Configuration¶

You can provide options to Rasa NLU through:

a yaml-formatted config file
environment variables
command line arguments

Environment variables override options in your config file, and command line args will override any options specified elsewhere. Environment variables are capitalised and prefixed with RASA_, so the option pipeline is specified with the RASA_PIPELINE env var.

Default¶

Here is an example model configuration:

language: "en"

pipeline:
- name: "nlp_spacy"
  model: "en"
- name: "ner_spacy"
- name: "ner_ngrams"
  max_number_of_ngrams: 7
- name: "ner_duckling_http"
  url: "http://my_url"
  dimensions:
  - "NUMBER"
- name: "ner_crf"
  BILOU_flag: true
  features:
    # features for word before token
    - ["low", "title", "upper", "pos", "pos2"]
    # features of token itself
    - ["bias", "low", "word3", "word2", "upper", "title", "digit", "pos", "pos2", "pattern"]
    # features for word after the token we want to tag
    - ["low", "title", "upper", "pos", "pos2"]
  max_iterations: 50
  L1_c: 1
  L2_c: 1e-3
- name: "intent_classifier_sklearn"
  C: [1, 2, 5, 10, 20, 100]
  kernel: "linear"

As you can see, there are a couple of top-level configuration keys, like language and pipeline - but most of the configuration is component specific.

Explanations for the configuration keys of the different components are part of the Processing Pipeline.

Options¶

A short explanation and examples for each configuration value.

pipeline¶

Type:

str or [dict]

Examples:

using a pipeline template (predefined set of components with default parameters):

pipeline: "spacy_sklearn"

or alternatively specifying the components and paremters:

pipeline:
- name: "nlp_spacy"
  model: "en"               # parameter of the spacy component
- name: "ner_synonyms"

Description:

The pipeline used for training. Can either be a template (passing a string) or a list of components (array) and there configuration values. For all available templates, see Processing Pipeline. The component specific parameters are listed there as well.

language¶

Type:	`str`
Examples:	language: "en"
Description:	Language the model is trained in. Underlying word vectors will be loaded by using this language. There is more info about available languages in Language Support.