* add entry for Applied Language Technology under "Courses"
Added the following entry into `universe.json`:
```
{
"type": "education",
"id": "applt-course",
"title": "Applied Language Technology",
"slogan": "NLP for newcomers using spaCy and Stanza",
"description": "These learning materials provide an introduction to applied language technology for audiences who are unfamiliar with language technology and programming. The learning materials assume no previous knowledge of the Python programming language.",
"url": "https://applied-language-technology.readthedocs.io/",
"image": "https://www.mv.helsinki.fi/home/thiippal/images/applt-preview.jpg",
"thumb": "https://applied-language-technology.readthedocs.io/en/latest/_static/logo.png",
"author": "Tuomo Hiippala",
"author_links": {
"twitter": "tuomo_h",
"github": "thiippal",
"website": "https://www.mv.helsinki.fi/home/thiippal/"
},
"category": ["courses"]
},
```
* Update the entry for "Applied Language Technology"
* Edited Slovenian stop words list (#9707)
* Noun chunks for Italian (#9662)
* added it vocab
* copied portuguese
* added possessive determiner
* added conjed Nps
* added nmoded Nps
* test misc
* more examples
* fixed typo
* fixed parenth
* fixed comma
* comma fix
* added syntax iters
* fix some index problems
* fixed index
* corrected heads for test case
* fixed tets case
* fixed determiner gender
* cleaned left over
* added example with apostophe
* French NP review (#9667)
* adapted from pt
* added basic tests
* added fr vocab
* fixed noun chunks
* more examples
* typo fix
* changed naming
* changed the naming
* typo fix
* Add Japanese kana characters to default exceptions (fix#9693) (#9742)
This includes the main kana, or phonetic characters, used in Japanese.
There are some supplemental kana blocks in Unicode outside the BMP that
could also be included, but because their actual use is rare I omitted
them for now, but maybe they should be added. The omitted blocks are:
- Kana Supplement
- Kana Extended (A and B)
- Small Kana Extension
* Remove NER words from stop words in Norwegian (#9820)
Default stop words in Norwegian bokmål (nb) in Spacy contain important entities, e.g. France, Germany, Russia, Sweden and USA, police district, important units of time, e.g. months and days of the week, and organisations.
Nobody expects their presence among the default stop words. There is a danger of users complying with the general recommendation of filtering out stop words, while being unaware of filtering out important entities from their data.
See explanation in https://github.com/explosion/spaCy/issues/3052#issuecomment-986756711 and comment https://github.com/explosion/spaCy/issues/3052#issuecomment-986951831
* Bump sudachipy version
* Update sudachipy versions
* Bump versions
Bumping to the most recent dictionary just to keep thing current.
Bumping sudachipy to 5.2 because older versions don't support recent
dictionaries.
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Richard Hudson <richard@explosion.ai>
Co-authored-by: Duygu Altinok <duygu@explosion.ai>
Co-authored-by: Haakon Meland Eriksen <haakon.eriksen@far.no>
* Speed up the StateC::L feature function
This function gets the n-th most-recent left-arc with a particular head.
Before this change, StateC::L would construct a vector of all left-arcs
with the given head and then pick the n-th most recent from that vector.
Since the number of left-arcs strongly correlates with the doc length
and the feature is constructed for every transition, this can make
transition-parsing quadratic.
With this change StateC::L:
- Searches left-arcs backwards.
- Stops early when the n-th matching transition is found.
- Does not construct a vector (reducing memory pressure).
This change doesn't avoid the linear search when the transition that is
queried does not occur in the left-arcs. Regardless, performance is
improved quite a bit with very long docs:
Before:
N Time
400 3.3
800 5.4
1600 11.6
3200 30.7
After:
N Time
400 3.2
800 5.0
1600 9.5
3200 23.2
We can probably do better with more tailored data structures, but I
first wanted to make a low-impact PR.
Found while investigating #9858.
* StateC::L: simplify loop
* Check for assets with size of 0 bytes
* Update spacy/cli/project/assets.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* added ruler coe
* added error for none existing pattern
* changed error to warning
* changed error to warning
* added basic tests
* fixed place
* added test files
* went back to error
* went back to pattern error
* minor change to docs
* changed style
* changed doc
* changed error slightly
* added remove to phrasem api
* error key already existed
* phrase matcher match code to api
* blacked tests
* moved comments before expr
* corrected error no
* Update website/docs/api/entityruler.md
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update website/docs/api/entityruler.md
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Added sents property to Span class that returns a generator of sentences the Span belongs to
* Added description to Span.sents property
* Update test_span to clarify the difference between span.sent and span.sents
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update spacy/tests/doc/test_span.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix documentation typos in spacy/tokens/span.pyx
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update Span.sents doc string in spacy/tokens/span.pyx
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Parametrized test_span_spans
* Corrected Span.sents to check for span-level hook first. Also, made Span.sent respect doc-level sents hook if no span-level hook is provided
* Corrected Span ocumentation copy/paste issue
* Put back accidentally deleted lines
* Fixed formatting in span.pyx
* Moved check for SENT_START annotation after user hooks in Span.sents
* add version where the property was introduced
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Migrate regressions 1-1000
* Move serialize test to correct file
* Remove tests that won't work in v3
* Migrate regressions 1000-1500
Removed regression test 1250 because v3 doesn't support the old LEX
scheme anymore.
