
# GENA-LM (gena-lm-bigbird-base-sparse-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. 

`gena-lm-bigbird-base-sparse-t2t` follows the BigBird architecture and uses sparse attention from DeepSpeed.

Differences between GENA-LM (`gena-lm-bigbird-base-sparse-t2t`) and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 36000 nucleotides (4096 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

## Installation
`gena-lm-bigbird-base-sparse-t2t` sparse ops require DeepSpeed.

### DeepSpeed
DeepSpeed installation is needed to work with SparseAttention versions of language models. DeepSpeed Sparse attention supports only GPUs with compute compatibility >= 7 (V100, T4, A100).
```bash
pip install triton==1.0.0
DS_BUILD_SPARSE_ATTN=1 pip install deepspeed==0.6.0 --global-option="build_ext" --global-option="-j8" --no-cache
```
and check installation with
```bash
ds_report
```

### APEX for FP16
Install APEX https://github.com/NVIDIA/apex#quick-start
```
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
```

## Examples

### How to load pre-trained model for Masked Language Modeling
```python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse-t2t')
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse-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:
```bash
git clone https://github.com/AIRI-Institute/GENA_LM.git
```

```python
from GENA_LM.src.gena_lm.modeling_bert import BertForSequenceClassification
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse-t2t')
model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse-t2t')
```
or you can just download [modeling_bert.py](https://github.com/AIRI-Institute/GENA_LM/tree/main/src/gena_lm) and put it close to your code.

OR you can get model class from HuggingFace AutoModel:
```python
from transformers import AutoTokenizer, AutoModel
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse-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-bigbird-base-sparse-t2t', num_labels=2)
```

## Model description
GENA-LM (`gena-lm-bigbird-base-sparse-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-sparse-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
- Rotary positional embeddings
- 32k Vocabulary size, tokenizer trained on DNA data.

We pre-trained `gena-lm-bigbird-base-sparse-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 800,000 iterations with batch size 256. We modified Transformer with [Pre-Layer normalization](https://arxiv.org/abs/2002.04745).

## Evaluation
For evaluation results, see our paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1

## Citation
```bibtex
@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}
}
```
{"tags": ["dna", "human_genome"]}
transformers pytorch bert fill-mask dna human_genome custom_code arxiv:2002.04745 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #dna #human_genome #custom_code #arxiv-2002.04745 #autotrain_compatible #endpoints_compatible #region-us 


# GENA-LM (gena-lm-bigbird-base-sparse-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. 

'gena-lm-bigbird-base-sparse-t2t' follows the BigBird architecture and uses sparse attention from DeepSpeed.

Differences between GENA-LM ('gena-lm-bigbird-base-sparse-t2t') and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 36000 nucleotides (4096 BPE tokens) compared to 512 nucleotides of DNABERT;
- pre-training on T2T vs. GRCh38.p13 human genome assembly.

Source code and data: URL

Paper: URL

## Installation
'gena-lm-bigbird-base-sparse-t2t' sparse ops require DeepSpeed.

### DeepSpeed
DeepSpeed installation is needed to work with SparseAttention versions of language models. DeepSpeed Sparse attention supports only GPUs with compute compatibility >= 7 (V100, T4, A100).

and check installation with


### APEX for FP16
Install APEX URL


## Examples

### How to load pre-trained model for Masked Language Modeling


### How to load pre-trained model to fine-tune it on classification task
Get model class from GENA-LM repository:



or you can just download modeling_bert.py and put it close to your code.

OR you can get model class from HuggingFace AutoModel:


## Model description
GENA-LM ('gena-lm-bigbird-base-sparse-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-sparse-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
- Rotary positional embeddings
- 32k Vocabulary size, tokenizer trained on DNA data.

We pre-trained 'gena-lm-bigbird-base-sparse-t2t' using the latest T2T human genome assembly (URL The data was augmented by sampling mutations from 1000-genome SNPs (gnomAD dataset). Pre-training was performed for 800,000 iterations with batch size 256. We modified Transformer with Pre-Layer normalization.

## Evaluation
For evaluation results, see our paper: URL


# GENA-LM (gena-lm-bigbird-base-sparse-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. 

'gena-lm-bigbird-base-sparse-t2t' follows the BigBird architecture and uses sparse attention from DeepSpeed.

Differences between GENA-LM ('gena-lm-bigbird-base-sparse-t2t') and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 36000 nucleotides (4096 BPE tokens) compared to 512 nucleotides of DNABERT;
- pre-training on T2T vs. GRCh38.p13 human genome assembly.

