As discussed with Ines in https://github.com/explosion/spaCy/issues/3568 , adding a new project proposal for the community in SpaCy Universe website
GracyQL a tiny graphql wrapper aroung spacy using graphene and starlette.
## Description
Change only in universe.json file to add a new project
### Types of change
New project reference in Universe
## Checklist
- [x ] I have submitted the spaCy Contributor Agreement.
- [x ] I ran the tests, and all new and existing tests passed.
- [ x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* fix(util): fix decaying function output
* fix(util): better test and adhere to code standards
* fix(util): correct variable name, pytestify test, update website text
* Fix code for bag-of-words feature extraction
The _ml.py module had a redundant copy of a function to extract unigram
bag-of-words features, except one had a bug that set values to 0.
Another function allowed extraction of bigram features. Replace all three
with a new function that supports arbitrary ngram sizes and also allows
control of which attribute is used (e.g. ORTH, LOWER, etc).
* Support 'bow' architecture for TextCategorizer
This allows efficient ngram bag-of-words models, which are better when
the classifier needs to run quickly, especially when the texts are long.
Pass architecture="bow" to use it. The extra arguments ngram_size and
attr are also available, e.g. ngram_size=2 means unigram and bigram
features will be extracted.
* Fix size limits in train_textcat example
* Explain architectures better in docs
Add and document CLI options for batch size, max doc length, min doc length for `spacy pretrain`.
Also improve CLI output.
Closes#3216
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Add component_cfg kwarg to begin_training
* Document component_cfg arg to begin_training
* Update docs and auto-format
* Support component_cfg across Language
* Format
* Update docs and docstrings [ci skip]
* Fix begin_training
* Make serialization methods consistent
exclude keyword argument instead of random named keyword arguments and deprecation handling
* Update docs and add section on serialization fields
* Use default return instead of else
* Add Doc.is_nered to indicate if entities have been set
* Add properties in Doc.to_json if they were set, not if they're available
This way, if a processed Doc exports "pos": None, it means that the tag was explicitly unset. If it exports "ents": [], it means that entity annotations are available but that this document doesn't contain any entities. Before, this would have been unclear and problematic for training.
<!--- Provide a general summary of your changes in the title. -->
## Description
* tidy up and adjust Cython code to code style
* improve docstrings and make calling `help()` nicer
* add URLs to new docs pages to docstrings wherever possible, mostly to user-facing objects
* fix various typos and inconsistencies in docs
### Types of change
enhancement, docs
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Improve handling of missing NER tags
GoldParse can accept missing NER tags, if entities is provided
in BILUO format (rather than as spans). Missing tags can be provided
as None values.
Fix bug that occurred when first tag was a None value. Closes#2603.
* Document specification of missing NER tags.
<!--- Provide a general summary of your changes in the title. -->
## Description
This PR adds the abilility to override custom extension attributes during merging. This will only work for attributes that are writable, i.e. attributes registered with a default value like `default=False` or attribute that have both a getter *and* a setter implemented.
```python
Token.set_extension('is_musician', default=False)
doc = nlp("I like David Bowie.")
with doc.retokenize() as retokenizer:
attrs = {"LEMMA": "David Bowie", "_": {"is_musician": True}}
retokenizer.merge(doc[2:4], attrs=attrs)
assert doc[2].text == "David Bowie"
assert doc[2].lemma_ == "David Bowie"
assert doc[2]._.is_musician
```
### Types of change
enhancement
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
