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	* 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 commit70f4e8adf3. * 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 commitbdebbef455. * 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 commit62358dd867. * 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
		
			
				
	
	
		
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| //- 💫 DOCS > API > TOKEN
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| 
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| include ../_includes/_mixins
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| 
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| p An individual token — i.e. a word, punctuation symbol, whitespace, etc.
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| 
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| +h(2, "init") Token.__init__
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|     +tag method
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| 
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| p Construct a #[code Token] object.
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| 
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| +aside-code("Example").
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|     doc = nlp(u'Give it back! He pleaded.')
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|     token = doc[0]
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|     assert token.text == u'Give'
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| 
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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 A storage container for lexical types.
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| 
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|     +row
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|         +cell #[code doc]
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|         +cell #[code Doc]
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|         +cell The parent document.
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| 
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|     +row
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|         +cell #[code offset]
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|         +cell int
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|         +cell The index of the token within the document.
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| 
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|     +row("foot")
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|         +cell returns
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|         +cell #[code Token]
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|         +cell The newly constructed object.
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| 
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| +h(2, "len") Token.__len__
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|     +tag method
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| 
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| p The number of unicode characters in the token, i.e. #[code token.text].
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| 
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| +aside-code("Example").
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|     doc = nlp(u'Give it back! He pleaded.')
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|     token = doc[0]
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|     assert len(token) == 4
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| 
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| +table(["Name", "Type", "Description"])
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|     +row("foot")
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|         +cell returns
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|         +cell int
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|         +cell The number of unicode characters in the token.
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| 
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| +h(2, "set_extension") Token.set_extension
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|     +tag classmethod
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|     +tag-new(2)
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| 
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| p
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|     |  Define a custom attribute on the #[code Token] which becomes available
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|     |  via #[code Token._]. For details, see the documentation on
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|     |  #[+a("/usage/processing-pipelines#custom-components-attributes") custom attributes].
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| 
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| +aside-code("Example").
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|     from spacy.tokens import Token
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|     fruit_getter = lambda token: token.text in ('apple', 'pear', 'banana')
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|     Token.set_extension('is_fruit', getter=fruit_getter)
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|     doc = nlp(u'I have an apple')
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|     assert doc[3]._.is_fruit
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| 
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| +table(["Name", "Type", "Description"])
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|     +row
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|         +cell #[code name]
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|         +cell unicode
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|         +cell
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|             |  Name of the attribute to set by the extension. For example,
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|             |  #[code 'my_attr'] will be available as #[code token._.my_attr].
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| 
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|     +row
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|         +cell #[code default]
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|         +cell -
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|         +cell
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|             |  Optional default value of the attribute if no getter or method
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|             |  is defined.
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| 
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|     +row
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|         +cell #[code method]
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|         +cell callable
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|         +cell
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|             |  Set a custom method on the object, for example
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|             |  #[code token._.compare(other_token)].
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| 
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|     +row
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|         +cell #[code getter]
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|         +cell callable
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|         +cell
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|             |  Getter function that takes the object and returns an attribute
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|             |  value. Is called when the user accesses the #[code ._] attribute.
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| 
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|     +row
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|         +cell #[code setter]
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|         +cell callable
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|         +cell
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|             |  Setter function that takes the #[code Token] and a value, and
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|             |  modifies the object. Is called when the user writes to the
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|             |  #[code Token._] attribute.
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| 
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| +h(2, "get_extension") Token.get_extension
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|     +tag classmethod
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|     +tag-new(2)
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| 
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| p
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|     |  Look up a previously registered extension by name. Returns a 4-tuple
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|     |  #[code.u-break (default, method, getter, setter)] if the extension is
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|     |  registered. Raises a #[code KeyError] otherwise.
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| 
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| +aside-code("Example").
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|     from spacy.tokens import Token
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|     Token.set_extension('is_fruit', default=False)
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|     extension = Token.get_extension('is_fruit')
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|     assert extension == (False, None, None, None)
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| 
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| +table(["Name", "Type", "Description"])
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|     +row
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|         +cell #[code name]
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|         +cell unicode
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|         +cell Name of the extension.
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| 
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|     +row("foot")
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|         +cell returns
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|         +cell tuple
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|         +cell
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|             |  A #[code.u-break (default, method, getter, setter)] tuple of the
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|             |  extension.
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| 
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| +h(2, "has_extension") Token.has_extension
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|     +tag classmethod
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|     +tag-new(2)
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| 
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| p Check whether an extension has been registered on the #[code Token] class.
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| 
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| +aside-code("Example").
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|     from spacy.tokens import Token
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|     Token.set_extension('is_fruit', default=False)
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|     assert Token.has_extension('is_fruit')
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| 
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| +table(["Name", "Type", "Description"])
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|     +row
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|         +cell #[code name]
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|         +cell unicode
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|         +cell Name of the extension to check.
