2016-10-31 21:04:15 +03:00
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//- 💫 DOCS > API > LEXEME
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2017-10-03 15:27:22 +03:00
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include ../_includes/_mixins
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2016-10-31 21:04:15 +03:00
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2017-05-20 16:13:42 +03:00
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p
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| An entry in the vocabulary. A #[code Lexeme] has no string context – it's
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2017-05-23 12:32:25 +03:00
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| a word type, as opposed to a word token. It therefore has no
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2017-05-20 16:13:42 +03:00
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| part-of-speech tag, dependency parse, or lemma (if lemmatization depends
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| on the part-of-speech tag).
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+h(2, "init") Lexeme.__init__
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+tag method
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p Create a #[code Lexeme] object.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell The parent vocabulary.
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+row
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+cell #[code orth]
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+cell int
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+cell The orth id of the lexeme.
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-20 16:13:42 +03:00
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+cell returns
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+cell #[code Lexeme]
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+cell The newly constructed object.
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+h(2, "set_flag") Lexeme.set_flag
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+tag method
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p Change the value of a boolean flag.
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+aside-code("Example").
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COOL_FLAG = nlp.vocab.add_flag(lambda text: False)
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nlp.vocab[u'spaCy'].set_flag(COOL_FLAG, True)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to set.
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+row
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+cell #[code value]
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+cell bool
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+cell The new value of the flag.
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+h(2, "check_flag") Lexeme.check_flag
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+tag method
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p Check the value of a boolean flag.
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+aside-code("Example").
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is_my_library = lambda text: text in ['spaCy', 'Thinc']
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MY_LIBRARY = nlp.vocab.add_flag(is_my_library)
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assert nlp.vocab[u'spaCy'].check_flag(MY_LIBRARY) == True
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to query.
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-20 16:13:42 +03:00
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+cell returns
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+cell bool
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+cell The value of the flag.
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+h(2, "similarity") Lexeme.similarity
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+tag method
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+tag-model("vectors")
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p Compute a semantic similarity estimate. Defaults to cosine over vectors.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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orange = nlp.vocab[u'orange']
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apple_orange = apple.similarity(orange)
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orange_apple = orange.similarity(apple)
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assert apple_orange == orange_apple
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+table(["Name", "Type", "Description"])
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+row
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+cell other
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+cell -
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+cell
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| The object to compare with. By default, accepts #[code Doc],
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| #[code Span], #[code Token] and #[code Lexeme] objects.
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-20 16:13:42 +03:00
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+cell returns
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+cell float
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+cell A scalar similarity score. Higher is more similar.
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+h(2, "has_vector") Lexeme.has_vector
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+tag property
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+tag-model("vectors")
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p
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| A boolean value indicating whether a word vector is associated with the
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| lexeme.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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assert apple.has_vector
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+table(["Name", "Type", "Description"])
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+row("foot")
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2017-05-20 16:13:42 +03:00
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+cell returns
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+cell bool
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+cell Whether the lexeme has a vector data attached.
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+h(2, "vector") Lexeme.vector
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+tag property
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+tag-model("vectors")
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p A real-valued meaning representation.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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assert apple.vector.dtype == 'float32'
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assert apple.vector.shape == (300,)
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+table(["Name", "Type", "Description"])
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+row("foot")
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2017-05-20 16:13:42 +03:00
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+cell returns
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2017-08-19 13:44:23 +03:00
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell A 1D numpy array representing the lexeme's semantics.
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+h(2, "vector_norm") Lexeme.vector_norm
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+tag property
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+tag-model("vectors")
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p The L2 norm of the lexeme's vector representation.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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pasta = nlp.vocab[u'pasta']
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apple.vector_norm # 7.1346845626831055
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pasta.vector_norm # 7.759851932525635
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assert apple.vector_norm != pasta.vector_norm
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+table(["Name", "Type", "Description"])
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-20 16:13:42 +03:00
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+cell returns
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+cell float
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+cell The L2 norm of the vector representation.
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2016-10-31 21:04:15 +03:00
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+h(2, "attributes") Attributes
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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2017-10-27 22:07:50 +03:00
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+cell The lexeme's vocabulary.
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2017-05-20 16:13:42 +03:00
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+row
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+cell #[code text]
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+cell unicode
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+cell Verbatim text content.
