* Update universe plugins
* Adjust azure trigger
* Add init to tests/universe
* deliberatly trying to break the universe to see if the CI catches it
* revert
Co-authored-by: svlandeg <svlandeg@github.com>
* Update universe plugins
* Adjust azure trigger
* Add init to tests/universe
* deliberatly trying to break the universe to see if the CI catches it
* revert
Co-authored-by: svlandeg <svlandeg@github.com>
* remove text argument from W108 to enable 'once' filtering
* include the option of partial POS annotation
* fix typo
* Update spacy/errors.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add test for case where parser overwrite annotations
* Move test to its own file
Also add note about how other tokens modify results.
* Fix xfail decorator
* Add util function to unique lists and preserve order
* Use unique function instead of list(set())
list(set()) has the issue that it's not consistent between runs of the
Python interpreter, so order can vary.
list(set()) calls were left in a few places where they were behind calls
to sorted(). I think in this case the calls to list() can be removed,
but this commit doesn't do that.
* Use the existing pattern for this
* Fix inconsistency
This makes the failing test pass, so that behavior is consistent whether
patterns are added in one call or two.
The issue is that the hash for patterns depended on the index of the
pattern in the list of current patterns, not the list of total patterns,
so a second call would get identical match ids.
* Add illustrative test case
* Add failing test for remove case
Patterns are not removed from the internal matcher on calls to remove,
which causes spurious weird matches (or misses).
* Fix removal issue
Remove patterns from the internal matcher.
* Check that the single add call also gets no matches
Hyphen is unsuitable because of interactions with the JA data fields,
but pipe is also unsuitable because it has a different meaning in UD
data, so it's better to use something that has no significance in either
case. So this uses semicolon.
* use language-matching to allow language code aliases
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
* link to "IETF language tags" in docs
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
* Make requirements consistent
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
* change "two-letter language ID" to "IETF language tag" in language docs
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
* use langcodes 3.2 and handle language-tag errors better
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
* all unknown language codes are ImportErrors
Signed-off-by: Elia Robyn Speer <elia@explosion.ai>
Co-authored-by: Elia Robyn Speer <elia@explosion.ai>
Since a component may reference anything in the vocab, share the full
vocab when loading source components and vectors (which will include
`strings` as of #8909).
When loading a source component from a config, save and restore the
vocab state after loading source pipelines, in particular to preserve
the original state without vectors, since `[initialize.vectors]
= null` skips rather than resets the vectors.
The vocab references are not synced for components loaded with
`Language.add_pipe(source=)` because the pipelines are already loaded
and not necessarily with the same vocab. A warning could be added in
`Language.create_pipe_from_source` that it may be necessary to save and
reload before training, but it's a rare enough case that this kind of
warning may be too noisy overall.
* Use morph for extra Japanese tokenizer info
Previously Japanese tokenizer info that didn't correspond to Token
fields was put in user data. Since spaCy core should avoid touching user
data, this moves most information to the Token.morph attribute. It also
adds the normalized form, which wasn't exposed before.
The subtokens, which are a list of full tokens, are still added to user
data, except with the default tokenizer granualarity. With the default
tokenizer settings the subtokens are all None, so in this case the user
data is simply not set.
* Update tests
Also adds a new test for norm data.
* Update docs
* Add Japanese morphologizer factory
Set the default to `extend=True` so that the morphologizer does not
clobber the values set by the tokenizer.
* Use the norm_ field for normalized forms
Before this commit, normalized forms were put in the "norm" field in the
morph attributes. I am not sure why I did that instead of using the
token morph, I think I just forgot about it.
* Skip test if sudachipy is not installed
* Fix import
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add link to Discussions FAQ
* Remove old FAQ entries
I think these are no longer relevant.
- no-cache-dir: affected pip versions are *very* old now
- narrow unicode: not an issue from py3.3+
- utf-8 osx: upstream bug closed in 2019
Some of the other issues are also maybe not frequent.
* Add link to Discussions FAQ
* Remove old FAQ entries
I think these are no longer relevant.
- no-cache-dir: affected pip versions are *very* old now
- narrow unicode: not an issue from py3.3+
- utf-8 osx: upstream bug closed in 2019
Some of the other issues are also maybe not frequent.
* Add overwrite settings for more components
For pipeline components where it's relevant and not already implemented,
add an explicit `overwrite` setting that controls whether
`set_annotations` overwrites existing annotation.
For the `morphologizer`, add an additional setting `extend`, which
controls whether the existing features are preserved.
* +overwrite, +extend: overwrite values of existing features, add any new
features
* +overwrite, -extend: overwrite completely, removing any existing
features
* -overwrite, +extend: keep values of existing features, add any new
features
* -overwrite, -extend: do not modify the existing value if set
In all cases an unset value will be set by `set_annotations`.
Preserve current overwrite defaults:
* True: morphologizer, entity linker
* False: tagger, sentencizer, senter
* Add backwards compat overwrite settings
* Put empty line back
Removed by accident in last commit
* Set backwards-compatible defaults in __init__
Because the `TrainablePipe` serialization methods update `cfg`, there's
no straightforward way to detect whether models serialized with a
previous version are missing the overwrite settings.
It would be possible in the sentencizer due to its separate
serialization methods, however to keep the changes parallel, this also
sets the default in `__init__`.
* Remove traces
Co-authored-by: Paul O'Leary McCann <polm@dampfkraft.com>