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| 
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|     +row("foot")
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|         +cell returns
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|         +cell bool
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|         +cell Whether the extension has been registered.
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| 
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| +h(2, "remove_extension") Token.remove_extension
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|     +tag classmethod
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|     +tag-new("2.0.11")
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| 
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| p Remove a previously registered extension.
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| 
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| +aside-code("Example").
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|     from spacy.tokens import Token
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|     Token.set_extension('is_fruit', default=False)
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|     removed = Token.remove_extension('is_fruit')
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|     assert not Token.has_extension('is_fruit')
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| 
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| +table(["Name", "Type", "Description"])
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|     +row
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|         +cell #[code name]
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|         +cell unicode
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|         +cell Name of the extension.
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| 
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|     +row("foot")
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|         +cell returns
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|         +cell tuple
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|         +cell
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|             |  A #[code.u-break (default, method, getter, setter)] tuple of the
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|             |  removed extension.
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| 
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| +h(2, "check_flag") Token.check_flag
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|     +tag method
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| 
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| p Check the value of a boolean flag.
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| 
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| +aside-code("Example").
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|     from spacy.attrs import IS_TITLE
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|     doc = nlp(u'Give it back! He pleaded.')
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|     token = doc[0]
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|     assert token.check_flag(IS_TITLE) == True
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| 
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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 check.
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| 
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|     +row("foot")
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|         +cell returns
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|         +cell bool
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|         +cell Whether the flag is set.
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| 
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| +h(2, "similarity") Token.similarity
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|     +tag method
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|     +tag-model("vectors")
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| 
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| p Compute a semantic similarity estimate. Defaults to cosine over vectors.
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| 
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| +aside-code("Example").
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|     apples, _, oranges = nlp(u'apples and oranges')
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|     apples_oranges = apples.similarity(oranges)
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|     oranges_apples = oranges.similarity(apples)
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|     assert apples_oranges == oranges_apples
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| 
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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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| 
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|     +row("foot")
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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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| 
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| +h(2, "nbor") Token.nbor
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|     +tag method
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| 
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| p Get a neighboring token.
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| 
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| +aside-code("Example").
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|     doc = nlp(u'Give it back! He pleaded.')
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|     give_nbor = doc[0].nbor()
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|     assert give_nbor.text == u'it'
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| 
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| +table(["Name", "Type", "Description"])
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|     +row
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|         +cell #[code i]
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|         +cell int
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|         +cell The relative position of the token to get. Defaults to #[code 1].
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| 
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|     +row("foot")
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|         +cell returns
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|         +cell #[code Token]
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|         +cell The token at position #[code self.doc[self.i+i]].
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| 
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| +h(2, "is_ancestor") Token.is_ancestor
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|     +tag method
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|     +tag-model("parse")
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| 
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| p
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|     |  Check whether this token is a parent, grandparent, etc. of another
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|     |  in the dependency tree.
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| 
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| +aside-code("Example").
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|     doc = nlp(u'Give it back! He pleaded.')
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|     give = doc[0]
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|     it = doc[1]
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|     assert give.is_ancestor(it)
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| 
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| +table(["Name", "Type", "Description"])
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|     +row
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|         +cell descendant
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|         +cell #[code Token]
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|         +cell Another token.
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| 
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|     +row("foot")
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|         +cell returns
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|         +cell bool
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|         +cell Whether this token is the ancestor of the descendant.
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| 
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| +h(2, "ancestors") Token.ancestors
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|     +tag property
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|     +tag-model("parse")
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| 
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| p The rightmost token of this token's syntactic descendants.
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| 
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| +aside-code("Example").
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|     doc = nlp(u'Give it back! He pleaded.')
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|     it_ancestors = doc[1].ancestors
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|     assert [t.text for t in it_ancestors] == [u'Give']
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|     he_ancestors = doc[4].ancestors
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|     assert [t.text for t in he_ancestors] == [u'pleaded']
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| 
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| +table(["Name", "Type", "Description"])
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|     +row("foot")
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|         +cell yields
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|         +cell #[code Token]
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|         +cell
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|             |  A sequence of ancestor tokens such that
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|             |  #[code ancestor.is_ancestor(self)].
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| 
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| +h(2, "conjuncts") Token.conjuncts
 | |
|     +tag property
 | |
|     +tag-model("parse")
 | |
| 
 | |
| p A sequence of coordinated tokens, including the token itself.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'I like apples and oranges')
 | |
|     apples_conjuncts = doc[2].conjuncts
 | |
|     assert [t.text for t in apples_conjuncts] == [u'oranges']
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell yields
 | |
|         +cell #[code Token]
 | |
|         +cell A coordinated token.
 | |
| 
 | |
| +h(2, "children") Token.children
 | |
|     +tag property
 | |
|     +tag-model("parse")
 | |
| 
 | |
| p A sequence of the token's immediate syntactic children.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'Give it back! He pleaded.')
 | |
|     give_children = doc[0].children
 | |
|     assert [t.text for t in give_children] == [u'it', u'back', u'!']
