Deepankar Sharma

Generating State-of-the-Art Embeddings with GTE and BGE Models

In this project, I developed a FastAPI-based application to generate high-quality embeddings using the state-of-the-art GTE (Global Text Embeddings) and BGE (Binary Global Embeddings) models available from the Hugging Face Transformers library. The application allows users to input text data, and it returns the corresponding embeddings generated by these models. The FastAPI application is accessible through API endpoints, making it easy to integrate with other applications.
The project involves utilizing models such as `thenlper/gte-base`, `thenlper/gte-large`, `BAAI/bge-large-en`, and `efederici/e5-large-v2-4096`. These models are known for their ability to generate contextual embeddings that capture semantic information in text data. The application supports various endpoints for generating embeddings with different model variants, providing flexibility to users.
The FastAPI app utilizes the `uvicorn` server for deployment, making it accessible via API requests. The input text is processed and sent through the chosen model, generating embeddings that represent the semantic content of the text. The embeddings are then returned to the user in JSON format.
This project demonstrates the practical application of advanced language models for generating meaningful embeddings that can be used in various downstream tasks such as text classification, clustering, and more. The utilization of Hugging Face's Transformers library and FastAPI framework ensures efficiency, scalability, and ease of integration for embedding generation.
As a result, this project showcases my proficiency in using advanced language models, API development, and integrating external libraries to provide a valuable tool for generating state-of-the-art embeddings from text data. The project's modular architecture and clean code contribute to its usability and maintainability.