Components

For clarity, we have renamed the pre-defined pipelines to reflect what they do rather than which libraries they use as of Rasa NLU 0.15. The tensorflow_embedding pipeline is now called supervised_embeddings, and spacy_sklearn is now known as pretrained_embeddings_spacy. Please update your code if you are using these.

This is a reference of the configuration options for every built-in component in Rasa NLU. If you want to build a custom component, check out Custom NLU Components.

Word Vector Sources

• MitieNLP
• SpacyNLP

Text Featurizers

• MitieFeaturizer
• SpacyFeaturizer
• ConveRTFeaturizer
• RegexFeaturizer
• CountVectorsFeaturizer

Intent Classifiers

• MitieIntentClassifier
• SklearnIntentClassifier
• EmbeddingIntentClassifier
• KeywordIntentClassifier

Selectors

• Response Selector

Tokenizers

• WhitespaceTokenizer
• JiebaTokenizer
• MitieTokenizer
• SpacyTokenizer
• ConveRTTokenizer

Entity Extractors

• MitieEntityExtractor
• SpacyEntityExtractor
• EntitySynonymMapper
• CRFEntityExtractor
• DucklingHTTPExtractor

Configuration for MitieNLP

pipeline:
- name: "MitieNLP"
  # language model to load
  model: "data/total_word_feature_extractor.dat"

Configuration for SpacyNLP

pipeline:
- name: "SpacyNLP"
  # language model to load
  model: "en_core_web_md"
  case_sensitive: false

Configuration for Text Featurizers

Example for CountVectorsFeaturizer:

pipeline:
- name: "CountVectorsFeaturizer"
  use_shared_vocab: False
  analyzer: 'word'
  token_pattern: r'(?u)\b\w\w+\b'
  min_df: 1
  max_df: 1.0
  min_ngram: 1
  max_ngram: 1
  lowercase: true
  OOV_token: None
  OOV_words: []

Example of Output

{
    "entities": [{
        "value": "New York City",
        "start": 20,
        "end": 33,
        "entity": "city",
        "extractor": "MitieEntityExtractor"
    }]
}