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., using this download link 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. 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

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:

git clone https://github.com/AIRI-Institute/GENA_LM.git

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 and put it close to your code.

OR you can get model class from HuggingFace AutoModel:

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

@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},
}