I am working on a project where I am using rasa-nlu to extract entities from the user’s query. I have few doubts regarding Rasa-NLU. What is the memory footprint for rasa-nlu project which is developed using spacy-pipeline? And what rate this memory footprint increases with training data? How many CPU’s and how much RAM is required for rasa-nlu to run smoothly?
# Configuration for Rasa NLU.
# https://rasa.com/docs/rasa/nlu/components/
language: en
pipeline:
- name: SpacyNLP
case_sensitive: True
- name: SpacyTokenizer
intent_tokenization_flag: True
intent_split_symbol: " "
- name: SpacyFeaturizer
- name: RegexFeaturizer
- name: CRFEntityExtractor
- name: EntitySynonymMapper
- name: "regex.RegexEntityExtractor"
- name: SklearnIntentClassifier
This is my config file.
Thank you in advance.