metadata

license: cc-by-nc-sa-4.0
widget:
  - text: >-
      AGTCCAGTGGACGACCAGCCACGGCTCCGGTCTGTAGAACCATCGCGGAAACGGCTCGCAAAACTCTAAACAGCGCAAACGATGCGCGCGCCGAAGCAACCCGGCTCTACTTATAAAAACGTCCAACGGTGAGCACCGAGCAGCTACTACTCGTACTCCCCCCACCGATC
tags:
  - DNA
  - biology
  - genomics
datasets:
  - zhangtaolab/plant-multi-species-promoter-strength
metrics:
  - r_squared
base_model:
  - zhangtaolab/plant-dnagemma-BPE

Plant foundation DNA large language models

The plant DNA large language models (LLMs) contain a series of foundation models based on different model architectures, which are pre-trained on various plant reference genomes.
All the models have a comparable model size between 90 MB and 150 MB, BPE tokenizer is used for tokenization and 8000 tokens are included in the vocabulary.

Developed by: zhangtaolab

Model Sources

Repository: Plant DNA LLMs
Manuscript: Versatile applications of foundation DNA large language models in plant genomes

Architecture

The model is trained based on the Google Gemma model with modified config and tokenizer specific for DNA sequence.

This model is fine-tuned for predicting promoter strength in tobacco leaves system.

How to use

Install the runtime library first:

pip install transformers

Here is a simple code for inference:

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model_name = 'plant-dnagemma-BPE-promoter_strength_leaf'
# load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)

# inference
sequences = ['TACTCTAATCGTATCAGCTGCACTTGCGTACAGGCTACCGGCGTCCTCAGCCACGTAAGAAAAGGCCCAATAAAGGCCCAACTACAACCAGCGGATATATATACTGGAGCCTGGCGAGATCACCCTAACCCCTCACACTCCCATCCAGCCGCCACCAGGTGCAGAGTGTT',
             'ATTTCAAAACTAGTTTTCTATAAACGAAAACTTATATTTATTCCGCTTGTTCCGTTTGATCTGCTGATTCGACACCGTTTTAACGTATTTTAAGTAAGTATCAGAAATATTAATGTGAAGATAAAAGAAAATAGAGTAAATGTAAAGGAAAATGCATAAGATTTTGTTGA']
pipe = pipeline('text-classification', model=model, tokenizer=tokenizer,
                trust_remote_code=True, function_to_apply="none")
results = pipe(sequences)
print(results)

Training data

We use GemmaForSequenceClassification to fine-tune the model.
Detailed training procedure can be found in our manuscript.

Hardware

Model was trained on a NVIDIA GTX1080Ti GPU (11 GB).