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2017-10-27 22:07:50 +03:00
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+row
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+cell #[code orth]
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+cell int
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+cell ID of the verbatim text content.
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+row
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+cell #[code orth_]
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+cell unicode
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+cell
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💫 Port master changes over to develop (#2979)
* Create aryaprabhudesai.md (#2681)
* Update _install.jade (#2688)
Typo fix: "models" -> "model"
* Add FAC to spacy.explain (resolves #2706)
* Remove docstrings for deprecated arguments (see #2703)
* When calling getoption() in conftest.py, pass a default option (#2709)
* When calling getoption() in conftest.py, pass a default option
This is necessary to allow testing an installed spacy by running:
pytest --pyargs spacy
* Add contributor agreement
* update bengali token rules for hyphen and digits (#2731)
* Less norm computations in token similarity (#2730)
* Less norm computations in token similarity
* Contributor agreement
* Remove ')' for clarity (#2737)
Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know.
* added contributor agreement for mbkupfer (#2738)
* Basic support for Telugu language (#2751)
* Lex _attrs for polish language (#2750)
* Signed spaCy contributor agreement
* Added polish version of english lex_attrs
* Introduces a bulk merge function, in order to solve issue #653 (#2696)
* Fix comment
* Introduce bulk merge to increase performance on many span merges
* Sign contributor agreement
* Implement pull request suggestions
* Describe converters more explicitly (see #2643)
* Add multi-threading note to Language.pipe (resolves #2582) [ci skip]
* Fix formatting
* Fix dependency scheme docs (closes #2705) [ci skip]
* Don't set stop word in example (closes #2657) [ci skip]
* Add words to portuguese language _num_words (#2759)
* Add words to portuguese language _num_words
* Add words to portuguese language _num_words
* Update Indonesian model (#2752)
* adding e-KTP in tokenizer exceptions list
* add exception token
* removing lines with containing space as it won't matter since we use .split() method in the end, added new tokens in exception
* add tokenizer exceptions list
* combining base_norms with norm_exceptions
* adding norm_exception
* fix double key in lemmatizer
* remove unused import on punctuation.py
* reformat stop_words to reduce number of lines, improve readibility
* updating tokenizer exception
* implement is_currency for lang/id
* adding orth_first_upper in tokenizer_exceptions
* update the norm_exception list
* remove bunch of abbreviations
* adding contributors file
* Fixed spaCy+Keras example (#2763)
* bug fixes in keras example
* created contributor agreement
* Adding French hyphenated first name (#2786)
* Fix typo (closes #2784)
* Fix typo (#2795) [ci skip]
Fixed typo on line 6 "regcognizer --> recognizer"
* Adding basic support for Sinhala language. (#2788)
* adding Sinhala language package, stop words, examples and lex_attrs.
* Adding contributor agreement
* Updating contributor agreement
* Also include lowercase norm exceptions
* Fix error (#2802)
* Fix error
ValueError: cannot resize an array that references or is referenced
by another array in this way. Use the resize function
* added spaCy Contributor Agreement
* Add charlax's contributor agreement (#2805)
* agreement of contributor, may I introduce a tiny pl languge contribution (#2799)
* Contributors agreement
* Contributors agreement
* Contributors agreement
* Add jupyter=True to displacy.render in documentation (#2806)
* Revert "Also include lowercase norm exceptions"
This reverts commit 70f4e8adf37cfcfab60be2b97d6deae949b30e9e.
* Remove deprecated encoding argument to msgpack
* Set up dependency tree pattern matching skeleton (#2732)
* Fix bug when too many entity types. Fixes #2800
* Fix Python 2 test failure
* Require older msgpack-numpy
* Restore encoding arg on msgpack-numpy
* Try to fix version pin for msgpack-numpy
* Update Portuguese Language (#2790)
* Add words to portuguese language _num_words
* Add words to portuguese language _num_words
* Portuguese - Add/remove stopwords, fix tokenizer, add currency symbols
* Extended punctuation and norm_exceptions in the Portuguese language
* Correct error in spacy universe docs concerning spacy-lookup (#2814)
* Update Keras Example for (Parikh et al, 2016) implementation (#2803)
* bug fixes in keras example
* created contributor agreement
* baseline for Parikh model
* initial version of parikh 2016 implemented
* tested asymmetric models
* fixed grevious error in normalization
* use standard SNLI test file
* begin to rework parikh example
* initial version of running example
* start to document the new version
* start to document the new version
* Update Decompositional Attention.ipynb
* fixed calls to similarity
* updated the README
* import sys package duh
* simplified indexing on mapping word to IDs
* stupid python indent error
* added code from https://github.com/tensorflow/tensorflow/issues/3388 for tf bug workaround
* Fix typo (closes #2815) [ci skip]
* Update regex version dependency
* Set version to 2.0.13.dev3
* Skip seemingly problematic test
* Remove problematic test
* Try previous version of regex
* Revert "Remove problematic test"
This reverts commit bdebbef45552d698d390aa430b527ee27830f11b.
