We introduce PyText - a deep learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces for model components, and by using PyTorch's capabilities of exporting models for inference via the optimized Caffe2 execution engine. We report our own experience of migrating experimentation and production workflows to PyText, which enabled us to iterate faster on novel modeling ideas and then seamlessly ship them at industrial scale.
@article{arxiv.1812.08729,
title = {PyText: A Seamless Path from NLP research to production},
author = {Ahmed Aly and Kushal Lakhotia and Shicong Zhao and Mrinal Mohit and Barlas Oguz and Abhinav Arora and Sonal Gupta and Christopher Dewan and Stef Nelson-Lindall and Rushin Shah},
journal= {arXiv preprint arXiv:1812.08729},
year = {2018}
}