PyText: A Seamless Path from NLP research to production

December 12, 2018 ยท Declared Dead ยท ๐Ÿ› arXiv.org

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Authors Ahmed Aly, Kushal Lakhotia, Shicong Zhao, Mrinal Mohit, Barlas Oguz, Abhinav Arora, Sonal Gupta, Christopher Dewan, Stef Nelson-Lindall, Rushin Shah arXiv ID 1812.08729 Category cs.CL: Computation & Language Citations 17 Venue arXiv.org Last Checked 4 months ago
Abstract
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.
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