Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

<div><p>Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of parameters, and thus are too resource- hungry and computation-intensive to suit low- capability device...

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Bibliographic Details
Main Author: Prakhar Ganesh (18459042) (author)
Other Authors: Yao Chen (149006) (author), Xin Lou (487486) (author), Mohammad Ali Khan (18459045) (author), Yin Yang (35103) (author), Hassan Sajjad (5297441) (author), Preslav Nakov (17760905) (author), Deming Chen (1477738) (author), Marianne Winslett (18459048) (author)
Published: 2021
Subjects:
Information and computing sciences
Artificial intelligence
Natural Language Processing (NLP)
State-of-the-art performance
Model parameters
Resource-hungry
Computation-intensive
Model compression
Low-capability devices
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