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell yields
 | |
|         +cell #[code Token]
 | |
|         +cell A child token such that #[code child.head==self].
 | |
| 
 | |
| +h(2, "lefts") Token.lefts
 | |
|     +tag property
 | |
|     +tag-model("parse")
 | |
| 
 | |
| p
 | |
|     |  The leftward immediate children of the word, in the syntactic dependency
 | |
|     |  parse.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'I like New York in Autumn.')
 | |
|     lefts = [t.text for t in doc[3].lefts]
 | |
|     assert lefts == [u'New']
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell yields
 | |
|         +cell #[code Token]
 | |
|         +cell A left-child of the token.
 | |
| 
 | |
| +h(2, "rights") Token.rights
 | |
|     +tag property
 | |
|     +tag-model("parse")
 | |
| 
 | |
| p
 | |
|     |  The rightward immediate children of the word, in the syntactic
 | |
|     |  dependency parse.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'I like New York in Autumn.')
 | |
|     rights = [t.text for t in doc[3].rights]
 | |
|     assert rights == [u'in']
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell yields
 | |
|         +cell #[code Token]
 | |
|         +cell A right-child of the token.
 | |
| 
 | |
| +h(2, "n_lefts") Token.n_lefts
 | |
|     +tag property
 | |
|     +tag-model("parse")
 | |
| 
 | |
| p
 | |
|     |  The number of leftward immediate children of the word, in the syntactic
 | |
|     |  dependency parse.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'I like New York in Autumn.')
 | |
|     assert doc[3].n_lefts == 1
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell returns
 | |
|         +cell int
 | |
|         +cell The number of left-child tokens.
 | |
| 
 | |
| +h(2, "n_rights") Token.n_rights
 | |
|     +tag property
 | |
|     +tag-model("parse")
 | |
| 
 | |
| p
 | |
|     |  The number of rightward immediate children of the word, in the syntactic
 | |
|     |  dependency parse.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'I like New York in Autumn.')
 | |
|     assert doc[3].n_rights == 1
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell returns
 | |
|         +cell int
 | |
|         +cell The number of right-child tokens.
 | |
| 
 | |
| +h(2, "subtree") Token.subtree
 | |
|     +tag property
 | |
|     +tag-model("parse")
 | |
| 
 | |
| p A sequence of all the token's syntactic descendants.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'Give it back! He pleaded.')
 | |
|     give_subtree = doc[0].subtree
 | |
|     assert [t.text for t in give_subtree] == [u'Give', u'it', u'back', u'!']
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell yields
 | |
|         +cell #[code Token]
 | |
|         +cell A descendant token such that #[code self.is_ancestor(descendant)].
 | |
| 
 | |
| +h(2, "is_sent_start") Token.is_sent_start
 | |
|     +tag property
 | |
|     +tag-new(2)
 | |
| 
 | |
| p
 | |
|     |  A boolean value indicating whether the token starts a sentence.
 | |
|     |  #[code None] if unknown.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'Give it back! He pleaded.')
 | |
|     assert doc[4].is_sent_start
 | |
|     assert not doc[5].is_sent_start
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell returns
 | |
|         +cell bool
 | |
|         +cell Whether the token starts a sentence.
 | |
| 
 | |
| +infobox("Changed in v2.0", "⚠️")
 | |
|     |  As of spaCy v2.0, the #[code Token.sent_start] property is deprecated and
 | |
|     |  has been replaced with #[code Token.is_sent_start], which returns a
 | |
|     |  boolean value instead of a misleading #[code 0] for #[code False] and
 | |
|     |  #[code 1] for #[code True]. It also now returns #[code None] if the
 | |
|     |  answer is unknown, and fixes a quirk in the old logic that would always
 | |
|     |  set the property to #[code 0] for the first word of the document.
 | |
| 
 | |
|     +code-wrapper
 | |
|         +code-new assert doc[4].is_sent_start == True
 | |
|         +code-old assert doc[4].sent_start == 1
 | |
| 
 | |
| +h(2, "has_vector") Token.has_vector
 | |
|     +tag property
 | |
|     +tag-model("vectors")
 | |
| 
 | |
| p
 | |
|     |  A boolean value indicating whether a word vector is associated with the
 | |
|     |  token.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'I like apples')
 | |
|     apples = doc[2]
 | |
|     assert apples.has_vector
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell returns
 | |
|         +cell bool
 | |
|         +cell Whether the token has a vector data attached.
