* Fix example code for spacy-wordnet
It looks like in the most recent version, 0.1.0, it's no longer possible
to pass the lang parameter to the component separately. Doing so will
raise an error.
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Cleanup
* More cleanup
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Add experimental coref docs
* Docs cleanup
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Apply changes from code review
* Fix prettier formatting
It seems a period after a number made this think it was a list?
* Update docs on examples for initialize
* Add docs for coref scorers
* Remove 3.4 notes from coref
There won't be a "new" tag until it's in core.
* Add docs for span cleaner
* Fix docs
* Fix docs to match spacy-experimental
These weren't properly updated when the code was moved out of spacy
core.
* More doc fixes
* Formatting
* Update architectures
* Fix links
* Fix another link
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: svlandeg <svlandeg@github.com>
* Add SpanRuler component
Add a `SpanRuler` component similar to `EntityRuler` that saves a list
of matched spans to `Doc.spans[spans_key]`. The matches from the token
and phrase matchers are deduplicated and sorted before assignment but
are not otherwise filtered.
* Update spacy/pipeline/span_ruler.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix cast
* Add self.key property
* Use number of patterns as length
* Remove patterns kwarg from init
* Update spacy/tests/pipeline/test_span_ruler.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Add options for spans filter and setting to ents
* Add `spans_filter` option as a registered function'
* Make `spans_key` optional and if `None`, set to `doc.ents` instead of
`doc.spans[spans_key]`.
* Update and generalize tests
* Add test for setting doc.ents, fix key property type
* Fix typing
* Allow independent doc.spans and doc.ents
* If `spans_key` is set, set `doc.spans` with `spans_filter`.
* If `annotate_ents` is set, set `doc.ents` with `ents_fitler`.
* Use `util.filter_spans` by default as `ents_filter`.
* Use a custom warning if the filter does not work for `doc.ents`.
* Enable use of SpanC.id in Span
* Support id in SpanRuler as Span.id
* Update types
* `id` can only be provided as string (already by `PatternType`
definition)
* Update all uses of Span.id/ent_id in Doc
* Rename Span id kwarg to span_id
* Update types and docs
* Add ents filter to mimic EntityRuler overwrite_ents
* Refactor `ents_filter` to take `entities, spans` args for more
filtering options
* Give registered filters more descriptive names
* Allow registered `filter_spans` filter
(`spacy.first_longest_spans_filter.v1`) to take any number of
`Iterable[Span]` objects as args so it can be used for spans filter
or ents filter
* Implement future entity ruler as span ruler
Implement a compatible `entity_ruler` as `future_entity_ruler` using
`SpanRuler` as the underlying component:
* Add `sort_key` and `sort_reverse` to allow the sorting behavior to be
customized. (Necessary for the same sorting/filtering as in
`EntityRuler`.)
* Implement `overwrite_overlapping_ents_filter` and
`preserve_existing_ents_filter` to support
`EntityRuler.overwrite_ents` settings.
* Add `remove_by_id` to support `EntityRuler.remove` functionality.
* Refactor `entity_ruler` tests to parametrize all tests to test both
`entity_ruler` and `future_entity_ruler`
* Implement `SpanRuler.token_patterns` and `SpanRuler.phrase_patterns`
properties.
Additional changes:
* Move all config settings to top-level attributes to avoid duplicating
settings in the config vs. `span_ruler/cfg`. (Also avoids a lot of
casting.)
* Format
* Fix filter make method name
* Refactor to use same error for removing by label or ID
* Also provide existing spans to spans filter
* Support ids property
* Remove token_patterns and phrase_patterns
* Update docstrings
* Add span ruler docs
* Fix types
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Move sorting into filters
* Check for all tokens in seen tokens in entity ruler filters
* Remove registered sort key
* Set Token.ent_id in a backwards-compatible way in Doc.set_ents
* Remove sort options from API docs
* Update docstrings
* Rename entity ruler filters
* Fix and parameterize scoring
* Add id to Span API docs
* Fix typo in API docs
* Include explicit labeled=True for scorer
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* signing contributor agreement
* adding new content to the spaCy universe
* updating outdated example codes
* resolving issues for the PR
* resolve review for klayers
* remove contributor-agreement file from the PR
* Update code example of spaCySentiWS
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy-sentiws code example
Co-authored-by: schaeran <schaeran1994@gmail.com>
Co-authored-by: schaeran <schaeran@explosion.ai>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* added crosslingual coreference to spacy universe
* Updated example to introduce batching example.
Co-authored-by: David Berenstein <david.berenstein@pandoraintelligence.com>
* Add edit tree lemmatizer
Co-authored-by: Daniël de Kok <me@danieldk.eu>
* Hide edit tree lemmatizer labels
* Use relative imports
* Switch to single quotes in error message
* Type annotation fixes
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Reformat edit_tree_lemmatizer with black
* EditTreeLemmatizer.predict: take Iterable
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Validate edit trees during deserialization
This change also changes the serialized representation. Rather than
mirroring the deep C structure, we use a simple flat union of the match
and substitution node types.
* Move edit_trees to _edit_tree_internals
* Fix invalid edit tree format error message
* edit_tree_lemmatizer: remove outdated TODO comment
* Rename factory name to trainable_lemmatizer
* Ignore type instead of casting truths to List[Union[Ints1d, Floats2d, List[int], List[str]]] for thinc v8.0.14
* Switch to Tagger.v2
* Add documentation for EditTreeLemmatizer
* docs: Fix 3.2 -> 3.3 somewhere
* trainable_lemmatizer documentation fixes
* docs: EditTreeLemmatizer is in edit_tree_lemmatizer.py
Co-authored-by: Daniël de Kok <me@danieldk.eu>
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update universe.json
added classy-classification to Spacy universe
* Update universe.json
added classy-classification to the spacy universe resources
* Update universe.json
corrected a small typo in json
* Update website/meta/universe.json
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update website/meta/universe.json
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update website/meta/universe.json
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update universe.json
processed merge feedback
* Update universe.json
* updated information for Classy Classificaiton
Made a more comprehensible and easy description for Classy Classification based on feedback of Philip Vollet to prepare for sharing.
* added note about examples
* corrected for wrong formatting changes
* Update website/meta/universe.json with small typo correction
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* resolved another typo
* Update website/meta/universe.json
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* added Concise Concepts package to spaCy universe.
* updated example code Concise Concepts
* updated description for Concise Concepts
* updated PR with more visually appealing examples
SO to koaning for the suggestions.
* corrected for small json typo's in concise concepts
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update universe.json
added classy-classification to Spacy universe
* Update universe.json
added classy-classification to the spacy universe resources
* Update universe.json
corrected a small typo in json
* Update website/meta/universe.json
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update website/meta/universe.json
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update website/meta/universe.json
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update universe.json
processed merge feedback
* Update universe.json
* updated information for Classy Classificaiton
Made a more comprehensible and easy description for Classy Classification based on feedback of Philip Vollet to prepare for sharing.
* added note about examples
* corrected for wrong formatting changes
* Update website/meta/universe.json with small typo correction
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* resolved another typo
* Update website/meta/universe.json
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* 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>
* 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>
* 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"
* Update universe plugins
* Adjust azure trigger
* Add init to tests/universe
* deliberatly trying to break the universe to see if the CI catches it
* revert
Co-authored-by: svlandeg <svlandeg@github.com>
