GENA-LM Fly 🪰 (gena-lm-bert-base-fly)
GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
gena-lm-bert-base-fly is trained on drosophila genome.
Model description
GENA-LM (gena-lm-bert-base-fly) 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-fly 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-fly on data obtained from Progressive Cactus alignment of 298 drosophilid species generated by Kim et al., dataset source: link.
Pre-training was performed for 1,925,000 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.
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-fly')
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bert-base-fly', 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-fly')
model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bert-base-fly')
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-fly', 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-fly', 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},
}