tags:
- dna
- human_genome
GENA-LM (gena-lm-bert-base)
GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
GENA-LM models are transformer masked language models trained on human DNA sequence.
Differences between GENA-LM (gena-lm-bert-base) and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 4500 nucleotides (512 BPE tokens) compared to 512 nucleotides of DNABERT
- pre-training on T2T vs. GRCh38.p13 human genome assembly.
Source code and data: https://github.com/AIRI-Institute/GENA_LM
Paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1
Examples
How to load the model to fine-tune it on classification task
from src.gena_lm.modeling_bert import BertForSequenceClassification
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bert-base')
model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bert-base')
Model description
GENA-LM (gena-lm-bert-base) model is trained in a masked language model (MLM) fashion, following the methods proposed in the BigBird paper by masking 15% of tokens. Model config for gena-lm-bert-base 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 using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). Pre-training was performed for 500,000 iterations with the same parameters as in BigBird, except sequence length was equal to 512 tokens. We modified Transformer with Pre-Layer normalization, but without the final layer LayerNorm.
Evaluation
For evaluation results, see our paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1
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 Models for Long DNA 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/2023/06/13/2023.06.12.544594},
eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594.full.pdf},
journal = {bioRxiv}
}