Filtering by orth and tag, create variants of training docs with
alternate orth variants, e.g., unicode quotes, dashes, and ellipses.
The variants can be single tokens (dashes) or paired tokens (quotes)
with left and right versions.
Currently restricted to only add variants to training documents without
raw text provided, where only gold.words needs to be modified.
* Prevent subtok label if not learning tokens
The parser introduces the subtok label to mark tokens that should be
merged during post-processing. Previously this happened even if we did
not have the --learn-tokens flag set. This patch passes the config
through to the parser, to prevent the problem.
* Make merge_subtokens a parser post-process if learn_subtokens
* Fix train script
* Add test for 3830: subtok problem
* Fix handlign of non-subtok in parser training
* allow phrasematcher to link one match to multiple original patterns
* small fix for defining ent_id in the matcher (anti-ghost prevention)
* cleanup
* formatting
* Improve load_language_data helper
* WIP: Add Lookups implementation
* Start moving lemma data over to JSON
* WIP: move data over for more languages
* Convert more languages
* Fix lemmatizer fixtures in tests
* Finish conversion
* Auto-format JSON files
* Fix test for now
* Make sure tables are stored on instance
Check for relevant components in the pipeline when Matcher is called,
similar to the checks for PhraseMatcher in #4105.
* keep track of attributes seen in patterns
* when Matcher is called on a Doc, check for is_tagged for LEMMA, TAG,
POS and for is_parsed for DEP
* Fix typo in rule-based matching docs
* Improve token pattern checking without validation
Add more detailed token pattern checks without full JSON pattern validation and
provide more detailed error messages.
Addresses #4070 (also related: #4063, #4100).
* Check whether top-level attributes in patterns and attr for PhraseMatcher are
in token pattern schema
* Check whether attribute value types are supported in general (as opposed to
per attribute with full validation)
* Report various internal error types (OverflowError, AttributeError, KeyError)
as ValueError with standard error messages
* Check for tagger/parser in PhraseMatcher pipeline for attributes TAG, POS,
LEMMA, and DEP
* Add error messages with relevant details on how to use validate=True or nlp()
instead of nlp.make_doc()
* Support attr=TEXT for PhraseMatcher
* Add NORM to schema
* Expand tests for pattern validation, Matcher, PhraseMatcher, and EntityRuler
* Remove unnecessary .keys()
* Rephrase error messages
* Add another type check to Matcher
Add another type check to Matcher for more understandable error messages
in some rare cases.
* Support phrase_matcher_attr=TEXT for EntityRuler
* Don't use spacy.errors in examples and bin scripts
* Fix error code
* Auto-format
Also try get Azure pipelines to finally start a build :(
* Update errors.py
Co-authored-by: Ines Montani <ines@ines.io>
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
