readme: ---
  tags:
    - DNA
  license: mit
  ---
  
  ## MiniDNA mini model
  
  This is a distilled version of [DNABERT](https://github.com/jerryji1993/DNABERT) by using MiniLM technique. It has a BERT architecture with 3 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](http://www.diva-portal.org/smash/record.jsf?dswid=846&pid=diva2%3A1676068&c=1&searchType=SIMPLE&language=en&query=joana+palés&af=%5B%5D&aq=%5B%5B%5D%5D&aq2=%5B%5B%5D%5D&aqe=%5B%5D&noOfRows=50&sortOrder=author_sort_asc&sortOrder2=title_sort_asc&onlyFullText=false&sf=all)..
  
  
  ## How to Use  
  
  The model can be used to fine-tune on a downstream genomic task, e.g. promoter identification.
  
  ```python
  import torch
  from transformers import BertForSequenceClassification
  model = BertForSequenceClassification.from_pretrained('Peltarion/dnabert-minilm-mini')
  ```
  
  More details on how to fine-tune the model, dataset and additional source codes are available on [github.com/joanaapa/Distillation-DNABERT-Promoter](https://github.com/joanaapa/Distillation-DNABERT-Promoter).