Hello everyone,

I recently trained a tiny bidirectional LSTM model to achieve high accuracy on Stanford’s SST-2 by using knowledge distillation and data augmentation. The accuracy is comparable to BERT after fine-tuning, but the model is small enough to run at hundreds of iterations per second on a laptop CPU core. I believe this approach could be very useful since most user-devices in the world are low-power.

I believe this can also give some insight into the success of huggingface’s DistilBERT, as it seems their success doesn’t stem solely from knowledge distillation but also from the Transformer’s unique architecture and the clever way they initialize its weights.

If you have any questions or insights, please share 🙂

For more details please take a look at the article:

https://blog.floydhub.com/knowledge-distillation/