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.
Please Cite As:
@misc{peerzada_fabiha_akmal_makhdoomi_2024,
author = {Peerzada Fabiha Akmal Makhdoomi, Nimisha Ghosh},
title = {TinyDNABERT},
year = 2024,
url = {https://huggingface.co/fabihamakhdoomi/TinyDNABERT},
doi = {10.57967/hf/2886},
publisher = {Hugging Face}
}