Hi everyone !
I’ve been using RASA for a project since several weeks / months and I still have problems with the accuracy of the NLU.
To be quick the project is to provide a Chatbot factory for specific entities which can use a spreadsheet and we convert this spreadsheet in Rasa files (domain.yml, stories.md and nlu.json).
One entity is doing tests with us and we have a major problem. The NLU, despite several modification on the configuration file, recognize exact sentences with few accuracy (like 50%).
Do you have an idea how to improve this ? We only use RASA for intent recognition (no entities).
# Configuration for Rasa NLU. # https://rasa.com/docs/rasa/nlu/components/ language: fr pipeline: - name: WhitespaceTokenizer - name: RegexFeaturizer - name: CountVectorsFeaturizer - name: CountVectorsFeaturizer analyzer: 'char_wb' min_ngram: 1 max_ngram: 4 - name: DIETClassifier entity_recognition: false epochs: 20 # Configuration for Rasa Core. # https://rasa.com/docs/rasa/core/policies/ policies: - name: MemoizationPolicy max_history: 1 - name: TEDPolicy max_history: 1 epochs: 1 - name: FallbackPolicy nlu_threshold: 0.6 core_threshold: 0.5 fallback_action_name: 'utter_phrase_hors_sujet_0'
Some data usefull:
We are currently training the model with more than 800 intents and more than 3000 examples (this number is growing).
After the DIETClassifier training and the 20 epochs the accuracy is around 0.98x (which for me is quite good).
Rasa Version : 1.10