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Rasa NLU Examples

This repository contains some example components meant for educational and inspirational purposes. These are components that we open source to encourage experimentation but these are components that are not officially supported. There will be some tests and some documentation but this is a community project, not something that is part of core Rasa.

The goal of these tools will be to be compatible with the most recent version of rasa only. You may need to point to an older release of the project if you want it to be compatible with an older version of Rasa.

The following components are implemented.


Tokenizers can split up the input text into tokens. Depending on the Tokenizer that you pick you can also choose to apply lemmatization. For languages that have rich grammatical features this might help reduce the size of all the possible tokens.


rasa_nlu_examples.tokenizers.StanzaTokenizer docs

We support a tokenizier based on Stanza. This tokenizer offers part of speech tagging as well as lemmatization for many languages that spaCy currently does not support. These features might help your ML pipelines in those situations.


rasa_nlu_examples.tokenizers.ThaiTokenizer docs

We support a Thai tokenizier based on PyThaiNLP link.

Dense Featurizers

Dense featurizers attach dense numeric features per token as well as to the entire utterance. These features are picked up by intent classifiers and entity detectors later in the pipeline.


rasa_nlu_examples.featurizers.dense.FastTextFeaturizer docs

These are the pretrained embeddings from FastText, see for more info here. These are available in 157 languages, see here.


rasa_nlu_examples.featurizers.dense.BytePairFeaturizer docs

These BytePair embeddings are specialized subword embeddings that are built to be lightweight. See this link for more information. These are available in 227 languages and you can specify the subword vocabulary size as well as the dimensionality.


rasa_nlu_examples.featurizers.dense.GensimFeaturizer docs

A benefit of the gensim library is that it is very easy to train your own word embeddings. It's typically only about 5 lines of code. That means that you could train your own word-embeddings and then easily use them in a Rasa pipeline. This can be useful if you have specific jargon you'd like to capture.

Another benefit of the tool is that it has made it easy for community members to train custom embeddings for many languages. Here's a list of resources;

  • AraVec has embeddings for Arabic trained on twitter and/or Wikipedia.

Fallback Classifiers

Fallback classifiers are models that can override previous intents. In Rasa NLU there is a NLU Fallback Classifier that can "fallback" whenever the main classifier isn't confident about their prediction. In this repository we also host a few of these models such that you can handle specific instances with a custom model too. These models are meant to be used in combination with a RulePolicy.


rasa_nlu_examples.fallback.FasttextLanguageFallbackClassifier docs

This fallback classifier is based on fasttext. It can detect when a user is speaking in an unintended language such that you can create a rule to respond appropriately.


The components listed here won't effect the NLU pipeline but they might instead cause extra logs to appear to help with debugging.


rasa_nlu_examples.meta.Printer docs

This component will print what each featurizer adds to the NLU message. Very useful for debugging. You can find a tutorial on it here.

Entity Extraction

Name Lists

Language models in spaCy are typically trained on Western news datasets. That means that the reported benchmarks might not apply to your use-case. For example; detecting names in texts from France is not the same thing as detecting names in Madagascar. Even thought French is used actively in both countries, the names of it's citizens might be so different that you cannot assume that the benchmarks apply universally.

To remedy this we've started collecting name lists. These can be used as a lookup table which can be picked up by Rasa's RegexEntityExtractor. It won't be 100% perfect but it should give a reasonable starting point.

You can find the namelists here. We currently offer namelists for the United States, Germany as well as common Arabic names. Feel free to submit PRs for more languages. We're also eager to receive feedback.


You can find the contribution guide here.