This article was published on HuggingFace on December 3rd, 2024.
Overview:
This article presents a deep dive into Capital Fund Management’s (CFM) use of open-source large language models (LLMs) and the Hugging Face (HF) ecosystem to optimize Named Entity Recognition (NER) for financial data. By leveraging LLM-assisted labeling with HF Inference Endpoints and refining data with Argilla, the team improved accuracy by up to 6.4% and reduced operational costs, achieving solutions up to 80x cheaper than large LLMs alone.
In this post, you will learn:
1. How to use LLMs for efficient data labeling
2. Steps for fine-tuning compact models with LLM insights
3. Deployment of models on Hugging Face Inference Endpoints for scalable NER applications
This structured approach combines accuracy and cost-effectiveness, making it ideal for real-world financial applications.
Read the full article here.