NLP

NLP focuses largely on converting text to structured data. Common tasks include parsing, speech recognition, part-of-speech tagging, and information extraction.

Custom SpaCy 3.0 models in Rasa

In this guide, we're going to bootstrap a spaCy 3.0 project and show you how you can integrate it with Rasa. We will train an entity detector and demonstrate how to use the new spaCy projects feature.…

Vincent Warmerdam

Why Rasa uses Sparse Layers in Transformers

By Johannes Mosig and Vladimir Vlasov. Feed forward neural network layers are typically fully connected, or dense. But do we actually need to connect every input…

Johannes E. M. Mosig

Exploring Semantic Map Embeddings / Part II

In this second part of our series on semantic maps, we show how to create them and see how they perform as featurizers for DIET.…

Johannes E. M. Mosig

Exploring Semantic Map Embeddings / Part I

We explore a new sparse text embedding that has some interesting properties.…

Johannes E. M. Mosig

Custom Gensim Embeddings in Rasa

Training your own word embeddings can be a hassle, and we typically advise against it. It can require a lot of computing power, time, and a…

Vincent Warmerdam

Visualise Word-Embeddings with Whatlies

We're happy to announce that we're open sourcing a visualisation tool!…

Vincent Warmerdam

Rasa NLU in Depth: Part 3 – Hyperparameter Tuning

Part 3 of our Rasa NLU in Depth series covers hyperparameter tuning. We will explain how to use Docker containers to run a Rasa NLU hyperparameter search for the best NLU pipeline at scale.…

Tobias Wochinger

Rasa NLU in Depth: Part 1 – Intent Classification

This is part one of our three-part in-depth series about Rasa NLU. It will cover intent classification components, when to choose which and how to solve common real-world problems.…

Tobias Wochinger