lonePatient/MobileBert_PyTorch
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
This project helps developers integrate a compact, efficient language understanding model into applications that run on devices with limited computing power, like mobile phones or embedded systems. It takes raw text data as input and provides language understanding capabilities, such as sentiment analysis or text classification. This is ideal for developers building NLP-powered features for resource-constrained environments.
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Use this if you are a developer looking to deploy sophisticated natural language processing (NLP) models on devices with restricted memory or processing power.
Not ideal if you are a data scientist working on high-performance server-side NLP tasks where model size is not a primary concern.
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71
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12
Language
Python
License
MIT
Category
Last pushed
May 19, 2020
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