* Unskip test
* Try older version of regex
* 💫 Update training examples and use minibatching (#2830)
<!--- Provide a general summary of your changes in the title. -->
## Description
Update the training examples in `/examples/training` to show usage of spaCy's `minibatch` and `compounding` helpers ([see here](https://spacy.io/usage/training#tips-batch-size) for details). The lack of batching in the examples has caused some confusion in the past, especially for beginners who would copy-paste the examples, update them with large training sets and experienced slow and unsatisfying results.
### Types of change
enhancements
## 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.
* Visual C++ link updated (#2842) (closes #2841) [ci skip]
* New landing page
* Add contribution agreement
* Correcting lang/ru/examples.py (#2845)
* Correct some grammatical inaccuracies in lang\ru\examples.py; filled Contributor Agreement
* Correct some grammatical inaccuracies in lang\ru\examples.py
* Move contributor agreement to separate file
* Set version to 2.0.13.dev4
* Add Persian(Farsi) language support (#2797)
* Also include lowercase norm exceptions
* Remove in favour of https://github.com/explosion/spaCy/graphs/contributors
* Rule-based French Lemmatizer (#2818)
<!--- Provide a general summary of your changes in the title. -->
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
Add a rule-based French Lemmatizer following the english one and the excellent PR for [greek language optimizations](https://github.com/explosion/spaCy/pull/2558) to adapt the Lemmatizer class.
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
- Lemma dictionary used can be found [here](http://infolingu.univ-mlv.fr/DonneesLinguistiques/Dictionnaires/telechargement.html), I used the XML version.
- Add several files containing exhaustive list of words for each part of speech
- Add some lemma rules
- Add POS that are not checked in the standard Lemmatizer, i.e PRON, DET, ADV and AUX
- Modify the Lemmatizer class to check in lookup table as a last resort if POS not mentionned
- Modify the lemmatize function to check in lookup table as a last resort
- Init files are updated so the model can support all the functionalities mentioned above
- Add words to tokenizer_exceptions_list.py in respect to regex used in tokenizer_exceptions.py
## 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.
* Set version to 2.0.13
* Fix formatting and consistency
* Update docs for new version [ci skip]
* Increment version [ci skip]
* Add info on wheels [ci skip]
* Adding "This is a sentence" example to Sinhala (#2846)
* Add wheels badge
* Update badge [ci skip]
* Update README.rst [ci skip]
* Update murmurhash pin
* Increment version to 2.0.14.dev0
* Update GPU docs for v2.0.14
* Add wheel to setup_requires
* Import prefer_gpu and require_gpu functions from Thinc
* Add tests for prefer_gpu() and require_gpu()
* Update requirements and setup.py
* Workaround bug in thinc require_gpu
* Set version to v2.0.14
* Update push-tag script
* Unhack prefer_gpu
* Require thinc 6.10.6
* Update prefer_gpu and require_gpu docs [ci skip]
* Fix specifiers for GPU
* Set version to 2.0.14.dev1
* Set version to 2.0.14
* Update Thinc version pin
* Increment version
* Fix msgpack-numpy version pin
* Increment version
* Update version to 2.0.16
* Update version [ci skip]
* Redundant ')' in the Stop words' example (#2856)
<!--- Provide a general summary of your changes in the title. -->
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [ ] I have submitted the spaCy Contributor Agreement.
- [ ] I ran the tests, and all new and existing tests passed.
