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---
tags:
- dna
- human_genome
---
# WARNING
This readme should be changed according to current model. Num steps: 810000
# GENA-LM
GENA-LM is a transformer masked language model trained on human DNA sequence.
Differences between GENA-LM and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 3000 nucleotides (512 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
### How to load the model to fine-tune it on classification task
```python
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 model is trained in a masked language model (MLM) fashion, following the methods proposed in the BigBird paper by masking 85% 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 and we used pre-layer normalization in Transformer.
## Downstream tasks
Currently, gena-lm-bert-base model has been finetuned and tested on promoter prediction task. Its' performance is comparable to previous SOTA results. We plan to fine-tune and make available models for other downstream tasks in the near future.
### Fine-tuning GENA-LM on our data and scoring
After fine-tuning gena-lm-bert-base on promoter prediction dataset, following results were achieved:
| model | seq_len (bp) | F1 |
|--------------------------|--------------|-------|
| DeePromoter | 300 | 95.60 |
| GENA-LM bert-base (ours) | 2000 | 95.72 |
| BigBird | 16000 | 99.90 |
We can conclude that our model achieves comparable performance to the previously published results for promoter prediction task.
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