Distil BertForSequence Classification

Generating counterfactual explanations got a lot easier with CFNOW, but what are counterfactual explanations, and how can I use them? This article is based on the following article: https://www.sciencedirect.com/science/article/abs/pii/S0377221723006598 And here is the address for the CFNOW repository: https://github.com/rmazzine/CFNOW If you are reading this, you may know how pivotal Artificial Intelligence (AI) is becoming in our world today.

Overview The DistilBERT model was proposed in the blog post Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT, and the paper DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base.

Source: Canva Introduction In 2018, GoogleAI researchers released the BERT model. It was a fantastic work that brought a revolution in theowever, the BERT model did have some drawbacks i.e. it was bulky and hence a little slow. To navigate these issues, researchers from Hugging Face proposed DistilBERT, which employed knowledge distillation for model compression. In this article, we will look at this work in more detail.