Image by authorBuilding a transformer model from scratch can often be the only option for many more specific use cases. Although BERT and other transformer models have been pre-trained for many languages and domains, they do not cover everything. Often, these less common use cases stand to gain the most from having someone come along and build a specific transformer model. It could be for an uncom

BERT, but in Italy — image by authorMany of my articles have been focused on BERT — the model that came and dominated the world of natural language processing (NLP) and marked a new age for language models. For those of you that may not have used transformers models (eg what BERT is) before, the process looks a little like this: pip install transformersInitialize a pre-trained transformers model —

Distance Measures. Image by the author.Many algorithms, whether supervised or unsupervised, make use of distance measures. These measures, such as euclidean distance or cosine similarity, can often be found in algorithms such as k-NN, UMAP, HDBSCAN, etc.

Writing is very different today compared to fifteen years ago. To be published used to mean in print, which constrained space but was less hasty. There were more gatekeepers, and far less content competing for readers’ attention. It had only taken a few years, but technology completely overhauled the economics of the written word. I didn’t think the process was over. I like writing, and at the ver

To date, there are a lot of books out there about Natural Language Processing that you could learn from. However, choosing the right book for yourself might be intimidating since there is just so much! This post provides a list of the top books I personally recommend to supplement your NLP learning. I have divided the list into practice and theory books, depending on whether you are more of a prac

(Image by Author)IntroductionNatural language processing (NLP) is an intimidating name for an intimidating field. Generating useful insight from unstructured text is hard, and there are countless techniques and algorithms out there, each with their own use-cases and complexities. As a developer with minimal NLP exposure, it can be difficult to know which methods to use, and how to implement them.

SummaryIn this article, using NLP and Python, I will explain 3 different strategies for text multiclass classification: the old-fashioned Bag-of-Words (with Tf-Idf ), the famous Word Embedding (with Word2Vec), and the cutting edge Language models (with BERT).