Scaling text classification / reusing models
If I have a system, where I want to train many text classifiers for many users, how could I go about it with the transformers library in a scalable way?
Right now I would have to run let's say a 10min training per user on a RTX 2080 ti for Albert for the dataset I have. That doesn't scale if I have thousands of users.

If I understand correctly, in the sequence classification models, the whole transformer model is being trained, so the backpropagation happens through the whole network.

However, if I now want to reuse the model for another user, maybe just passing in a bit more labeled data to customize a base classifier, how could I go about that?

It seems to me, that I would have to basically "freeze" the whole "Bert" model, no matter which one I would use, and then only train a thin layer on top.

One possibility I see would be KNN using sentence transformers, I already asked in the repo there https://github.com/UKPLab/sentence-transformers/issues/209


Maybe someone here has an idea which approach would make sense for such a situation.

Thanks!
You can pretty easily "freeze" parameters you don't want to backpropagate against, in PyTorch: 

```python
for param in parameters:
    param.requires_grad = False
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