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