Closed niless closed 4 years ago
Sure, if you want to prevent your feature embedder from updating its weights, set requires_grad
to false before passing it in.
What exactly are you evaluating? Are you saying you want to evaluate a predicted dependency tree just from the post/feats/lemma gold data alone? Seems like you'll need the gold dependency tree if you want a good evaluation, as it's globally context sensitive, while the other annotations don't need as much context.
Hi,
Is it possible to disable fine-tuning by simply changing requires_grad to false in token_embedder for a frozen feature based embedder? Also, is there a preferred approach to just evaluate dependency parsing using gold tags for pos/feats/lemmas?
Thanks.