Feeding Custom/Pretrained embeddings for ner_crf

Rasa Open Source
9

You just need to replace the pipeline: "pretrained_embeddings_spacy" with individual components. You can pick and choose, but if you want mostly spacy based components, you could do:

pipeline:
  - name: 'SpacyNLP'
    model: 'your_model_name_here'
  - name: 'SpacyTokenizer'
  - name: 'SpacyFeaturizer'
    ner_feature_vectors: true      # this is the part that's new functionality
  - name: 'CRFEntityExtractor'
  - name: 'EmbeddingIntentClassifier'

This would use spacy to tokenize, would create features for intents using the .vector attribute on the Doc, and would pass the .vector attribute on each token to the CRFEntityExtractor as (some of) the features to do custom entity extraction.