In the previous posts, we saw how to build strong and versatile named entity recognition systems and how to properly evaluate them. But often you want to understand your model beyond the metrics. So in this tutorial I will show you how you can build an explainable and interpretable NER system with keras and the LIME algorithm. Read more

Since a lot of people recently asked me how neural networks learn the embeddings for categorical variables, for example words, I’m going to write about it today. You all might have heard about methods like word2vec for creating dense vector representation of words in an unsupervised way. Read more

You might have heard, that neural language models power a lot of the recent advances in natural language processing. Namely large models like Bert and GPT-2. But there is a fairly old approach to language modeling that is quite successful in a way. Read more

Today we want to build a model, that can identify ingredients in cooking recipes. I use the “German Recipes Dataset”, I recently published on kaggle. We have more than 12000 German recipes and their ingredients list. Read more

This time we have a look into the magnitude library, a feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. Read more

This is the second post of my series about understanding text datasets. If you read my blog regularly, you probably noticed quite some posts about named entity recognition. In this posts, we focused on finding the named entities and explored different techniques to do this. Read more

This is the first post of my series about understanding text datasets. A lot of the current NLP progress is made in predictive performance. But in practice, you often want and need to know, what is going on in your dataset. Read more

Once named entities have been identified in a text, we then want to extract the relations that exist between them. As indicated earlier, we will typically be looking for relations between specified types of named entity. Read more

An important part of every machine learning project is the proper evaluation of the performance of the system. In this post we will talk about evaluation of token-based sequence models. This is especially tricky because: Read more

This is the sixth post in my series about named entity recognition. This time I’m going to show you some cutting edge stuff. We will use a residual LSTM network together with ELMo embeddings, developed at Allen NLP. You will learn how to wrap a tensorflow hub pre-trained model to work with keras. The resulting model with give you state-of-the-art performance on the named entity recognition task.