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
This project helps pharmaceutical companies and medical safety professionals automatically identify crucial information from adverse event reports. It takes in free-text emails describing patient experiences and extracts specific drug names and reported side effects. This solution is designed for pharmacovigilance teams, regulatory affairs specialists, and data analysts who need to efficiently process large volumes of patient feedback.
No commits in the last 6 months.
Use this if you need to quickly and accurately pull drug names and adverse side effects from unstructured text, especially from patient-reported emails, to enhance drug safety monitoring.
Not ideal if your primary need is general information extraction beyond drug names and adverse events, or if you require analysis of structured medical records rather than free-text reports.
Stars
89
Forks
13
Language
Python
License
MIT
Category
Last pushed
Dec 13, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/architkaila/Fine-Tuning-LLMs-for-Medical-Entity-Extraction"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
OptimalScale/LMFlow
An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
adithya-s-k/AI-Engineering.academy
Mastering Applied AI, One Concept at a Time
jax-ml/jax-llm-examples
Minimal yet performant LLM examples in pure JAX
young-geng/scalax
A simple library for scaling up JAX programs
riyanshibohra/TuneKit
Upload your data → Get a fine-tuned SLM. Free.