = {GENA-LM: a family of open-source foundational DNA language models for long sequences},
journal = {Nucleic Acids Research},
volume = {53},
number = {2},
pages = {gkae1310},
year = {2025},
month = {01},
issn = {0305-1048},
doi = {10.1093/nar/gkae1310},
url = {https://doi.org/10.1093/nar/gkae1310},
eprint = {https://academic.oup.com/nar/article-pdf/53/2/gkae1310/61443229/gkae1310.pdf},
}
``` |