PyText builds on PyTorch for language recognition

Facebook has open-sourced its PyText project, a machine learning library for natural language processing (NLP) intended to make it easier to put together both experimental projects and production systems.

PyText, built with Facebook’s existing PyTorch library for machine learning and used internally by the company, was created to address how machine learning using neural networks (such as for NLP). Such libraries typically were “a trade-off between frameworks optimized for experimentation and those optimized for production,” they said in a post.

Frameworks built for experimentation allowed fast prototyping, but suffered from “increased latency and memory use in production,” Facebook’s engineers wrote. On the other hand, frameworks built for production worked better under load, but were tougher to develop quickly with.

PyText’s main touted difference is its workflow, which Facebook claims can be optimized for either experiments or production use. The frameworks’ components can be stitched together to create an entire NLP pipeline, or individual pieces can be broken out and reused in other contexts.

Training new models can be distributed across multiple nodes, and multiple models can be trained at the same time. PyText can also use many existing models for text classification, skipping the need for training entirely in those cases.

PyText also improves comprehension via contextual models, a way to enrich the model’s understanding of a text from previous inputs. A chatbot, for example, could reuse information from earlier messages in a discussion to shape its answers.

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