Load the pretrained charlm, adds it as inputs to the POS model #1086
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This improves accuracy on almost all POS models Doing the same thing for depparse would also make sense, but is currently not done. However, the downstream scores of depparse don't seem to be negatively affected by using the different (better) POS tags produced by models using the pretrained charlm Add a pos-specific charlm map for the medical EN datasets and the one dataset which appears to be hurt by the charlm (tr_boun) craft, genia -> None Produces resources.json with pos charlms Make the Pipeline pass in charlm paths if present in resources.json TODO: use the foundation_cache to load them
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Load the pretrained charlm, adds it as inputs to the POS model
This improves accuracy on almost all POS models
Doing the same thing for depparse would also make sense, but is
currently not done. However, the downstream scores of depparse don't
seem to be negatively affected by using the different (better) POS
tags produced by models using the pretrained charlm
Add a pos-specific charlm map for the medical EN datasets and the one dataset which appears to be hurt by the charlm (tr_boun)
craft, genia -> None
Produces resources.json with pos charlms
Make the Pipeline pass in charlm paths if present in resources.json
TODO: use the foundation_cache to load them