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Check out this excellent guide to text classification from Google. Covers important best practices and rules of thumb, including a complete flowchart for picking the right model & preprocessing pipeline based on the statistical structure of your dataset. https://developers.google.com/machine-learning/guides/text-classification/step-2-5 …pic.twitter.com/4nBkb3HjFj
An important insight is that the ratio between number of training samples and mean number of words per sample can tell you whether you should be using a n-gram model or a sequence model -- and whether you should use pre-trained word embeddings or train your own from scratch.
This guide will serve as your free expert consultant for all your text classification problems. That flowchart is a form of AutoML! Of the cheap, effective kind.
Would you have a link to a paper about the « sepCNN » model ? Sounds like a CNN flavor but cannot find it when I Google it (maybe it might have another name? Or is it a typo seq-cnn ? )
Is it explained in the guide. It's a 1D convnet with depthwise separable convolution layers.
Thanks for sharing
More of this, please and thank you.
An other way for text analysishttps://twitter.com/SEBsemdee/status/1018986001077809152?s=19 …
I think it's correct @_aatkinson_. When you calculate W/S the number will be very less, not in the range of thousands as shown in the chart.
Great guide really, we are using this now !
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