v0.6.0 – Partial supervision
This release mainly adds support for training using partially annotated data (materialized in the CoNLL-U files by _
in a column), along with some improvement to tooling and bump of dependencies upper versions.
Added
hopsparser evaluate
now accepts an optional output argument, allowing to write directly to a
file if needed.- A new script to help catch performances regressions on released models.
Changed
- We now accept partially annotated CoNLL-U files as input for training: any learnable cell (UPOS,
HEAD, DEPREL) for which the value is_
will not contribute to the loss.
Full Changelog: v0.5.0...v0.6.0