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Algorithms

Named Entity Recognition with Bert

Named entity recognition series: Introduction To Named Entity Recognition In Python Named Entity Recognition With Conditional Random Fields In Python Guide To Sequence Tagging With Neural Networks In Python Sequence Tagging With A LSTM-CRF Enhancing LSTMs With Character Embeddings For… Continue Reading →

Understanding text data with topic models

This is the first post of my series about understanding text data sets. In practice, you often want and need to know, what is going on in your data. In this post we will focus on applying a Latent Dirichlet allocation (LDA) topic model to the “Quora Insincere Questions Classification” data set on kaggle.

Evaluate sequence models in python

Named entity recognition series: Introduction To Named Entity Recognition In Python Named Entity Recognition With Conditional Random Fields In Python Guide To Sequence Tagging With Neural Networks In Python Sequence Tagging With A LSTM-CRF Enhancing LSTMs With Character Embeddings For… Continue Reading →

Explain neural networks with keras and eli5

  In this post I’m going to show you how you can use a neural network from keras with the LIME algorithm implemented in the eli5 TextExplainer class. For this we will write a scikit-learn compatible wrapper for a keras… Continue Reading →

Debugging black-box text classifiers with LIME

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 completly hidden from you. A famous example are deep neural nets, in text classification oftern recurrent or… Continue Reading →

Getting started with Multivariate Adaptive Regression Splines

In this post we will introduce multivariate adaptive regression splines model (MARS) using python. This is a regression model that can be seen as a non-parametric extension of the standard linear model.

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