Does somebody know a good Rasa NLU or Dialogflow converter to SNIPS format?
Maybe with an Graphical User Interface.
There are some that convert from rasa to DF and vice versa but I cant seem to find a converter to SNIPS.
Thank you
You could translate the training files.
If the size of your training set is manageable you may achieve this by:
rewriting it with chatito, that supports both formats,
generating the training set in the Rasa format and check that it's consistent with the original
and finally by generating the training set in the Snips format
Related
I want to train and use an ML based personal voice to text converter for a highly impaired voice, for a small set of 300-400 words. This is to be used for people with voice impairment. But cannot be generic because each person will have a unique voice input for words, depending on their type of impairment.
Wanted to know if there are any ML engines which allow for such a training. If not, what is the best approach to go about it.
Thanks
Most of the speech recognition engines support training (wav2letter, deepspeech, espnet, kaldi, etc), you just need to feed in the data. The only issue is that you need a lot of data to train reliably (1000 of samples for each word). You can check Google Commands dataset for example of how to train from scratch.
Since the training dataset will be pretty small for your case and will consist of just a few samples, you can probably start with existing pretrained model and finetune it on your samples to get best accuracy. You need to look on "few short learning" setups.
You can probably look on wav2vec 2.0 pretrained model, it should be effective for such learning. You can find examples and commands for fine-tuning and inference here.
You can also try fine-tuning Japser models in Google Commands for NVIDIA NEMO. It might be a little less effective but could still work and should be easier to setup.
I highely recommend watching the youtube original series "The age of AI"'s First season, episode two.
Basically, google already done this for people who can't really form normal words with impared voice. It is very interesting and speaks a little bit about how they done and doing that with ML technologies.
enter link description here
Is AWS Sage Maker Auto Pilot suitable for NLP?
We currently have a tensorflow model that does classification on input of a sequence of URLS (
We transform the URLs to Word vec and Char vec to feed it to the model).
Looking at Sage Maker Auto Pilot documentation it says that it works on input in tabular form.
I was wondering if we could use it to for our use case.
No. SageMaker AutoPilot doesn't support deep learning at the moment, only classification and regression problems on tabular data. Technically, I guess you could pass embeddings in CSV format, and pray that XGBoost figures them out, but I seriously doubt that this would deliver meaningful results :)
Amazon Comprehend does support fully managed custom classification models https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html. It may be worth taking a look at it.
I am working on a binary logistic regression data set in python. I want to know if there are any numerical methods to calculate how well the model fits the data.
please don't include graphical methods like plotting etc.
Thanks :)
read through 3.3.2. Classification metrics in sklearn documentation.
http://scikit-learn.org/stable/modules/model_evaluation.html
hope it helps.
I am newbie to WEKA. I have written a program to classify Text using LibSVM and WEKA.
The code is available in BitBucket Repo .
Can somebody tell me whether I am in the right path.
Bet regards
Jaggu
You might want to evaluate your approach to see if your 'on the right path'. Use cross validation to see if the models trained by your approach yield the performance you want.
I am trying to develop a spam detector application using svm classifier.
But I am not able to find any input data. Can anyone please suggest what kind of input data should I take and from where I could find it. I tried google but didnt found the satisfactory answeres
Stanford machine learning course (ml-class.org) has a lab (no. 6) where you build a spam
filter using support vector machines. The dataset is supplied.