## Named entity recognition with Bert

In 2018 we saw the rise of pretraining and finetuning in natural language processing. Large neural networks have been trained on general tasks like language modeling and then fine-tuned for classification tasks. One of the latest milestones in this development is the release of BERT.

## Understanding text data with topic models

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.

## LSTM with attention for relation classification

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.

## Evaluate sequence models in python

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:

## Image segmentation with test time augmentation with keras

In the last post, I introduced the U-Net model for segmenting salt depots in seismic images. This time, we will see how to improve the model by data augmentation and especially test time augmentation (TTA). You will learn how to use data augmentation with segmentation masks and what test time augmentation is and how to use it in keras.

## U-Net for segmenting seismic images with keras

Today I’m going to write about a kaggle competition I started working on recently. In the TGS Salt Identification Challenge, you are asked to segment salt deposits beneath the Earth’s surface. So we are given a set of seismic images that are $101 \times 101$ pixels each and each pixel is classified as either salt or sediment.

## State-of-the-art named entity recognition with residual LSTM and ELMo

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.

## 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 completely hidden from you. A famous example are deep neural nets, in text classification often recurrent or convolutional neural nets.

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

## PyData Amsterdam 2018

Last weekend I participated at the PyData Amsterdam 2018 Conference in, you guess it, in Amsterdam. It has been a great conference and I meet a lot of great people and had a very good time in Amsterdam.

## Enhancing LSTMs with character embeddings for Named entity recognition

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.