How to improve the confidence score of the intent in Rasa NLU?
How to improve the confidence score of the intent in Rasa NLU?
I was working on Rasa NLU for intent classification, in link how shall I improve the confidence score for a given intent.
I have tried to give more training data but still the confidence score isn't increasing. Can anyone please let me know which parameters hyperparameters I can tune in order to get good confidence score.
I did tried to all possible combinations provided in this link but still there was hardly any improvement.
I did checked the suggestion provided over here, but I am looking for granular tuning of the model such that it can perform better.
Thanks.
Edit 1: Please provide a valid reason for down-voting the question.
Sorry couldn't able to share the training data, Intents is around 44 and training distinct examples are around 1k per intent.
– Aman Dalmia
Sep 3 at 6:16
Sounds way over fit. As in way too many training examples per intent.
– Caleb Keller
Sep 3 at 20:23
2 Answers
2
You can use tensorflow_embedding which gives confidence score near to .9, rather than using spacy_sklearn which provides neat to .3
Depending whether spaCy provides a good language model for your language, you should either use the spaCy pipeline (as it comes with pretrained models) or the tensorflow_embedding pipeline which works with any language but requires more training examples.
tensorflow_embedding
I think that your problems might be caused by overlapping training examples. An example to clarify:
## intent:ask_bot_name
- Tell me your name
- What is your name
- name please
## intent:ask_location_name
- Tell me the name
- What's the name
- name please
So I would suggest to go through your training data and have a look whether different intents have the same or very very similar examples.
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Is your training data proprietary or can you share it? How many intents/training examples per intent? which pipeline? How similar are your intents? Do you have entities? I don't feel like you've provided enough information to get more of an answer than just: provide more training data.
– Caleb Keller
Aug 31 at 19:38