* Fix debug data check for ents that cross sents
* Use aligned sent starts to have the same indices for the NER and sent
start annotation
* Add a temporary, insufficient hack for the case where a
sentence-initial reference token is split into multiple tokens in the
predicted doc, since `Example.get_aligned("SENT_START")` currently
aligns `True` to all the split tokens.
* Improve test example
* Use Example.get_aligned_sent_starts
* Add test for crossing entity
* Auto-format code with black
* add black requirement to dev dependencies and pin to 22.x
* ignore black dependency for comparison with setup.cfg
Co-authored-by: explosion-bot <explosion-bot@users.noreply.github.com>
Co-authored-by: svlandeg <svlandeg@github.com>
So that overriding `paths.vectors` works consistently in generated
configs, set vectors model in `paths.vectors` and always refer to this
path in `initialize.vectors`.
Remove exception for whitespace tokens in `Example.get_aligned` so that
annotation on whitespace tokens is aligned in the same way as for
non-whitespace tokens.
* Added spacy-wrap to universe
Added spacy-wrap to universe a small package for wrapping fine-tuned huggingface transformers to a spacy pipeline following the same API as spacy-transformers. (Currently limited to classification models)
* Update website/meta/universe.json
* Update website/meta/universe.json
* Update website/meta/universe.json
* Update website/meta/universe.json
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Clarify Span.ents documentation
Ref: #10135
Retain current behaviour. Span.ents will only include entities within
said span. You can't get tokens outside of the original span.
* Reword docstrings
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update API docs in the website
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* This comma has been most probably been left out unintentionally, leading to string concatenation between the two consecutive lines. This issue has been found automatically using a regular expression.
* This comma has been most probably been left out unintentionally, leading to string concatenation between the two consecutive lines. This issue has been found automatically using a regular expression.
* Fix infix as prefix in Tokenizer.explain
Update `Tokenizer.explain` to align with the `Tokenizer` algorithm:
* skip infix matches that are prefixes in the current substring
* Update tokenizer pseudocode in docs
* Improve typing hints for Matcher.__call__
* Add typing hints for DependencyMatcher
* Add typing hints to underscore extensions
* Update Doc.tensor type (requires numpy 1.21)
* Fix typing hints for Language.component decorator
* Use generic np.ndarray type in Doc to avoid numpy version update
* Fix mypy errors
* Fix cyclic import caused by Underscore typing hints
* Use Literal type from spacy.compat
* Update matcher.pyi import format
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Instead of the running the actual suggester, which may require
annotation from annotating components that is not necessarily present in
the reference docs, use the built-in 1-gram suggester.
* Support version tags in universe and add note about reporting
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* added iob to int
* added tests
* added iob strings
* added error
* blacked attrs
* Update spacy/tests/lang/test_attrs.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/attrs.pyx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* added iob strings as global
* minor refinement with iob
* removed iob strings from token
* changed to uppercase
* cleaned and went back to master version
* imported iob from attrs
* Update and format errors
* Support and test both str and int ENT_IOB key
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* added new field
* added exception for IOb strings
* minor refinement to schema
* removed field
* fixed typo
* imported numeriacla val
* changed the code bit
* cosmetics
* added test for matcher
* set ents of moc docs
* added invalid pattern
* minor update to documentation
* blacked matcher
* added pattern validation
* add IOB vals to schema
* changed into test
* mypy compat
* cleaned left over
* added compat import
* changed type
* added compat import
* changed literal a bit
* went back to old
* made explicit type
* Update spacy/schemas.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/schemas.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/schemas.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Determine labels by factory name in debug data
For all components, return labels for all components with the
corresponding factory name rather than for only the default name.
For `spancat`, return labels as a dict keyed by `spans_key`.
* Refactor for typing
* Add test
* Use assert instead of cast, removed unneeded arg
* Mark test as slow
* Add link to pattern file info in EntityRuler.initialize docs
* Update website/docs/api/entityruler.md
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Use Vectors.shape rather than Vectors.data.shape
* Use Vectors.size rather than Vectors.data.size
* Add Vectors.to_ops to move data between different ops
* Add documentation for Vector.to_ops
By @polm, redone from #9917 after incorrect (reverted) rebase.
`sudachipy>=0.5.2` is needed for newer dictionaries. `sudachipy<0.6.0`
is kept for users who might still prefer the older version, in
particular to be able to compile it without rust.
* Corrected Span's __richcmp__ implementation to take end, label and kb_id in consideration
* Updated test
* Updated test
* Removed formatting from a test for readability sake
* Use same tuples for all comparisons
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* 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>
This change changes the type of left/right-arc collections from
vector[ArcC] to unordered_map[int, vector[Arc]], so that the arcs are
keyed by the head. This allows us to find all the left/right arcs for a
particular head in constant time in StateC::{L,R}.
Benchmarks with long docs (N is the number of text repetitions):
Before (using #10019):
N Time (s)
400 3.2
800 5.0
1600 9.5
3200 23.2
6400 66.8
12800 220.0
After (this commit):
N Time (s)
400 3.1
800 4.3
1600 6.7
3200 12.0
6400 22.0
12800 42.0
Related to #9858 and #10019.
* 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
* 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>
