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Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

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arxiv 1902.08295 v1 pith:VRTBOOEW submitted 2019-02-21 cs.LG stat.ML

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

classification cs.LG stat.ML
keywords frameworklingvomodelsmodularofferingresearchsequence-to-sequenceadvanced
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, and experiment configurations are centralized and highly customizable. Distributed training and quantized inference are supported directly within the framework, and it contains existing implementations of a large number of utilities, helper functions, and the newest research ideas. Lingvo has been used in collaboration by dozens of researchers in more than 20 papers over the last two years. This document outlines the underlying design of Lingvo and serves as an introduction to the various pieces of the framework, while also offering examples of advanced features that showcase the capabilities of the framework.

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Cited by 3 Pith papers

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    LaMDA shows that fine-tuning on human-value annotations and consulting external knowledge sources significantly improves safety and factual grounding in large dialog models beyond what scaling alone achieves.

  3. GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

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    GShard supplies automatic sharding and conditional computation support that enabled training a 600-billion-parameter multilingual translation model on thousands of TPUs with superior quality.