Intent classification with rasa-nlu #54
Open
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
satyammittal
reviewed
May 1, 2020
| @@ -106,6 +114,7 @@ | |||
| engine.runAndWait() | |||
| intent = intentClassifier.intent_identifier(translate) | |||
| print("Intent:", intent) | |||
| intent_rasa = interpreter.parse(translate) | |||
There was a problem hiding this comment.
I don't see any use of this variable. Can we try replace intent with intent_rasa and check how better it is detecting. I would also like to see accuracy % in pr description.
|
Hello, the rasa model is trained on data in nlu.md. We can customise it as per the needs of the chatbot. I will have to prepare a test dataset and calculate the accuracy. I will do that for both the bayesian model and the rasa toolkit and will update you with the accuracy of both. Thank you! |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
I have added the model, dataset and the other files required for training the model.