The shapley value is a popular and useful method to explain machine learning models. The shapley value of a feature is the average contribution of a feature value to the prediction. In this article I’ll show you how to compute shapley values from scratch. Read more

In this article, we look at SimCSE, a simple contrastive sentence embedding framework, which can be used to produce superior sentence embeddings, from either unlabeled or labeled data. The idea behind the unsupervised SimCSE is to simply predicts the input sentence itself, with only dropout used as noise. Read more

Today, I’m going to talk about topic models in NLP. Specifically we will see how the Latent Dirichlet Allocation model works and we will implement it from scratch in numpy. What is a topic model? Assume we are given a large collections of documents. Read more

In every machine learning project, the training data is the most valuable part of your system. In many real-world machine learning projects the largest gains in performance come from improving training data quality. Training data is often hard to aquire and since the data can be large, quality can be hard to check. Read more

Often in text classification, we use so called black-box classifiers. By black-box classifiers I mean a classification system where the internal workings are completely hidden from you. A famous example are deep neural nets, in text classification often recurrent or convolutional neural nets. Read more