|
--- |
|
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} |
|
} |
|
|