- [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Documentation improvement regarding joblib and SO (#2867)
Some documentation improvements
## Description
1. Fixed the dead URL to joblib
2. Fixed Stack Overflow brand name (with space)
### Types of change
Documentation
## 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.
* raise error when setting overlapping entities as doc.ents (#2880)
* Fix out-of-bounds access in NER training
The helper method state.B(1) gets the index of the first token of the
buffer, or -1 if no such token exists. Normally this is safe because we
pass this to functions like state.safe_get(), which returns an empty
token. Here we used it directly as an array index, which is not okay!
This error may have been the cause of out-of-bounds access errors during
training. Similar errors may still be around, so much be hunted down.
Hunting this one down took a long time...I printed out values across
training runs and diffed, looking for points of divergence between
runs, when no randomness should be allowed.
* Change PyThaiNLP Url (#2876)
* Fix missing comma
* Add example showing a fix-up rule for space entities
* Set version to 2.0.17.dev0
* Update regex version
* Revert "Update regex version"
This reverts commit 62358dd867d15bc6a475942dff34effba69dd70a.
* Try setting older regex version, to align with conda
* Set version to 2.0.17
* Add spacy-js to universe [ci-skip]
* Add spacy-raspberry to universe (closes #2889)
* Add script to validate universe json [ci skip]
* Removed space in docs + added contributor indo (#2909)
* - removed unneeded space in documentation
* - added contributor info
* Allow input text of length up to max_length, inclusive (#2922)
* Include universe spec for spacy-wordnet component (#2919)
* feat: include universe spec for spacy-wordnet component
* chore: include spaCy contributor agreement
* Minor formatting changes [ci skip]
* Fix image [ci skip]
Twitter URL doesn't work on live site
* Check if the word is in one of the regular lists specific to each POS (#2886)
* 💫 Create random IDs for SVGs to prevent ID clashes (#2927)
Resolves #2924.
## Description
Fixes problem where multiple visualizations in Jupyter notebooks would have clashing arc IDs, resulting in weirdly positioned arc labels. Generating a random ID prefix so even identical parses won't receive the same IDs for consistency (even if effect of ID clash isn't noticable here.)
### Types of change
bug fix
## 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.
* Fix typo [ci skip]
* fixes symbolic link on py3 and windows (#2949)
* fixes symbolic link on py3 and windows
during setup of spacy using command
python -m spacy link en_core_web_sm en
closes #2948
* Update spacy/compat.py
Co-Authored-By: cicorias <cicorias@users.noreply.github.com>
* Fix formatting
* Update universe [ci skip]
* Catalan Language Support (#2940)
* Catalan language Support
* Ddding Catalan to documentation
* Sort languages alphabetically [ci skip]
* Update tests for pytest 4.x (#2965)
<!--- Provide a general summary of your changes in the title. -->
## Description
- [x] Replace marks in params for pytest 4.0 compat ([see here](https://docs.pytest.org/en/latest/deprecations.html#marks-in-pytest-mark-parametrize))
- [x] Un-xfail passing tests (some fixes in a recent update resolved a bunch of issues, but tests were apparently never updated here)
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
## 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.
* Fix regex pin to harmonize with conda (#2964)
* Update README.rst
* Fix bug where Vocab.prune_vector did not use 'batch_size' (#2977)
Fixes #2976
* Fix typo
* Fix typo
* Remove duplicate file
* Require thinc 7.0.0.dev2
Fixes bug in gpu_ops that would use cupy instead of numpy on CPU
* Add missing import
* Fix error IDs
* Fix tests
2018-11-29 18:30:29 +03:00
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| Verbatim text content (identical to #[code Lexeme.text]). Exists
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2017-10-27 22:07:50 +03:00
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| mostly for consistency with the other attributes.
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2017-05-20 16:13:42 +03:00
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+row
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+cell #[code lex_id]
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+cell int
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+cell ID of the lexeme's lexical type.
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2017-10-27 22:07:50 +03:00
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+row
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+cell #[code rank]
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+cell int
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+cell
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| Sequential ID of the lexemes's lexical type, used to index into
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| tables, e.g. for word vectors.
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+row
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+cell #[code flags]
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+cell int
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+cell Container of the lexeme's binary flags.
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+row
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+cell #[code norm]
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+cell int
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+cell The lexemes's norm, i.e. a normalised form of the lexeme text.
