Hi everyone. I’m running Rasa NLU in production on the following spec: GCP e2-standard-8 machine - From which RASA is allocated the following: 4 CPU, 26 GB RAM
Config:
pipeline:
- name: SpacyNLP model: “en_core_web_lg” case_sensitive: False
- name: SpacyTokenizer
- name: RegexFeaturizer
- name: LanguageModelFeaturizer model_name: “bert” model_weights: “rasa/LaBSE”
- name: CountVectorsFeaturizer analyzer: “char_wb” min_ngram: 1 max_ngram: 4
- name: DIETClassifier model_confidence: softmax epochs: 100 batch_strategy: balanced constrain_similarities: True
- name: EntitySynonymMapper
- name: SpacyEntityExtractor case_sensitive: False
- name: EntitySynonymMapper
- name: CRFEntityExtractor
- name: “DucklingEntityExtractor”
For each RASA classification (Intent prediction + entity extraction) it takes in average and median ~0.24 second. Is that considered a reasonable running time? Can we speed up the process?
Thank you!