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 bidirectional LSTM model. The wrapper will also handle the tokenization and the storage of the vocabulary.

This is the fifth post in my series about named entity recognition. If you haven’t seen the last four, have a look now. The last time we used a CRF-LSTM to model the sequence structure of our sentences. Read more

Word vectors Today, I tell you what word vectors are, how you create them in python and finally how you can use them with neural networks in keras. For a long time, NLP methods use a vectorspace model to represent words. Read more

This time we’re going to discuss a current machine learning completion on kaggle. In this competition, you’re challenged to build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate. Read more

This is the fourth post in my series about named entity recognition. If you haven’t seen the last three, have a look now. The last time we used a recurrent neural network to model the sequence structure of our sentences. Read more

This is the third post in my series about named entity recognition. If you haven’t seen the last two, have a look now. The last time we used a conditional random field to model the sequence structure of our sentences. Read more

This is the second post in my series about named entity recognition. If you haven’t seen the first one, have a look now. Last time we started by memorizing entities for words and then used a simple classification model to improve the results a bit. Read more

In this post, I will introduce you to something called Named Entity Recognition (NER). NER is a part of natural language processing (NLP) and information retrieval (IR). The task in NER is to find the entity-type of words. Read more