Comments (2)
@wassname Nice paper!
Re. serving: yes, the server must know about the relevant adapter parameters. Two possibilities I can think of are 1) Store adapters for various tasks on the server (like a word embedding matrix), each request needs to specify which adapters it wants to use. One needs to own the service in order to do this. 2) Pass the adapter parameters to the server alongside the input text with the request. This would probably only be effective with very small adapters (which can work well for some tasks).
Re. online learning: are you talking about the setting where each training example is seen only once, then discarded? If so, this is an interesting idea, that we have not tried. Indeed, the fact that one can get away with a more aggressive learning rate might help.
from adapter-bert.
Re: online learning: Similar, it might be stored by you want results right away, for example, online learning. Later you could probably do full retraining like suggested in your paper with weighted sampling. I haven't tried it either but will let you know if I do.
Thanks!
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Related Issues (10)
- ValueError: Tensor not found in checkpoint HOT 1
- How a near-identity initialization is implemented HOT 2
- In adapter-fine-tuning, why don't fix original params? HOT 2
- regarding the training speed and data amount requirement HOT 1
- missing processors HOT 1
- Hyperparameters of GLUE datasets
- How to implement adapters in case of pre norm HOT 2
- freezing "layer_norm" and "head" HOT 2
- Adapters on large-datasets in GLUE could not get the same results HOT 1
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from adapter-bert.