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.
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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.
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Good day, I am a student that is interested in NLP. I have come across the demo on AllenNLP's homepage, which stated that:
The model is a simple LSTM using GloVe embeddings that is trained on the binary classification setting of the Stanford Sentiment Treebank. It achieves about 87% accuracy on the test set.
Is there any reference to the sample code or any tutorial that I can follow to replicate this result, so that I can learn more about this subject? I am trying to obtain a Regression Output (Instead of classification).
I hope that someone can point me in the right direction.. Any help is much appreciated. Thank you!
AllenAI provides all code for examples and lib opensource on Git, including AllenNLP.
I found exactly how the example was run here: https://github.com/allenai/allennlp/blob/master/allennlp/tests/data/dataset_readers/stanford_sentiment_tree_bank_test.py
However, to make it a Regression task, you'll have to tweak directly on Pytorch, which is the underlying technology for AllenNLP.
I have a project where I need to analyze a text to extract some information if the user who post this text need help in something or not, I tried to use sentiment analysis but it didn't work as expected, my idea was to get the negative post and extract the main words in the post and suggest to him some articles about that subject, if there is another way that can help me please post it below and thanks.
for the dataset i useed, it was a dataset for sentiment analyze, but now I found that it's not working and I need a dataset use for this subject.
Please use the NLP methods before processing the sentiment analysis. Use the TFIDF, Word2Vector to create vectors on the given dataset. And them try the sentiment analysis. You may also need glove vector for the conducting analysis.
For this topic I found that this field in machine learning is called "Natural Language Questions" it's a field where machine learning models trained to detect questions in text and suggesting answer for them based on data set you are working with, check this article for more detail.
I am doing a License Plate Recognition system using python. I browsed through the net and I found many people have done the recognition of characters in the license plate using kNN algorithm.
Can anyone explain how we predict the characters in the License Plate using kNN ?
Is there any other algorithm or method that can do the prediction better ?
I am referring to this Git repo https://github.com/MicrocontrollersAndMore/OpenCV_3_License_Plate_Recognition_Python
Well, I did this 5 years ago. I will suggest you that, maybe right now is so much better to do this using ML Classifier Models, but if you want to use OpenCV. OpenCV has a pretty cool way to make ANPR using an OCR.
When I did it, I used a RasberryPi for processing and capture images and with c++ run openCV in another computer. I recommend you check this repo and if you're interested look for the book reference there. I hope my answer helps you to find your solution.
https://github.com/MasteringOpenCV/code.
I am looking for ways to train my GATE. Not just running the application, but training it with like data that already annotated (not just plain document). I really appreciate if anybody help me. Thanks :)