Feeding Custom/Pretrained embeddings for ner_crf

Rasa Open Source
8

@jamesmf @souvikg10 - I read your tutorial too

Just to check, this is how we did things:

  1. Downloaded the language I desire from here - Word vectors for 157 languages · fastText
  2. Started this code and saved the model - https://github.com/souvikg10/spacy-fasttext/blob/master/load_fastText.py
  3. Loaded the model with rasa, made our domain,stories, etc. files for that language and trained on it
  4. Started chatting and used config like this:
language: "br"
pipeline: "pretrained_embeddings_spacy"
policies:
- epochs: 45
 max_history: 10
 name: KerasPolicy
- max_history: 10
 name: AugmentedMemoizationPolicy
- name: "FallbackPolicy"
 nlu_threshold: 0.2
 core_threshold: 0.1
 fallback_action_name: "action_default_fallback"

This worked and we started to chat with the bot but I know we do not have any entity extraction so it is pretty lame, can you maybe help me with what should I do next, should I wait for your solution to come to master branch or are there things I need to do beforehand?

Thanks.