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+row
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+cell #[code norm_]
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+cell unicode
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+cell The lexemes's norm, i.e. a normalised form of the lexeme text.
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code lower]
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+cell int
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+cell Lowercase form of the word.
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+row
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+cell #[code lower_]
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+cell unicode
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+cell Lowercase form of the word.
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+row
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+cell #[code shape]
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+cell int
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+cell Transform of the word's string, to show orthographic features.
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+row
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+cell #[code shape_]
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+cell unicode
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+cell Transform of the word's string, to show orthographic features.
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+row
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+cell #[code prefix]
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+cell int
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+cell
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| Length-N substring from the start of the word. Defaults to
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| #[code N=1].
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+row
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+cell #[code prefix_]
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+cell unicode
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+cell
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| Length-N substring from the start of the word. Defaults to
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| #[code N=1].
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+row
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+cell #[code suffix]
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+cell int
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+cell
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| Length-N substring from the end of the word. Defaults to
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| #[code N=3].
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+row
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+cell #[code suffix_]
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+cell unicode
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+cell
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| Length-N substring from the start of the word. Defaults to
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| #[code N=3].
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+row
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+cell #[code is_alpha]
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+cell bool
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2017-05-26 13:43:16 +03:00
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+cell
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| Does the lexeme consist of alphabetic characters? Equivalent to
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| #[code lexeme.text.isalpha()].
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+row
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+cell #[code is_ascii]
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+cell bool
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+cell
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| Does the lexeme consist of ASCII characters? Equivalent to
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| #[code [any(ord(c) >= 128 for c in lexeme.text)]].
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+row
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+cell #[code is_digit]
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+cell bool
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2017-05-26 13:43:16 +03:00
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+cell
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| Does the lexeme consist of digits? Equivalent to
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| #[code lexeme.text.isdigit()].
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code is_lower]
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+cell bool
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2017-05-26 13:43:16 +03:00
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+cell
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| Is the lexeme in lowercase? Equivalent to
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| #[code lexeme.text.islower()].
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+row
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+cell #[code is_upper]
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+cell bool
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+cell
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| Is the lexeme in uppercase? Equivalent to
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| #[code lexeme.text.isupper()].
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code is_title]
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+cell bool
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+cell
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| Is the lexeme in titlecase? Equivalent to
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| #[code lexeme.text.istitle()].
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+row
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+cell #[code is_punct]
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+cell bool
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+cell Is the lexeme punctuation?
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+row
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+cell #[code is_left_punct]
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+cell bool
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+cell Is the lexeme a left punctuation mark, e.g. #[code (]?
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+row
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+cell #[code is_right_punct]
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+cell bool
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+cell Is the lexeme a right punctuation mark, e.g. #[code )]?
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+row
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+cell #[code is_space]
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+cell bool
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+cell
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| Does the lexeme consist of whitespace characters? Equivalent to
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| #[code lexeme.text.isspace()].
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+row
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+cell #[code is_bracket]
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+cell bool
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+cell Is the lexeme a bracket?
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+row
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+cell #[code is_quote]
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+cell bool
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+cell Is the lexeme a quotation mark?
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+row
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+cell #[code is_currency]
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+tag-new("2.0.8")
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+cell bool
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+cell Is the lexeme a currency symbol?
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+row
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+cell #[code like_url]
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+cell bool
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+cell Does the lexeme resemble a URL?
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+row
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+cell #[code like_num]
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+cell bool
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+cell Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc.
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+row
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+cell #[code like_email]
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+cell bool
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+cell Does the lexeme resemble an email address?
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+row
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+cell #[code is_oov]
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+cell bool
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+cell Is the lexeme out-of-vocabulary?
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+row
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+cell #[code is_stop]
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+cell bool
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+cell Is the lexeme part of a "stop list"?
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+row
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+cell #[code lang]
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+cell int
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+cell Language of the parent vocabulary.
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+row
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+cell #[code lang_]
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+cell unicode
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+cell Language of the parent vocabulary.
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+row
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+cell #[code prob]
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+cell float
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+cell Smoothed log probability estimate of the lexeme's type.
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+row
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+cell #[code cluster]
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+cell int
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+cell Brown cluster ID.
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+row
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+cell #[code sentiment]
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+cell float
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+cell
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| A scalar value indicating the positivity or negativity of the
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| lexeme.
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