Update arguments to MultiHashEmbed layer so that the attributes can be
controlled. A kind of tricky scheme is used to allow optional
specification of the rows. I think it's an okay balance between
flexibility and convenience.
* add informative warning when messing up store_user_data DocBin flags
* add informative warning when messing up store_user_data DocBin flags
* cleanup test
* rename to patterns_path
* Refactor Token morph setting
* Remove `Token.morph_`
* Add `Token.set_morph()`
* `0` resets `token.c.morph` to unset
* Any other values are passed to `Morphology.add`
* Add token.morph setter to set from MorphAnalysis
* Support data augmentation in Corpus
* Note initial docs for data augmentation
* Add augmenter to quickstart
* Fix flake8
* Format
* Fix test
* Update spacy/tests/training/test_training.py
* Improve data augmentation arguments
* Update templates
* Move randomization out into caller
* Refactor
* Update spacy/training/augment.py
* Update spacy/tests/training/test_training.py
* Fix augment
* Fix test
* Allow `pkuseg_model` to be set to `None` on initialization
* Don't save config within tokenizer
* Force convert pkuseg_model to use pickle protocol 4 by reencoding with
`pickle5` on serialization
* Update pkuseg serialization test
* Add MORPH handling to Matcher
* Add `MORPH` to `Matcher` schema
* Rename `_SetMemberPredicate` to `_SetPredicate`
* Add `ISSUBSET` and `ISSUPERSET` operators to `_SetPredicate`
* Add special handling for normalization and conversion of morph
values into sets
* For other attrs, `ISSUBSET` acts like `IN` and `ISSUPERSET` only
matches for 0 or 1 values
* Update test
* Rename to IS_SUBSET and IS_SUPERSET
* Add option to disable Matcher errors
* Add option to disable Matcher errors when a doc doesn't contain a
particular type of annotation
Minor additional change:
* Update `AttributeRuler.load_from_morph_rules` to allow direct `MORPH`
values
* Rename suppress_errors to allow_missing
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Refactor annotation checks in Matcher and PhraseMatcher
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* NEL: read sentences and ents from reference
* fiddling with sent_start annotations
* add KB serialization test
* KB write additional file with strings.json
* score_links function to calculate NEL P/R/F
* formatting
* documentation
Similar to how vectors are handled, move the vocab lookups to be loaded
at the start of training rather than when the vocab is initialized,
since the vocab doesn't have access to the full config when it's
created.
The option moves from `nlp.load_vocab_data` to `training.lookups`.
Typically these tables will come from `spacy-lookups-data`, but any
`Lookups` object can be provided.
The loading from `spacy-lookups-data` is now strict, so configs for each
language should specify the exact tables required. This also makes it
easier to control whether the larger clusters and probs tables are
included.
To load `lexeme_norm` from `spacy-lookups-data`:
```
[training.lookups]
@misc = "spacy.LoadLookupsData.v1"
lang = ${nlp.lang}
tables = ["lexeme_norm"]
```
In order to make it easier to construct `Doc` objects as training data,
modify how missing and blocked entity tokens are set to prioritize
setting `O` and missing entity tokens for training purposes over setting
blocked entity tokens.
* `Doc.ents` setter sets tokens outside entity spans to `O` regardless
of the current state of each token
* For `Doc.ents`, setting a span with a missing label sets the `ent_iob`
to missing instead of blocked
* `Doc.block_ents(spans)` marks spans as hard `O` for use with the
`EntityRecognizer`