* Add missing imports in serializer tests
* Migrate tests 1500-2000
* Migrate regressions from 2000-2500
* Migrate regressions from 2501-3000
* Migrate regressions from 3000-3501
* Migrate regressions from 3501-4000
* Migrate regressions from 4001-4500
* Migrate regressions from 4501-5000
* Migrate regressions from 5001-5501
* Migrate regressions from 5501 to 7000
* Migrate regressions from 7001 to 8000
* Migrate remaining regression tests
* Fixing missing imports
* Update docs with new system [ci skip]
* Update CONTRIBUTING.md
- Fix formatting
- Update wording
* Remove lemmatizer tests in el lang
* Move a few tests into the general tokenizer
* Separate Doc and DocBin tests
* morphologizer: avoid recreating label tuple for each token
The `labels` property converts the dictionary key set to a tuple. This
property was used for every annotated token, recreating the tuple over
and over again.
Construct the tuple once in the set_annotations function and reuse it.
On a Finnish pipeline that I was experimenting with, this results in a
speedup of ~15% (~13000 -> ~15000 WPS).
* tagger: avoid recreating label tuple for each token
* Add support for kb_id to be displayed via displacy.serve. The current support is only limited to the manual option in displacy.render
* Commit to check pre-commit hooks are run.
* Update spacy/displacy/__init__.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Changes as per suggestions on the PR.
* Update website/docs/api/top-level.md
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update website/docs/api/top-level.md
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* tag option as new from 3.2.1 onwards
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
* Use internal names for factories
If a component factory is registered like `@French.factory(...)` instead
of `@Language.factory(...)`, the name in the factories registry will be
prefixed with the language code. However in the nlp.config object the
factory will be listed without the language code. The `add_pipe` code
has fallback logic to handle this, but packaging code and the registry
itself don't.
This change makes it so that the factory name in nlp.config is the
language-specific form. It's not clear if this will break anything else,
but it does seem to fix the inconsistency and resolve the specific user
issue that brought this to our attention.
* Change approach to use fallback in package lookup
This adds fallback logic to the package lookup, so it doesn't have to
touch the way the config is built. It seems to fix the tests too.
* Remove unecessary line
* Add test
Thsi also adds an assert that seems to have been forgotten.
* Added Slovak
* Added Slovenian tests
* Added Estonian tests
* Added Croatian tests
* Added Latvian tests
* Added Icelandic tests
* Added Afrikaans tests
* Added language-independent tests
* Added Kannada tests
* Tidied up
* Added Albanian tests
* Formatted with black
* Added failing tests for anomalies
* Update spacy/tests/lang/af/test_text.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Added context to failing Estonian tokenizer test
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Added context to failing Croatian tokenizer test
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Added context to failing Icelandic tokenizer test
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Added context to failing Latvian tokenizer test
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Added context to failing Slovak tokenizer test
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Added context to failing Slovenian tokenizer test
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Added ENT_ID and ENT_KB_ID into the list of the attributes that Matcher matches on
* Added ENT_ID and ENT_KB_ID to TEST_PATTERNS in test_pattern_validation.py. Disabled tests that I added before
* Update website/docs/api/matcher.md
* Format
* Remove skipped tests
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* added error string
* added serialization test
* added more to if statements
* wrote file to tempdir
* added tempdir
* changed parameter a bit
* Update spacy/tests/pipeline/test_entity_ruler.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
If the predicted docs are missing annotation according to
`has_annotation`, treat the docs as having no predictions rather than
raising errors when the annotation is missing.
The motivation for this is a combined tokenization+sents scorer for a
component where the sents annotation is optional. To provide a single
scorer in the component factory, it needs to be possible for the scorer
to continue despite missing sents annotation in the case where the
component is not annotating sents.
* Add note on batch contract
Using listeners requires batches to be consistent. This is obvious if
you understand how the listener works, but it wasn't clearly stated in
the Docs, and was subtle enough that the EntityLinker missed it.
There is probably a clearer way to explain what the actual requirement
is, but I figure this is a good start.
* Rewrite to clarify role of caching
