tags:
- dna
- human_genome
GENA-LM (gena-lm-bert-base-lastln-t2t-lastln-t2t)
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-lastln-t2t-lastln-t2t) 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 pre-trained model for Masked Language Modeling
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bert-base-lastln-t2t')
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bert-base-lastln-t2t', 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-lastln-t2t')
model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bert-base-lastln-t2t')
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-lastln-t2t', 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-lastln-t2t', num_labels=2)
Model description
GENA-LM (gena-lm-bert-base-lastln-t2t-lastln-t2t) 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-lastln-t2t-lastln-t2t 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-lastln-t2t-lastln-t2t using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). The data was augmented by sampling mutations from 1000-genome SNPs (gnomAD dataset). Pre-training was performed for 2,100,000 iterations with batch size 256 and sequence length was equal to 512 tokens. We modified Transformer with Pre-Layer normalization.
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}
}