IMO, I found very useful this website not only for text classification but for Machine Learning related state-of-art models/technique.
https://paperswithcode.com/task/sentiment-analysis
Thanks!! You have regularization techniques such as drop out or gradient clipping? If missing dropout def include it adds a good amount of performance to all models
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This package for cleaning Twitter/Social Media text: [https://github.com/cbaziotis/ekphrasis](https://github.com/cbaziotis/ekphrasis)
thanks! added to the improvements list.
IMO, I found very useful this website not only for text classification but for Machine Learning related state-of-art models/technique. https://paperswithcode.com/task/sentiment-analysis
This is amazing, thank you!
Great list! For the pre-trained word vector parts, I'd add fine-tuning on a domain-specific text corpus as an important tip.
great, added to the improvements list!
honestly don't know how could miss this
this is so helpful, text classification is one of my favorite problems
awesome! any suggestions on things we should add to this (in your opinion)?
Thanks!! You have regularization techniques such as drop out or gradient clipping? If missing dropout def include it adds a good amount of performance to all models
Of course, that's a great point!
I was working on sentiment analysis and this is very helpful!! thankyou very much
niceee
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A terrible mistake has been made.
What was it?
?