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--- |
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tags: |
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- dna |
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- human_genome |
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--- |
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# GENA-LM (gena-lm-bigbird-base-t2t) |
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GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences. |
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GENA-LM models are transformer masked language models trained on human DNA sequence. |
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`gena-lm-bigbird-base-t2t` follows the BigBird architecture and its HuggingFace implementation. |
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Differences between GENA-LM (`gena-lm-bigbird-base-t2t`) and DNABERT: |
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- BPE tokenization instead of k-mers; |
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- input sequence size is about 36000 nucleotides (4096 BPE tokens) compared to 512 nucleotides of DNABERT; |
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- pre-training on T2T vs. GRCh38.p13 human genome assembly. |
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Source code and data: https://github.com/AIRI-Institute/GENA_LM |
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Paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594 |
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This repository also contains models that are finetuned on downstream tasks and models that are used in our [GENA-Web](https://dnalm.airi.net) web tool for genomic sequence annotation: |
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- splice sites prediction (branch [gena_web_spliceai](https://huggingface.co/AIRI-Institute/gena-lm-bigbird-base-t2t/tree/gena_web_spliceai)) |
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## Examples |
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### Load pre-trained model |
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```python |
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from transformers import AutoTokenizer, BigBirdForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t') |
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model = BigBirdForMaskedLM.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t') |
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``` |
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### How to load the model to fine-tune it on classification task |
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```python |
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from transformers import AutoTokenizer, BigBirdForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t') |
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model = BigBirdForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-t2t') |
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``` |
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## Model description |
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GENA-LM (`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`: |
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- 4096 Maximum sequence length |
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- 12 Layers, 12 Attention heads |
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- 768 Hidden size |
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- sparse config: |
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- block size: 64 |
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- random blocks: 3 |
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- global blocks: 2 |
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- sliding window blocks: 3 |
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- 32k Vocabulary size, tokenizer trained on DNA data. |
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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 mutations from 1000-genome SNPs (gnomAD dataset). Pre-training was performed for 1,070,000 iterations with batch size 256. |
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## Evaluation |
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For evaluation results, see our paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1 |
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## Citation |
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```bibtex |
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@article{GENA_LM, |
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author = {Veniamin Fishman and Yuri Kuratov and Maxim Petrov and Aleksei Shmelev and Denis Shepelin and Nikolay Chekanov and Olga Kardymon and Mikhail Burtsev}, |
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title = {GENA-LM: A Family of Open-Source Foundational Models for Long DNA Sequences}, |
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elocation-id = {2023.06.12.544594}, |
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year = {2023}, |
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doi = {10.1101/2023.06.12.544594}, |
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publisher = {Cold Spring Harbor Laboratory}, |
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URL = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594}, |
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eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594.full.pdf}, |
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journal = {bioRxiv} |
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} |
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``` |
