This article investigates the performance of models on social media posts by examining different Transformer architectures. It evaluates the effectiveness of fine-tuning and prompt-based methods, aiming to identify best practices and trade-offs through performance comparisons across various models.
Topic modeling, particularly with advanced tools like BERTopic, presents a promising approach for extracting insights into SDoH from unstructured text.
“AI for Good: Transforming Social Determinants of Health” “AI for Good” and “Data for Good” initiatives […]
The prevalence of diabetes has been steadily increasing for the past 30 years and is growing […]
Government agencies and civil organizations collect, consolidate and publish Social Determinants of Health (SDOH) data. Public […]
Government agencies and civil organizations publish Social Determinants of Health (SDOH) data that can be used […]
In this story we will show you how to use the GitHub extension for JupyterLab. GitHub […]
Collaboration requires good communication. Your team members need to understand work products. And you need to […]
A Docker image is a great way to get started with JupyterHub for your data science […]
Your data science team needs to be able to share ideas and insights. You need to […]