metadata

license: isc
tags:
  - biology
  - code
  - medical

TinyDNABERT

🌟 Overview

TinyDNABERT is a specialized deep learning model designed for understanding the language of DNA and performing DNA sequence classification tasks. This model is a compact and efficient version of the DNABERT model, optimized to reduce memory usage while maintaining high performance. TinyDNABERT is particularly well-suited for tasks where computational efficiency and fast inference times are crucial.

This repository provides all the necessary scripts and configurations to fine-tune TinyDNABERT on various DNA-related tasks using LoRA (Low-Rank Adaptation) configurations, enabling efficient adaptation to specific DNA sequence classification problems.

🚀 Key Features:

Compact & Efficient: Smaller memory footprint with fast inference times.
LoRA Fine-Tuning: Leverage Low-Rank Adaptation for quick and effective model tuning.
Task-Specific Adaptability: Fine-tune the model for various DNA-related tasks with ease.