Instructions to use Peltarion/dnabert-minilm-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Peltarion/dnabert-minilm-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("Peltarion/dnabert-minilm-small") model = AutoModelForPreTraining.from_pretrained("Peltarion/dnabert-minilm-small") - Notebooks
- Google Colab
- Kaggle
MiniDNA small model
This is a distilled version of DNABERT by using MiniLM technique. It has a BERT architecture with 6 layers and 384 hidden units, pre-trained on 6-mer DNA sequences. For more details on the pre-training scheme and methods, please check the original thesis report..
How to Use
The model can be used to fine-tune on a downstream genomic task, e.g. promoter identification.
import torch
from transformers import BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained('Peltarion/dnabert-minilm-small')
More details on how to fine-tune the model, dataset and additional source codes are available on github.com/joanaapa/Distillation-DNABERT-Promoter.
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