architkaila/Fine-Tuning-LLMs-for-Medical-Entity-Extraction
Exploring the potential of fine-tuning Large Language Models (LLMs) like Llama2 and StableLM for medical entity extraction. This project focuses on adapting these models using PEFT, Adapter V2, and LoRA techniques to efficiently and accurately extract drug names and adverse side-effects from pharmaceutical texts
PythonMIT
Stargazers
- 0wnway
- acse-sc4623
- Alvaro8gbMadrid, Spain
- ArclyAI
- bugarin10Durham, NC
- devanshkhandekarIndia
- EdisonWhaleGeorgia Institute of Technology
- edmondzhang82
- erikcalcina
- fcertain82
- ftcku
- gpannetiJS developer, AI enthusiastic
- hinriksnaerNueron
- Jin66
- jjreifDuke University
- jli0117
- jpabbuehlSwitzerland
- kiranbeethojuCallHealth
- kishik
- kumarmtp
- Leude
- liquid36@andes @movilizame
- mail4y
- Mike-Xiao
- pythoncodeAshisHMahyco
- rezwanh001Dhaka, Bangladesh
- samjm000DTC - Doctors that Code
- ScottSucksAtProgrammingNew York
- showersky@springernature
- SpicyCatGamesBangladesh
- uiinlee
- Vishnu9535
- vveizhang
- XiaoXi14
- zhenweidingDHC
- zhoujieliPalo Alto