PyData Global 2025

Insee, France's National Institute of Statistics and Economic Studies, has long relied on fastText for automatic coding tasks. Recognizing the need to modernize and future-proof this critical functionality, we developed torchTextClassifiers — an open-source Python package that enables easy training and deployment of a PyTorch-based model for text classification, paving the way for further innovation in this domain.

This session will delve into the motivations behind replacing the archived fastText package, the design and implementation of torchTextClassifiers , and its integration into Insee's production environment. We'll discuss the challenges faced during this transition, including model compatibility, performance optimization, and user adoption.​

Attendees will gain insights into:​

• The rationale for moving from fastText to a PyTorch-based model​ in production

• Packaging a PyTorch-based model architecture and open-source collaboration

• Key features and architecture of torchTextClassifiers ​

• Deployment strategies within a public administration (MLOps, cloud native tools, security)

• Lessons learned and best practices for similar transitions​

This talk is intended for data scientists, machine learning engineers, and practitioners interested in NLP, model deployment, and open-source tool development.