August 23, 2020
Data augmentation with transformer models for named entity recognition
In this article we sample from pre-trained transformers to augment small, labeled text datasets for named entity recognition.
July 7, 2020
How to approach almost any real-world NLP problem
This time, I’m going to talk about how to approach general NLP problems. But we’re not going to look at the standard tips which are tosed around on the internet, for example on platforms like kaggle.
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June 3, 2020
Data validation for NLP applications with topic models
In a recent article, we saw how to implement a basic validation pipeline for text data. Once a machine learning model has been deployed its behavior must be monitored. The predictive performance is expected to degrade over time as the environment changes.
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January 30, 2020
Data validation for NLP machine learning applications
An important part of machine learning applications, is making sure that there is no data degeneration while a model is in production. Sometimes downstream data processing changes and machine learning models are very prone to silent failure due to this.
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January 21, 2020
Find label issues with confident learning for NLP
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.
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November 24, 2019
Cluster discovery in german recipes
If you are dealing with a large collections of documents, you will often find yourself in the situation where you are looking for some structure and understanding what is contained in the documents. Here I’ll show you a convenient method for discovering and understanding clusters of text documents.
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April 14, 2019
Introduction to entity embeddings with neural networks
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.
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February 12, 2019
How to use magnitude with keras
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.
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March 16, 2018
Guide to word vectors with gensim and keras
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.
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January 27, 2018
Detecting Network Attacks with Isolation Forests
In this post, I will show you how to use the isolation forest algorithm to detect attacks to computer networks in python.
The term isolation means separating an instance from the rest of the instances. Since anomalies are ‘few and different’ and therefore they are more susceptible to isolation.
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December 23, 2017
A strong and simple baseline to classify toxic comments on wikipedia with keras
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.
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