In this tutorial we’ll look at the topic of classifying text with BERT, but where we also have additional numerical or categorical features that we want to use to improve our predictions.
In this post, we’ll create a very simple question answering system that, given a natural language question, returns the most likely answers from a corpus of documents.
In this post I wanted to share some of the main themes from NLP over the past year, as well as a few interesting highlights from NeurIPS, the largest annual machine learning conference.
Up to this point, our tutorials have focused almost exclusively on NLP applications using the English language. While the general algorithms and ideas extend to all languages, the huge number of resources that support English language NLP do not extend to all languages. For example, BERT and BERT-like models are an incredibly powerful tool, but model releases are almost always in English, perhaps followed by Chinese, Russian, or Western European language variants.
In this blog post / Notebook, I’ll demonstrate how to dramatically increase BERT’s training time by creating batches of samples with different sequence lengths.