PyText: A Seamless Path from NLP research to production
By Ahmed Aly, Kushal Lakhotia, Shicong Zhao, Mrinal Mohit, Barlas Oğuz, Abhinav Arora, Sonal Gupta, Christopher Dewan, Stef Nelson-Lindall, Rushin Shah. 2018
17 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Information Systems, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Luke S. Zettlemoyer
- Michael Lewis
- Sebastian Riedel
- Mohit Bansal
- Yejin Choi
- Graham Neubig
- Hannaneh Hajishirzi
- Jianfeng Gao
In Information Systems
- Michael Lewis
- Patrick Lewis
- Benjamin Piwowarski
- Veselin Stoyanov
- Jacopo Staiano
- Sylvain Lamprier
- Guokun Lai
- Sonal Gupta
In Natural Language Processing
- Luke S. Zettlemoyer
- Michael Lewis
- Sebastian Riedel
- Mohit Bansal
- Yejin Choi
- Graham Neubig
- Thomas Scialom
- Patrick Lewis
Citations in the corpus
- ColdGANs: Taming Language GANs with Cautious Sampling Strategies (2020, neutral): “All models are implemented in PyText [1].”
- Discriminative Adversarial Search for Abstractive Summarization (2020, methodology): “Implementation details All models are implemented in PyText (Aly et al., 2018).”
- MLQA: Evaluating Cross-lingual Extractive Question Answering (2019, methodology): “Models were trained with the SQuAD-v1 training method from Devlin et al. (2019) and implemented in Pytext (Aly et al., 2018).”
- Bridging the domain gap in cross-lingual document classification (2019, methodology): “A.1 IMPLEMENTATION DETAILS The experiments in this paper are based on the PyTorch (Paszke et al., 2017) and Pytext (Aly et al., 2018) package.”
- Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog (2018, methodology): “Implementation details We implement all classification and sequence labeling models within the PyText framework (Aly et al., 2018).”
- What BERT Sees: Cross-Modal Transfer for Visual Question Generation (2020, unknown): “All models are implemented in PyText (Aly et al., 2018).”