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How does FastText support online learning?

The pull request #1327 (https://github.com/facebookresearch/fastText/pull/1327) Allows for: test after each epoch checkpointing training on large data which does not fit into memory (largest I tested ...
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Going beyond intent classification

Found it! After a lot of searching and reading through different papers, this was the exact one I needed. "BERT for Joint Intent Classification and Slot Filling" There's also an example ...
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2 votes

Large Language Models vs Tabular Data

In Pytorch you can build models which are in part consisting of e.g. a pretrained Bert model and then add some custom or additional layers.
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1 vote
Accepted

Which loss / activation function with 2 classes that do not occur often and do not sum to one?

This is multi-label classification, which means you have two binary classification problems, one for each of your classes. This is different than multi-class classification. For this use binary cross-...
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Higher accuracy in the test set than in the training set

Getting higher accuracy in test set than in training set means that your test set is easier to work on than training set. Your graph shows results for only one epoch. In the 1st epoch, your train ...
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Higher accuracy in the test set than in the training set

Your test set images are more clean than your training set images, because you applied more types of noise to the training set images, so the classifier performs better on the cleaner images in the ...

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