tags:
- dna
GENA-LM Yeast 🍞 (gena-lm-bert-base-yeast)
GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
gena-lm-bert-base-yeast is trained on the baker’s yeast (Saccharomyces cerevisiae) genome.
Model description
GENA-LM (gena-lm-bert-base-yeast) 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-yeast 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-yeast on data obtained from O’Donnell et al. and includes telomere-to-telomere assemblies of 142 strains. Specific accessions are available here.
Pre-training was performed for 3,325,000 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://www.biorxiv.org/content/10.1101/2023.06.12.544594
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-yeast')
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bert-base-yeast', 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-yeast')
model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bert-base-yeast')
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-yeast', 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-yeast', num_labels=2)
Evaluation
For evaluation results, see our paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594
Citation
@article{GENA_LM,
author = {Veniamin Fishman and Yuri Kuratov and Maxim Petrov and Aleksei Shmelev and Denis Shepelin and Nikolay Chekanov and Olga Kardymon and Mikhail Burtsev},
title = {GENA-LM: A Family of Open-Source Foundational DNA Language Models for Long Sequences},
elocation-id = {2023.06.12.544594},
year = {2023},
doi = {10.1101/2023.06.12.544594},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2023/11/01/2023.06.12.544594},
eprint = {https://www.biorxiv.org/content/early/2023/11/01/2023.06.12.544594.full.pdf},
journal = {bioRxiv}
}