how can I complete the text classification task using less memory

how can I complete the text classification task using less memory

By : ayesh
Date : November 14 2020, 04:51 PM
To fix the issue you can do The main problem you're facing is that you're using far too many features. It's actually quite extraordinary that you've managed to generate 542401 features from documents that contain just 400 words! I've seen SVM classifiers separate spam from non-spam with high accuracy using just 150 features -- word counts of selected words that say a lot about whether the document is spam. These use stemming and other normalization tricks to make the features more effective.
You need to spend some time thinning out your features. Think about which features are most likely to contain information useful for this task. Experiment with different features. As long as you keep throwing everything but the kitchen sink in, you'll get memory errors. Right now you're trying to pass 10000 data points with 542401 dimensions each to your SVM. That's 542401 * 10000 * 4 = 21 gigabytes (conservatively) of data. My computer only has 4 gigabytes of RAM. You've got to pare this way down.1
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C# not releasing memory after task complete

C# not releasing memory after task complete

By : mi777
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further Okay, I've been following this...I think there are a couple issues, some of which people have touched on, but I think not answering the real question (which, admittedly, took me a while to recognize, and I'm not sure I'm answering what you want even now.)
Best scikit classifier for text classification task

Best scikit classifier for text classification task

By : nupur agrawal
Date : March 29 2020, 07:55 AM
I wish did fix the issue. The problem is not with the classifier, it is with the vectorizer. TfidfVectorizer has a parameter token_pattern : string, which is a "Regular expression denoting what constitutes a “token”, only used if tokenize == ‘word’. The default regexp select tokens of 2 or more letters characters (punctuation is completely ignored and always treated as a token separator)." (emphasis added). The tokenizer throws out the word i, resulting in an empty document. Naive Bayes then classifies that as class 1, because this is the most frequent class in the training data.
Depending on the data, you might want to consider using a uniform prior for Naive Bayes.
Text classification scheme for a classification task with 120 classes

Text classification scheme for a classification task with 120 classes

By : Kevin 8Ball Pool
Date : March 29 2020, 07:55 AM
wish helps you I assume that classes are not overlapping (that is, exactly one class per message).
A useful approach in the case of imbalanced classes is using asymetric miss-classification costs in order to enforce the classifier to focus on the less represented class, as its cost is assigned much bigger figure than other classes.
NLP data preparation and sorting for text-classification task

NLP data preparation and sorting for text-classification task

By : Aggy Tank
Date : March 29 2020, 07:55 AM
I wish this help you You can try OneVsAll / OneVsRest strategy. This will allow you to do both: predict exact one category without the need to strictly assign one label.
Which model (GPT2, BERT, XLNet and etc) would you use for a text classification task? Why?

Which model (GPT2, BERT, XLNet and etc) would you use for a text classification task? Why?

By : shineneo1
Date : October 03 2020, 05:00 AM
it helps some times It highly depends on your dataset and is part of the data scientist's job to find which model is more suitable for a particular task in terms of selected performance metric, training cost, model complexity etc.
When you work on the problem you will probably test all of the above models and compare them. Which one of them to choose first? Andrew Ng in "Machine Learning Yearning" suggest starting with simple model so you can quickly iterate and test your idea, data preprocessing pipeline etc.
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