Source code and data: URL

Paper: URL ## Installation
'gena-lm-bigbird-base-sparse-t2t' sparse ops require DeepSpeed. ### DeepSpeed
DeepSpeed installation is needed to work with SparseAttention versions of language models. DeepSpeed Sparse attention supports only GPUs with compute compatibility >= 7 (V100, T4, A100).

and check installation with ### APEX for FP16
Install APEX URL ## Examples ### How to load pre-trained model for Masked Language Modeling ### How to load pre-trained model to fine-tune it on classification task
Get model class from GENA-LM repository:



or you can just download modeling_bert.py and put it close to your code.

OR you can get model class from HuggingFace AutoModel: ## Model description
GENA-LM ('gena-lm-bigbird-base-sparse-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-sparse-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
- Rotary positional embeddings
- 32k Vocabulary size, tokenizer trained on DNA data.

We pre-trained 'gena-lm-bigbird-base-sparse-t2t' using the latest T2T human genome assembly (URL The data was augmented by sampling mutations from 1000-genome SNPs (gnomAD dataset). Pre-training was performed for 800,000 iterations with batch size 256. We modified Transformer with Pre-Layer normalization. ## Evaluation
For evaluation results, see our paper: URL
TAGS
#transformers #pytorch #bert #fill-mask #dna #human_genome #custom_code #arxiv-2002.04745 #autotrain_compatible #endpoints_compatible #region-us 
 # GENA-LM (gena-lm-bigbird-base-sparse-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. 

'gena-lm-bigbird-base-sparse-t2t' follows the BigBird architecture and uses sparse attention from DeepSpeed.

Differences between GENA-LM ('gena-lm-bigbird-base-sparse-t2t') and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 36000 nucleotides (4096 BPE tokens) compared to 512 nucleotides of DNABERT;
- pre-training on T2T vs. GRCh38.p13 human genome assembly.

Source code and data: URL

Paper: URL ## Installation
'gena-lm-bigbird-base-sparse-t2t' sparse ops require DeepSpeed. ### DeepSpeed
DeepSpeed installation is needed to work with SparseAttention versions of language models. DeepSpeed Sparse attention supports only GPUs with compute compatibility >= 7 (V100, T4, A100).

and check installation with ### APEX for FP16
Install APEX URL ## Examples ### How to load pre-trained model for Masked Language Modeling ### How to load pre-trained model to fine-tune it on classification task
Get model class from GENA-LM repository:



or you can just download modeling_bert.py and put it close to your code.

OR you can get model class from HuggingFace AutoModel: ## Model description
GENA-LM ('gena-lm-bigbird-base-sparse-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-sparse-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
- Rotary positional embeddings
- 32k Vocabulary size, tokenizer trained on DNA data.

We pre-trained 'gena-lm-bigbird-base-sparse-t2t' using the latest T2T human genome assembly (URL The data was augmented by sampling mutations from 1000-genome SNPs (gnomAD dataset). Pre-training was performed for 800,000 iterations with batch size 256. We modified Transformer with Pre-Layer normalization. ## Evaluation
For evaluation results, see our paper: URL
TAGS
#transformers #pytorch #bert #fill-mask #dna #human_genome #custom_code #arxiv-2002.04745 #autotrain_compatible #endpoints_compatible #region-us 
# GENA-LM (gena-lm-bigbird-base-sparse-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. 

'gena-lm-bigbird-base-sparse-t2t' follows the BigBird architecture and uses sparse attention from DeepSpeed.

Differences between GENA-LM ('gena-lm-bigbird-base-sparse-t2t') and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 36000 nucleotides (4096 BPE tokens) compared to 512 nucleotides of DNABERT;
- pre-training on T2T vs. GRCh38.p13 human genome assembly.

Source code and data: URL

Paper: URL## Installation
'gena-lm-bigbird-base-sparse-t2t' sparse ops require DeepSpeed.### DeepSpeed
DeepSpeed installation is needed to work with SparseAttention versions of language models. DeepSpeed Sparse attention supports only GPUs with compute compatibility >= 7 (V100, T4, A100).

and check installation with### APEX for FP16
Install APEX URL## Examples### How to load pre-trained model for Masked Language Modeling### How to load pre-trained model to fine-tune it on classification task
Get model class from GENA-LM repository:



or you can just download modeling_bert.py and put it close to your code.

OR you can get model class from HuggingFace AutoModel:## Model description
GENA-LM ('gena-lm-bigbird-base-sparse-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-sparse-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
- Rotary positional embeddings
- 32k Vocabulary size, tokenizer trained on DNA data.

We pre-trained 'gena-lm-bigbird-base-sparse-t2t' using the latest T2T human genome assembly (URL The data was augmented by sampling mutations from 1000-genome SNPs (gnomAD dataset). Pre-training was performed for 800,000 iterations with batch size 256. We modified Transformer with Pre-Layer normalization.## Evaluation
For evaluation results, see our paper: URL