-
Notifications
You must be signed in to change notification settings - Fork 4.6k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
How to speed up mitie training process #260
Comments
Thanks for creating this issue, this does sound concerning. Could you please try a benchmark using only MITIE's python bindings? See https://github.com/mit-nlp/MITIE/blob/master/examples/python/train_text_categorizer.py for an example. That will let us isolate if rasa is preventing a speedup or if it's on MITIE's end. |
Good suggestion! I will have a try and then let you know. Thanks! |
Finally I got the results calling directly from mitie train_text_categorizer.py for the intents recognition. I suspect there were issues with my data annotations as I notice there are something in the data like "play x" --> labelled intent: music_play which may have confused the trainer. |
Awesome! I think it's worth testing with a much smaller dataset so we can speed up the debug loop. |
I am testing with a much smaller dataset and will let you know and send you the test files after it finishes. |
I've done following tests. Test1: RASA: Training time: 49904 seconds. == 13.86 hours Test2: RASA: Training time: 4554 seconds. = 75.9 minutes Test3: RASA: Training time: 2225 seconds. = 37.08 minutes Confused thing is we can say that RASA is lower than DirectMitieCall in Test1 and Test3, Which test data would you like to have a try? All these test data? Cheers! |
We have added a new entity recognizer (CRF) which has a similar performance as the MITIE model but trains quite a bit faster. Since, we can not do anything about MITIEs performance (this should be raised on the MITIE project), I'll close this issue. |
Please read detailed explanation about MITIE working. That may solve the long duration training problem. |
* Fixed llm logging for custom components
Hi, I am using mitie backend to train my models for intents and entities. the intents and entities recognition accuracy is better than other backends from my tries. One problem for me is that mitie takes a long time.
I am wondering if anyone has any suggestions on making use of GPUs in RASA?
I tried num_threads:16 on the stable version (0.7.1) on my 24 Cores desktop, it is the same as in one thread. Does RASA support multiple threads for mitie? or Do I need to run on the latest version?
Thanks!
Xingkun
The text was updated successfully, but these errors were encountered: