> For the complete documentation index, see [llms.txt](https://lauradang.gitbook.io/notes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lauradang.gitbook.io/notes/machine-learning/rasa/text-featurizers.md).

# Text Featurizers

* Transforms tokenized text into something machine can read
* 2 Categories: Sparse, Dense
  * Sparse: return feature vectors with a lot of missing values (eg. 0s)
  * Dense: Contains mostly non-zeroes

## MitieFeaturizer

* Requires: MitieNLP
* Dense featurizer
* only SklearnIntentClassifier can use this
* For pre-training your own word vectors (need a huge corpus initially)

## SpacyFeaturizer

* Requires: SpacyNLP

## ConveRT Featurizer

* Requires: ConveRTTokenizer
* Dense featurizer
* Short training time
* Do not fine-tune parameters
* Need intent AND RESPONSE features
  * eg. EmbeddingIntentClassifier
  * eg. ResponseSelector

## RegexFeaturizer

* No requirements
* Only supports CRFEntityExtractor

## CountVectorsFeaturizer

* No requirements
* Sparse featurizer
* bag-of-words representations (intent and response)
* To fine-tune: [sklearn.feature\_extraction](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)