Warning: This document is for an old version of rasa NLU.

# Installation¶

rasa NLU itself doesn’t have any external requirements, but to do something useful with it you need to install & configure a backend.

## Setting up rasa NLU¶

The recommended way to install rasa NLU is using pip:

pip install rasa_nlu


If you want to use the bleeding edge version use github + setup.py:

git clone git@github.com:golastmile/rasa_nlu.git
cd rasa_nlu
python setup.py install


## Setting up a backend¶

### Option 1 : MITIE¶

The MITIE backend is all-inclusive, in the sense that it provides both the NLP and the ML parts.

pip install git+https://github.com/mit-nlp/MITIE.git


and then download the MITIE models . The file you need is total_word_feature_extractor.dat. Save this somewhere and in your config.json add 'mitie_file' : '/path/to/total_word_feature_extractor.dat'.

### Option 2 : spaCy + sklearn¶

You can also run using these two in combination.

installing spacy just requires:

pip install -U spacy


If you haven’t used numpy/scipy before, it is highly recommended that you use conda. steps are

• install anaconda
• conda install scikit-learn

otherwise if you know what you’re doing, you can also just pip install -U scikit-learn

### Option 3 : sklearn + MITIE¶

There is a third backend that combines the advantages of the two previous ones:

1. the fast and good intent classification from sklearn and
2. the good entitiy recognition and feature vector creation from MITIE

Especially, if you have a larger number of intents (more than 10), training intent classifiers with MITIE can take very long.

To use this backend you need to follow the instructions for installing both, sklearn and MITIE.