tags:
- dna
- human_genome
GENA-LM (BigBird-base T2T)
GENA-LM (BigBird-base T2T) is a transformer masked language model trained on human DNA sequence. GENA-LM (BigBird-base T2T) follows BigBird architecture.
Differences between GENA-LM (BigBird-base T2T) and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 24000 nucleotides (4096 BPE tokens) compared to 510 nucleotides of DNABERT;
- pre-training on T2T vs. GRCh38.p13 human genome assembly.
Source code and data: https://github.com/AIRI-Institute/GENA_LM
Examples
Load pre-trained model
from transformers import AutoTokenizer, BigBirdForMaskedLM
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t')
model = BigBirdForMaskedLM.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t')
How to load the model to fine-tune it on classification task
from transformers import AutoTokenizer, BigBirdForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t')
model = BigBirdForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t')
Model description
GENA-LM (BigBird-base 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-bigbird-base-t2t is similar to the google/bigbird-roberta-base:
- 4096 Maximum sequence length
- 12 Layers, 12 Attention heads
- 768 Hidden size
- sparse config:
- block size: 64
- random blocks: 3
- global blocks: 2
- sliding window blocks: 3
- 32k Vocabulary size, tokenizer trained on DNA data.
We pre-trained gena-lm-bigbird-base-t2t using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). The data was augmented by sampling SNPs human mutations. Pre-training was performed for 1,070,000 iterations with batch size 256.