Hello, is it possible to run
rasa train with one big yaml file containg all needed information?
Normally, files must be divided into nlu, rules, stories etc. I want to train in using just one file I’ve prepared.
(using Rasa 2.x)
rasa train --file moodbot.yml
rasa train < moodbot.yml
where moodbot.yaml contains everything
--- intents: - affirm - bot_challenge - deny - goodbye - greet - Mood Great - mood_unhappy actions: - action_core_fallback nlu: - intent: affirm examples: | - yes - y - indeed - of course - that sounds good - correct - intent: bot_challenge examples: | - are you a bot? - are you a human? - am I talking to a bot? - am I talking to a human? - intent: deny examples: | - no - n - never - I don't think so - don't like that - no way - not really - intent: goodbye examples: | - good afternoon - cu - good by - cee you later - good night - bye - goodbye - have a nice day - see you around - bye bye - see you later - intent: greet examples: | - hey - hello - hi - hello there - good morning - good evening - moin - hey there - let's go - hey dude - goodmorning - goodevening - good afternoon - Ahoy - intent: Mood Great examples: | - perfect - great - amazing - feeling like a king - wonderful - I am feeling very good - I am great - I am amazing - I am going to save the world - super stoked - extremely good - so so perfect - so good - so perfect - intent: mood_unhappy examples: | - my day was horrible - I am sad - I don't feel very well - I am disappointed - super sad - I'm so sad - sad - very sad - unhappy - not good - not very good - extremly sad - so saad - so sad version: "2.0" language: en policies: - name: AugmentedMemoizationPolicy max_history: 6 - name: TEDPolicy max_history: 6 epochs: 20 - name: RulePolicy core_fallback_threshold: 0.300000 core_fallback_action_name: action_core_fallback pipeline: - name: WhitespaceTokenizer - name: RegexFeaturizer - name: LexicalSyntacticFeaturizer - name: CountVectorsFeaturizer - name: CountVectorsFeaturizer analyzer: char_wb min_ngram: 1 max_ngram: 4 - name: DIETClassifier epochs: 100 - name: EntitySynonymMapper - name: ResponseSelector epochs: 100 - name: FallbackClassifier threshold: 0.700000 ambiguity_threshold: 0.100000 stories: - story: happy path 337 steps: - intent: greet - action: utter_bebea9fc52645e0488d95a99437a2dc72079c47d - intent: Mood Great - action: utter_a1d20b921fb25d7b69787dbe26049ac4509a591b - story: sad path 339 steps: - intent: greet - intent: greet - action: utter_bebea9fc52645e0488d95a99437a2dc72079c47d - action: utter_bebea9fc52645e0488d95a99437a2dc72079c47d - intent: mood_unhappy - intent: mood_unhappy - action: utter_136ab7dea3e7d179f22b91a3a786483f5b146804 - action: utter_136ab7dea3e7d179f22b91a3a786483f5b146804 - action: utter_f8b77d2047660e61cf709839722b571f3a3840d6 - action: utter_f8b77d2047660e61cf709839722b571f3a3840d6 - intent: affirm - intent: deny - action: utter_a1d20b921fb25d7b69787dbe26049ac4509a591b - action: utter_3ca9d381697c0922763e20804127dd373093ac4b responses: utter_bebea9fc52645e0488d95a99437a2dc72079c47d: - text: Hey! How are you? - text: Hey one more time! utter_a1d20b921fb25d7b69787dbe26049ac4509a591b: - text: Great, carry on! utter_136ab7dea3e7d179f22b91a3a786483f5b146804: - text: 'Here is something to cheer you up:' image: https://i.imgur.com/nGF1K8f.jpg utter_f8b77d2047660e61cf709839722b571f3a3840d6: - text: Did that help you? utter_3ca9d381697c0922763e20804127dd373093ac4b: - text: Bye utter_default: - text: ACTION DEFAULT FALLBACK rules: - rule: Default fallback steps: - intent: nlu_fallback ...