![]() Make sure not to edit the format or the metadata when you download and upload the file.Įdit your chit-chat questions and answers Download the personality you want, then upload it as a file source. There is a link to all the chit-chat datasets in the appropriate. Select your KB, and navigate to the Settings page. If you do not want to add chit-chat, or if you already have chit-chat support in your data sources, choose None. Choose the personality that you want as your chit-chat base. You can see all the personality datasets along with details of the personalities.įor the user query of When is your birthday?, each personality has a styled response: PersonalityĬhit-chat data sets are supported in the following languages: Languageĭuring knowledge base creation, after adding your source URLs and files, there is an option for adding chit-chat. Some examples of the different personalities are below. Given a user query, QnA Maker tries to match it with the closest known chit-chat QnA. Choose the persona that most closely resembles your bot's voice. This dataset has about 100 scenarios of chit-chat in the voice of multiple personas, like Professional, Friendly and Witty. For information on migrating existing QnA Maker knowledge bases to question answering, consult the migration guide. Starting 1st October, 2022 you won’t be able to create new QnA Maker resources. For question answering capabilities within the Language Service, see question answering. A newer version of the question and answering capability is now available as part of Azure Cognitive Service for Language. I can’t guarantee that we’ll reach a solution but I’m sure we’ll learn something about your assistant and what’s really happening in the model.The QnA Maker service is being retired on the 31st of March, 2025. The objective is to try out small changes in the source code and see what works best in your case. As a first step, are you familiar with how to install Rasa from source and work with experimental branches of Rasa Open Source? This is the recommended way to install from source. I would like to go deeper into the latter problems with your assistant. ![]() ![]() I would park that problem for now because in production you wouldn’t have such small amount of data anyways.įor a large project, as I mentioned if an example is being classified with low confidence with linear_norm, it means that multiple intents are competing for the correct class and that could be happening very much because of wrong annotations / overlapping intent classes / similar examples across different intents. ![]() ![]() I hope you could give me some insights on that.įor a small project, this is slightly expected because with small amount of data, the model isn’t able to learn properly what’s legible and what’s gibberish. I think that this is a problem, especially when we create Q/A assistants. I tried to test that on another projects with more training data but I get always the same results. The problem is that this ‘ab’ token doesn’t exist in the training data and on the other hand the min/max char ngram = 4. When I execute the “rasa shell nlu” command, I found that the confidence of random inputs is too high, like the following example: I found that with the new version 2.3.4 model_confidence " This should ease up tuning fallback thresholds as confidences for wrong predictions are better distributed across the range "Īlso, I tried to vary the model_confidence parameter (softmax, linear_norm, even cosine in <=2.3.3). This problem occurs even in the previous versions. I have two retrieval intents (faq and chitchat), when I provide a random input, Rasa NLU classifies the input using those retrieval intents when logically it’s a nlu_fallback intent. ![]()
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