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DistillBERT - Step-by-Step Knowledge Distillation
The DistillBERT paper provides an excellent walkthrough of incremental knowledge distillation techniques to compress BERT models. Here are the key steps:
Teacher BERT Model: Start with a pretrained BERT model as ..
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Which Neural Network layer is this code implementing? Bonus points if you can identify the Instruction Set Architecture!
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For teams looking to compress large models for production, we highly recommend reading the DistillBERT paper. It demonstrates creative techniques beyond just model size reduction.
Read more:
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Have you used knowledge distillation in your work? Let us know your experiences in the comments!
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While BERT pioneered the field, DistilBERT demonstrates that high-quality NLP can now be unlocked for everyone without massive computational requirements. Check out the open-source DistilBERT and let us know if it works for your use cases:
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DistilBERT - A More Efficient Alternative to BERT
Since its release, BERT has become the gold standard in language representation models, powering state-of-the-art results across many NLP tasks. However, its massive 340M parameter size makes it challenging .. (contd)
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Have you guys ever used this model?
MobileBERT - Bringing BERT to Mobile Devices
BERT has driven significant progress in NLP, but its hundreds of millions of parameters make on-device deployment impractical. Enter MobileBERT - a compressed variant designed specifically for mobile and embedded applications.
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