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zhangtaolab/dnabert2-conservation

sourceHugging Facecc-by-nc-sa-4.0updated 2y agoView on Hugging Face

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Model Card

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 zhihan1996/DNABERT-2-117M model with modified tokenizer.

This model is fine-tuned for predicting sequence conservation.

How to use

Install the runtime library first:

bash

pip install transformers

Here is a simple code for inference:

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

model_name = 'dnabert2-conservation'
# 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 = ['ACATGCTAAATTAGTTGGCAATTTTTTCTCAGGTAGCTGGGCACAATTTGGTAGTCCAGTTGAACAAAATCCATTAGCTTCTTTTAGCAAGTCCCCTGGTTTGGGCCCTGCCAGTCCCATTAATACCAACCATTTGTCTGGATTGGCTGCAATTCTTTCCCCACAAGCAACAACCTCTACCAAGATTGCACCGATTGGCAAGGACCCTGGAAGGGCTGCAAATCAGATGTTTTCTAACTCTGGATCAACACAAGGAGCAGCTTTTCAGCATTCTATATCCTTTCCTGAGCAAAATGTAAAGGCAAGTCCTAGGCCTATATCTACTTTTGGTGAATCAAGTTCTAGTGCATCAAGTATTGGAACACTGTCCGGTCCTCAATTTCTTTGGGGAAGCCCAACTCCTTACTCTGAGCATTCAAACACTTCTGCCTGGTCTTCATCTTCGGTGGGGCTTCCATTTACATCTAGTGTCCAAAGGCAGGGTTTCCCATATACTAGTAATCACAGTCCTTTTCTTGGCTCCCACTCTCATCATCATGTTGGATCTGCTCCATCTGGCCTTCCGCTTGATAGGCATTTTAGCTACTTCCCTGAGTCACCTGAAGCTTCTCTCATGAGCCCGGTTGCATTTGGGAATTTAAATCACGGTGATGGGAATTTTATGATGAACAACATTAGTGCTCGTGCATCTGTAGGAGCCGGTGTTGGTCTTTCTGGAAATACCCCTGAAATTAGTTCACCCAATTTCAGAATGATGTCTCTGCCTAGGCATGGTTCCTTGTTCCATGGAAATAGTTTGTATTCTGGACCTGGAGCAACTAACATTGAGGGATTAGCTGAACGTGGACGAAGTAGACGACCTGAAAATGGTGGGAACCAAATTGATAGTAAGAAGCTGTACCAGCTTGATCTTGACAAAATCGTCTGTGGTGAAGATACAAGGACTACTTTAATGATTAAAAACATTCCTAACAAGTAAGAATAACTAAACATCTATCCT',
             'GTCGCAAAAATTGGGCCACTTGCAGTTCAATCTGTTTAATCAAAATTGCATGTGTATCAACTTTTTGCCCAATACTAGCTATATCACACCTCAACTCTTTAATGTGTTCATCACTAGTGTCGAACCTCCTCATCATTTTGTCCAACATATCCTCAACTCGCGCCATACTATCTCCACCATCCCTAGGAGTAACTTCACGATTTTGAGGAGGGACATAGGGCCCATTCCTGTCGTTTCTATTAGCATAGTTACTCCTGTTAAAGTTGTTGTCGCGGTTGTAGTTTCCATCACGTACATAATGACTCTCACGGTTGTAGTTACCATAGTTCCGACCTGGGTTCCCTTGAACTTGGCGCCAGTTATCCTGATTTGAGCCTTGGGCGCTTGGTCGGAAACCCCCTGTCTGCTCATTTACTGCATAAGTGTCCTCCGCGTAACATCATTAGGAGGTGGTGGTTTAGCAAAGTAGTTGACTGCATTTATCTTTTCTGCACCCCCTGTGACATTTTTTAGTACCAACCCAAGCTCAGTTCTCATCTGAGACATTTCTTCTCGAATCTCATCTGTGGCTCGGTTGTGAGTGGACTGCACTACGAAGGTGTTTTTCCCTGTATCAAACTTCCTAGTACTCCAAGCTTTGTTATTTCGGGAGATTTTCTCTAGTTTTTCTGCAATCTCAACATAAGTGCATTCTCCATAAGATCCACCTGCTATAGTGTCCAACACCGCTTTATTGTTATCATCCTGTCCCCGATAGAAGTATTCCTTCAGTGACTCATCATCTATACGGTGATTTAGAACACTTCTCAAGAATGAGGTGAATCTATCCCAAGAACTACTAACTAACTCTCCTGGTAGTGCCACAAAGCTGTTCACCCTTTCTTTGTGGTTTAACTTCTTGGAGATCGGATAGTAGCGTGCTAAGAAGACATCCCTTAGTTGGTTCCAAGTGAATATGGAGTTGTATGCGAGCTTAGTGAACCACATTGCAGCCTCTCCC']
pipe = pipeline('text-classification', model=model, tokenizer=tokenizer,
                trust_remote_code=True, top_k=None)
results = pipe(sequences)
print(results)

Training data

We use BertForSequenceClassification 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).