Fine-Tune a Job Title Embedding Model using Synthetic Training Data

Sentence embedding models have a wide variety of use cases in the Natural Language Processing (NLP) landscape, such as to facilitate vector search of unstructured text for semantically similar entries, or within a larger model to enable tasks such as regression or classification on free-text.

During this project, you will devise a prompt to generate a synthetic dataset to fine-tune a pretrained sentence embedding model to address deficiencies in the domain-specific use-case of embedding job titles. You will then use this dataset to fine-tune your pretrained model and evaluate the improvement in performance for the vector search task. Finally, you will deploy your fine-tuned model within a Streamlit web app, allowing you to easily show your model to anyone.

This project will help build your familiarity with basic prompting techniques, querying LLMs and parsing their outputs through an API, as well as popular Machine Learning and NLP frameworks (such as Hugging Face and Sentence-Transformers) and provide a compact end-to end experience from initial prototyping to dataset gathering, to training and evaluation, and finally deployment, covering the core parts of common workflows for a Data Scientist or ML Engineer.