12/26/2023 0 Comments Conversational ai with rasaWe can pass it to an intent classification model. Once we've generated features for all of the tokens and for the entire sentence, This has changed, but it's good to be aware of the original design choice. With the introduction of Rasa's DIET algorithm Historically, the token features were used to extract entities while the sentenceįeatures were used to detect the intent. This is sometimes also referred to as the CLS features or the sentence features. Rasa applies these featurizers to all of the tokens but it also generates features for the entire sentence. If you want these to work, you should also include an appropriate tokenizer in your pipeline. Commonly from spacy via SpaCyFeaturizers or from huggingface via LanguageModelFeaturizers. Dense Features: these consist of many pre-trained embeddings from language models.We also have a LexicalSyntacticFeaturizer that generates window-based features useful for entity recognition. Sparse Features: usually generated by a CountVectorizer.The diagram below shows how the word "Hi" might be encoded. Once we have tokens, we can start adding numeric machine learning features. Which is why you'll usually have a tokenizer listed first at the start of a pipeline. This must happen before text is featurized for machine learning, The first step in a Rasa pipeline is to split an utterance into smaller chunks of text, knownĪs tokens. Let's discuss what each of these types of components do. There are different types of components that you can expect to find in a Rasa pipeline. Of intent prediction and entity extraction.The policies part takes care of The config.yml file consists of two parts.
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