{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pytext-a-seamless-path-from-nlp-research-to","title":"PyText: A Seamless Path from NLP research to production","arxiv_id":"1812.08729","date":"2018-12-12","proceeding":null,"authors":["Ahmed Aly","Kushal Lakhotia","Shicong Zhao","Mrinal Mohit","Barlas Oguz","Abhinav Arora","Sonal Gupta","Christopher Dewan","Stef Nelson-Lindall","Rushin Shah"],"abstract":"We introduce PyText - a deep learning based NLP modeling framework built on\nPyTorch. PyText addresses the often-conflicting requirements of enabling rapid\nexperimentation and of serving models at scale. It achieves this by providing\nsimple and extensible interfaces for model components, and by using PyTorch's\ncapabilities of exporting models for inference via the optimized Caffe2\nexecution engine. We report our own experience of migrating experimentation and\nproduction workflows to PyText, which enabled us to iterate faster on novel\nmodeling ideas and then seamlessly ship them at industrial scale.","url_abs":"http://arxiv.org/abs/1812.08729v1","url_pdf":"http://arxiv.org/pdf/1812.08729v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pytext-a-seamless-path-from-nlp-research-to","repo_url":"https://github.com/facebookresearch/pytext","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"pytext-a-seamless-path-from-nlp-research-to","repo_url":"https://github.com/AMinerOpen/pytext_clf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1812.08729","atlas_url":"https://app.syntology.ai/?focus=1812.08729","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}