Hi, so i am trying the DIETclassifier model with help from this guide ( https://towardsdatascience.com/how-do-chatbots-understand-87227f9f96a7 ) but i found that it doesnt work well with portuguese, i basically took the guide’s model, changed the language from en to pt and then inserted my own test and train data, but now i have very low accuracy (15-21% MAX) is there anything else that i would need to do to make my classifier models good for portuguese? I also dont think its the training data bc i copy-pasted the guide example data (which in English had close to 100% accuracy) , translated it to pt and only had a max of 21% of accuracy. heres my config file: pipeline: name: WhitespaceTokenizer name: RegexFeaturizer name: LexicalSyntacticFeaturizer name: CountVectorsFeaturizer analyzer: char_wb min_ngram: 1 max_ngram: 4 name: DIETClassifier entity_recognition: false intent_classification: true epochs: 100 name: classifier2.LRIntentClassifier max_iter: 100 name: EntitySynonymMapper name: ResponseSelector epochs: 200 constrain_similarities: true entity_recognition: false name: FallbackClassifier threshold: 0.7 ambiguity_threshold: 0.1

Hi so on my own dataset i have 4 intents and a total of 95 examples (ranging from 17-28 examples based on the intent), all intents are short phrases and none have ~ or ç (specific portuguese characters) and that dataset gives me only a 15% accuracy. Then i translated a small english dataset (provided by the guide) which has 3 intents and 23 examples (4-11 examples on each intent) that smaller dataset gave me a 21% accuracy in PT but close to 100% in EN. The only change besides the dataset i am making for portuguese is in the config files: language: pt I heard i could use a spacy transformers to try to get better PT performance, something like this: pipeline: name: SpacyNLP # Use Spacy as it supports Portuguese. model: “pt_core_news_sm” # Portuguese language model. name: SpacyTokenizer # Spacy tokenizer to use the Spacy model’s tokenization. name: SpacyFeaturizer # Featurizer that uses word vectors from Spacy. name: RegexFeaturizer name: LexicalSyntacticFeaturizer name: CountVectorsFeaturizer or alternatively using pt_core_news_lg , but then that bypasses the guide python code which saves the model and i dont know exactly how that would work, how would i save this model to use later? Since i am quite new to NLP would you mind helping me? maybe we can talk on telegram or discord

Rasa NLU intent recognition models in Portuguese

Rasa Open Source

nonola (Pedro Lopes) July 18, 2023, 6:36pm 2

Rasa is language agnostic. That said, how many examples do you have in each intent?

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Rasa NLU intent recognition models in Portuguese

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