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The CrossEncoder training hasn't yet been updated to use a new modern Trainer, that's still on my TODO-list, I'm afraid.
There's currently not a very convenient way to save checkpoints every k steps. The easiest is actually to subclass the CrossEncoder, copy the fit method, and add a line like if training_steps % 1000 == 0: self.save(output_path + str(training_steps)) or something like it.
Apologies. I'll improve CrossEncoder support in the coming months.
Hi,
I can see how to train embedding model with brand new trainer. but i couldn't see same for crossencoder.
can you point me if i am missing ?
Thanks
https://sbert.net/docs/sentence_transformer/training_overview.html
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