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[D] Distilling BERT — How to achieve BERT performance using Logistic Regression

Few days ago, in this post, I asked about a way to make BERT smaller. I got some interesting results and found some relevant papers. The basic idea is, given a relatively small labelled dataset, and another much bigger unlabelled set:

  1. Train BERT on the labeled set
  2. Predict values of the unlabelled set
  3. Train a much smaller model using the now the labelled big set

I tried it with Logistic Regression and got some interesting results here:

https://towardsdatascience.com/distilling-bert-how-to-achieve-bert-performance-using-logistic-regression-69a7fc14249d?source=friends_link&sk=2e62337d0b44f56409640c27277b99ce

submitted by /u/sudo_su_
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.