XingLuxi/Cal-FLOPs-for-PLM
Calculating FLOPs of Pre-trained Models in NLP
This tool helps machine learning engineers and researchers quickly understand the computational cost and memory footprint of their Natural Language Processing (NLP) models. You provide your pre-trained NLP model, and it outputs the number of floating-point operations (FLOPs) and parameters required. This allows you to evaluate model efficiency before deployment or large-scale training.
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Use this if you need to compare the efficiency of different NLP models or optimize existing ones for deployment on resource-constrained devices.
Not ideal if you are a business user looking for a no-code solution to optimize your NLP application; this is a developer tool requiring Python and PyTorch knowledge.
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Python
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Last pushed
Mar 29, 2021
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