HuggingFace project: Easy implementation from start to finish #NLP

Main Cover. Generating and end-to-end project with Hugging Face, FastAPI and Docker.

The article discusses how to create an end-to-end project using a Hugging Face model for sentiment analysis, FastAPI for creating an API endpoint, and Docker for containerization. The process involves selecting a Hugging Face model, setting up the model pipeline, creating an API endpoint with FastAPI, and containerizing the application with Docker for portability and deployment. The article provides step-by-step instructions on how to achieve this, including code snippets for setting up the model, defining the API endpoint, and creating a Docker image. The end result is a working sentiment classification model that can be accessed via an API and run anywhere using Docker. The author, Josep Ferrer, is an analytics engineer with a background in physics engineering and a focus on data science and technology. The article provides a comprehensive guide for implementing this project and includes a link to the author’s GitHub repository for further exploration.

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