Handling class imbalance in multilabel classification using ULMFit

Part 1 (2019)
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I am trying to develop a tone classifier that could infer multiple tones from a given email. Most of the labelled examples are “neutral” whereas the other ones are “anger”, “disappointment” etc…

Is there a way to address this type of class imbalance. I have looked up libraries such as imblearn but somehow I couldn’t find a suitable solution.

Could anybody suggest a way?

Thanks

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This thread would give an idea on handling class imbalances in multilabel setting

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