PyText Framework
A PyText Framework is an deep NLP modeling framework.
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- Counter-Example(s):
- See: DeepText.
References
2018b
- https://code.fb.com/ai-research/pytext-open-source-nlp-framework/
- QUOTE: ... To make it easier to build and deploy natural language processing (NLP) systems, we are open-sourcing PyText, a modeling framework that blurs the boundaries between experimentation and large-scale deployment. PyText is a library built on PyTorch, our unified, open source deep learning framework. It offers several benefits for NLP development:
- A simplified workflow for faster experimentation.
- Access to a rich set of prebuilt model architectures and utilities for text processing and vocabulary management to facilitate large-scale deployment.
- The ability to harness the PyTorch ecosystem, including prebuilt models and tools created by researchers and engineers in the NLP community.
- AI researchers and engineers can now use PyText to more quickly and easily experiment with and deploy systems to perform document classification, sequence tagging, semantic parsing, multitask modeling, and other tasks. ...
... PyText builds on and complements Facebook’s other NLP systems. It can be used in conjunction with our fastText library, for example. Researchers and engineers can train word embeddings in fastText and then use them in PyText.
PyText also improves in important ways upon DeepText, which, for example, cannot implement dynamic graphs. Some of the semantic parsing and multitask learning models in PyText cannot be built with DeepText, because of conditional execution and custom data structures in the model. PyText also speeds training, because it can utilize GPUs and more easily implement distributed training. We are planning to use PyText as our main NLP platform going forward.
With PyText, AI researchers and engineers can convert PyTorch models to ONNX and then export them as Caffe2 for production deployment at scale.
- QUOTE: ... To make it easier to build and deploy natural language processing (NLP) systems, we are open-sourcing PyText, a modeling framework that blurs the boundaries between experimentation and large-scale deployment. PyText is a library built on PyTorch, our unified, open source deep learning framework. It offers several benefits for NLP development:
2018a
- (Aly et al., 2018) ⇒ Ahmed Aly, Kushal Lakhotia, Shicong Zhao, Mrinal Mohit, Barlas Oguz, Abhinav Arora, Sonal Gupta, Christopher Dewan, Stef Nelson-Lindall, and Rushin Shah. (2018). “PyText: A Seamless Path from NLP Research to Production.” In: Facebook Research Journal.
- 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.