The intent of this was that it would be a component pipeline that used
entities as input, but that's now covered by the get_mentions function
as a pipeline arg.
This is closer to the traditional evaluation method. That uses an
average of three scores, this is just using the bcubed metric for now
(nothing special about bcubed, just picked one).
The scoring implementation comes from the coval project. It relies on
scipy, which is one issue, and is rather involved, which is another.
Besides being comparable with traditional evaluations, this scoring is
relatively fast.
When sentences are not available, just treat the whole doc as one
sentence. A reasonable general fallback, but important due to the init
call, where upstream components aren't run.
* Adapt tokenization methods from `pyvi` to preserve text encoding and
whitespace
* Add serialization support similar to Chinese and Japanese
Note: as for Chinese and Japanese, some settings are duplicated in
`config.cfg` and `tokenizer/cfg`.
This includes the coref code that was being tested separately, modified
to work in spaCy. It hasn't been tested yet and presumably still needs
fixes.
In particular, the evaluation code is currently omitted. It's unclear at
the moment whether we want to use a complex scorer similar to the
official one, or a simpler scorer using more modern evaluation methods.
* Handle partial entities in Span.as_doc
In `Span.as_doc` replace partial entities at the beginning or end of the
span with missing entity annotation.
Fixes a bug where invalid entity annotation (no initial `B`) was
returned for an initial partial entity.
* Check for empty span in ents conversion
Note: `Span.as_doc()` will still fail on an empty span due to failures
in `Span.vector`.
* Preserve existing ENT_KB_ID annotation in NER
Preserve `ent_kb_id` annotation on existing entity spans, which is not
preserved by the transition system.
* Simplify kb_id assignment
* Simplify further
This came up in #7878, but if --resume-path is a directory then loading
the weights will fail. On Linux this will give a straightforward error
message, but on Windows it gives "Permission Denied", which is
confusing.
* Fix percent unk display
This was showing (ratio %), so 10% would show as 0.10%. Fix by
multiplying ration by 100.
Might want to add a warning if this is over a threshold.
* Only show whole-integer percents
* Add training option to set annotations on update
Add a `[training]` option called `set_annotations_on_update` to specify
a list of components for which the predicted annotations should be set
on `example.predicted` immediately after that component has been
updated. The predicted annotations can be accessed by later components
in the pipeline during the processing of the batch in the same `update`
call.
* Rename to annotates / annotating_components
* Add test for `annotating_components` when training from config
* Add documentation
* Add empty lines at the end of Python files
* Only prepend the lang code if it's not there already
* Update spacy/cli/package.py
* fix whitespace stripping
* Set up CI for tests with GPU agent
* Update tests for enabled GPU
* Fix steps filename
* Add parallel build jobs as a setting
* Fix test requirements
* Fix install test requirements condition
* Fix pipeline models test
* Reset current ops in prefer/require testing
* Fix more tests
* Remove separate test_models test
* Fix regression 5551
* fix StaticVectors for GPU use
* fix vocab tests
* Fix regression test 5082
* Move azure steps to .github and reenable default pool jobs
* Consolidate/rename azure steps
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
* Add callback to copy vocab/tokenizer from model
Add callback `spacy.copy_from_base_model.v1` to copy the tokenizer
settings and/or vocab (including vectors) from a base model.
* Move spacy.copy_from_base_model.v1 to spacy.training.callbacks
* Add documentation
* Modify to specify model as tokenizer and vocab params
* Update sent_starts in Example.from_dict
Update `sent_starts` for `Example.from_dict` so that `Optional[bool]`
values have the same meaning as for `Token.is_sent_start`.
Use `Optional[bool]` as the type for sent start values in the docs.
* Use helper function for conversion to ternary ints
* Fix tokenizer cache flushing
Fix/simplify tokenizer init detection in order to fix cache flushing
when properties are modified.
* Remove init reloading logic
* Remove logic disabling `_reload_special_cases` on init
* Setting `rules` last in `__init__` (as before) means that setting
other properties doesn't reload any special cases
* Reset `rules` first in `from_bytes` so that setting other properties
during deserialization doesn't reload any special cases
unnecessarily
* Reset all properties in `Tokenizer.from_bytes` to allow any settings
to be `None`
* Also reset special matcher when special cache is flushed
* Remove duplicate special case validation
* Add test for special cases flushing
* Extend test for tokenizer deserialization of None values
* Replace negative rows with 0 in StaticVectors
Replace negative row indices with 0-vectors in `StaticVectors`.
* Increase versions related to StaticVectors
* Increase versions of all architctures and layers related to
`StaticVectors`
* Improve efficiency of 0-vector operations
Parallel `spacy-legacy` PR: https://github.com/explosion/spacy-legacy/pull/5
* Update config defaults to new versions
* Update docs
