* Pass through "silent" kwarg to the wrapper in the spacy module init.
reference issue #2196
* Pass through "silent" kwarg to the wrapper in the spacy module init.
reference issue #2196
* contributor agreement
* issue_2385 add tests for iob_to_biluo converter function
* issue_2385 fix and modify iob_to_biluo function to accept either iob or biluo tags in cli.converter
* issue_2385 add test to fix b char bug
* add contributor agreement
* fill contributor agreement
## Description
Fix for issue #2361 :
replace &, <, >, " with &amp; , &lt; , &gt; , &quot; in before rendering svg
## 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.
- [ ] I ran the tests, and all new and existing tests passed.
(As discussed in the comments to #2361)
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Simplify is_config() and normalize_string_keys()
* Use __in__ to avoid the nested _ands_ and _ors_.
* Dict comprehension directly tracks with the doc string
* Keep more basic loop in normalize_string_keys
* Whitespace
* Add logic to filter out warning IDs via environment variable
Usage: SPACY_WARNING_EXCLUDE=W001,W007
* Add warnings for empty vectors
* Add warning if no word vectors are used in .similarity methods
For example, if only tensors are available in small models – should hopefully clear up some confusion around this
* Capture warnings in tests
* Rename SPACY_WARNING_EXCLUDE to SPACY_WARNING_IGNORE
* Go back to using requests instead of urllib (closes#2320)
Fewer dependencies are good, but this one was simply causing too many other problems around SSL verification and Python 2/3 compatibility. requests is a popular enough package that it's okay for spaCy to depend on it – and this will hopefully make model downloads less flakey.
* Only download model if not installed (see #1456)
Use #egg=model==version to allow pip to check for existing installations. The download is only started if no installation matching the package/version is found. Fixes a long-standing inconvenience.
* Pass additional options to pip when installing model (resolves#1456)
Treat all additional arguments passed to the download command as pip options to allow user to customise the command. For example:
python -m spacy download en --user
* Add CLI option to enable installing model package dependencies
* Revert "Add CLI option to enable installing model package dependencies"
This reverts commit 9336ffe695.
* Update documentation
* Add Romanian lemmatizer lookup table.
Adapted from http://www.lexiconista.com/datasets/lemmatization/
by replacing cedillas with commas (ș and ț).
The original dataset is licensed under the Open Database License.
* Fix one blatant issue in the Romanian lemmatizer
* Romanian examples file
* Add ro_tokenizer in conftest
* Add Romanian lemmatizer test
* Update lex_attrs.py
Fixed spelling mistakes of some numbers (according to Brazilian Portuguese).
* Update lex_attrs.py
As requested, I've included the correct spelling for both Brazilian Portuguese and Portuguese Portuguese.
I will advise however, that the two are separated in the future. Brazilian Portuguese is a very different language from the original one, although most of the writing is unified, the way people talk in both countries is radically different. Keeping both languages as one may lead to bigger issues in the future, especially when it comes to spell checking.
* Add contraction forms of some common stopwords
All the stopwords added contain the apostrophe" ' "or " ’ ".
* Adds contributor agreement mauryaland
* Update mauryaland.md
* Port Japanese mecab tokenizer from v1
This brings the Mecab-based Japanese tokenization introduced in #1246 to
spaCy v2. There isn't a JapaneseTagger implementation yet, but POS tag
information from Mecab is stored in a token extension. A tag map is also
included.
As a reminder, Mecab is required because Universal Dependencies are
based on Unidic tags, and Janome doesn't support Unidic.
Things to check:
1. Is this the right way to use a token extension?
2. What's the right way to implement a JapaneseTagger? The approach in
#1246 relied on `tag_from_strings` which is just gone now. I guess the
best thing is to just try training spaCy's default Tagger?
-POLM
* Add tagging/make_doc and tests
* Remove erroneous lemma lookup år > åra in Swedish
* Add contributors agreement
* Add contrib agreement to correct directory
* Revert change to CONTRIBUTOR_AGREEMENT
* Remove incorrect lemma lookup gäng->gänga
In modern Swedish, "gäng" is mostly associated with "gang" or "group of people". The removed lemma lookup lemmatized it to the verb "thread".
* Add contrib agreement to correct directory
* Revert change to CONTRIBUTOR_AGREEMENT
* Add spacy.errors module
* Update deprecation and user warnings
* Replace errors and asserts with new error message system
* Remove redundant asserts
* Fix whitespace
* Add messages for print/util.prints statements
* Fix typo
* Fix typos
* Move CLI messages to spacy.cli._messages
* Add decorator to display error code with message
An implementation like this is nice because it only modifies the string when it's retrieved from the containing class – so we don't have to worry about manipulating tracebacks etc.
* Remove unused link in spacy.about
* Update errors for invalid pipeline components
* Improve error for unknown factories
* Add displaCy warnings
* Update formatting consistency
* Move error message to spacy.errors
* Update errors and check if doc returned by component is None
This patch takes a step towards #1487 by introducing the
doc.retokenize() context manager, to handle merging spans, and soon
splitting tokens.
The idea is to do merging and splitting like this:
with doc.retokenize() as retokenizer:
for start, end, label in matches:
retokenizer.merge(doc[start : end], attrs={'ent_type': label})
The retokenizer accumulates the merge requests, and applies them
together at the end of the block. This will allow retokenization to be
more efficient, and much less error prone.
A retokenizer.split() function will then be added, to handle splitting a
single token into multiple tokens. These methods take `Span` and `Token`
objects; if the user wants to go directly from offsets, they can append
to the .merges and .splits lists on the retokenizer.
The doc.merge() method's behaviour remains unchanged, so this patch
should be 100% backwards incompatible (modulo bugs). Internally,
doc.merge() fixes up the arguments (to handle the various deprecated styles),
opens the retokenizer, and makes the single merge.
We can later start making deprecation warnings on direct calls to doc.merge(),
to migrate people to use of the retokenize context manager.
Changed python set to cpp stl set #2032
## Description
Changed python set to cpp stl set. CPP stl set works better due to the logarithmic run time of its methods. Finding minimum in the cpp set is done in constant time as opposed to the worst case linear runtime of python set. Operations such as find,count,insert,delete are also done in either constant and logarithmic time thus making cpp set a better option to manage vectors.
Reference : http://www.cplusplus.com/reference/set/set/
### Types of change
Enhancement for `Vectors` for faster initialising of word vectors(fasttext)
This patch addresses #1660, which was caused by keying all pre-trained
vectors with the same ID when telling Thinc how to refer to them. This
meant that if multiple models were loaded that had pre-trained vectors,
errors or incorrect behaviour resulted.
The vectors class now includes a .name attribute, which defaults to:
{nlp.meta['lang']_nlp.meta['name']}.vectors
The vectors name is set in the cfg of the pipeline components under the
key pretrained_vectors. This replaces the previous cfg key
pretrained_dims.
In order to make existing models compatible with this change, we check
for the pretrained_dims key when loading models in from_disk and
from_bytes, and add the cfg key pretrained_vectors if we find it.