 | |
| 
 | |
| +h(2, "vector") Token.vector
 | |
|     +tag property
 | |
|     +tag-model("vectors")
 | |
| 
 | |
| p A real-valued meaning representation.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'I like apples')
 | |
|     apples = doc[2]
 | |
|     assert apples.vector.dtype == 'float32'
 | |
|     assert apples.vector.shape == (300,)
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell returns
 | |
|         +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
 | |
|         +cell A 1D numpy array representing the token's semantics.
 | |
| 
 | |
| +h(2, "vector_norm") Token.vector_norm
 | |
|     +tag property
 | |
|     +tag-model("vectors")
 | |
| 
 | |
| p The L2 norm of the token's vector representation.
 | |
| 
 | |
| +aside-code("Example").
 | |
|     doc = nlp(u'I like apples and pasta')
 | |
|     apples = doc[2]
 | |
|     pasta = doc[4]
 | |
|     apples.vector_norm # 6.89589786529541
 | |
|     pasta.vector_norm # 7.759851932525635
 | |
|     assert apples.vector_norm != pasta.vector_norm
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row("foot")
 | |
|         +cell returns
 | |
|         +cell float
 | |
|         +cell The L2 norm of the vector representation.
 | |
| 
 | |
| +h(2, "attributes") Attributes
 | |
| 
 | |
| +table(["Name", "Type", "Description"])
 | |
|     +row
 | |
|         +cell #[code doc]
 | |
|         +cell #[code Doc]
 | |
|         +cell The parent document.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code sent]
 | |
|             +tag-new("2.0.12")
 | |
|         +cell #[code Span]
 | |
|         +cell The sentence span that this token is a part of.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code text]
 | |
|         +cell unicode
 | |
|         +cell Verbatim text content.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code text_with_ws]
 | |
|         +cell unicode
 | |
|         +cell Text content, with trailing space character if present.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code whitespace_]
 | |
|         +cell unicode
 | |
|         +cell Trailing space character if present.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code orth]
 | |
|         +cell int
 | |
|         +cell ID of the verbatim text content.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code orth_]
 | |
|         +cell unicode
 | |
|         +cell
 | |
|             |  Verbatim text content (identical to #[code Token.text]). Exists
 | |
|             |  mostly for consistency with the other attributes.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code vocab]
 | |
|         +cell #[code Vocab]
 | |
|         +cell The vocab object of the parent #[code Doc].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code doc]
 | |
|         +cell #[code Doc]
 | |
|         +cell The parent document.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code head]
 | |
|         +cell #[code Token]
 | |
|         +cell The syntactic parent, or "governor", of this token.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code left_edge]
 | |
|         +cell #[code Token]
 | |
|         +cell The leftmost token of this token's syntactic descendants.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code right_edge]
 | |
|         +cell #[code Token]
 | |
|         +cell The rightmost token of this token's syntactic descendants.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code i]
 | |
|         +cell int
 | |
|         +cell The index of the token within the parent document.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code ent_type]
 | |
|         +cell int
 | |
|         +cell Named entity type.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code ent_type_]
 | |
|         +cell unicode
 | |
|         +cell Named entity type.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code ent_iob]
 | |
|         +cell int
 | |
|         +cell
 | |
|             |  IOB code of named entity tag. #[code "B"]
 | |
|             |  means the token begins an entity, #[code "I"] means it is inside
 | |
|             |  an entity, #[code "O"] means it is outside an entity, and
 | |
|             |  #[code ""] means no entity tag is set.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code ent_iob_]
 | |
|         +cell unicode
 | |
|         +cell
 | |
|             |  IOB code of named entity tag. #[code "B"]
 | |
|             |  means the token begins an entity, #[code "I"] means it is inside
 | |
|             |  an entity, #[code "O"] means it is outside an entity, and
 | |
|             |  #[code ""] means no entity tag is set.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code ent_id]
 | |
|         +cell int
 | |
|         +cell
 | |
|             |  ID of the entity the token is an instance of, if any. Currently
 | |
|             |  not used, but potentially for coreference resolution.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code ent_id_]
 | |
|         +cell unicode
 | |
|         +cell
 | |
|             |  ID of the entity the token is an instance of, if any. Currently
 | |
|             |  not used, but potentially for coreference resolution.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code lemma]
 | |
|         +cell int
 | |
|         +cell
 | |
|             |  Base form of the token, with no inflectional suffixes.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code lemma_]
 | |
|         +cell unicode
 | |
|         +cell Base form of the token, with no inflectional suffixes.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code norm]
 | |
|         +cell int
 | |
|         +cell
 | |
|             |  The token's norm, i.e. a normalised form of the token text.
 | |
|             |  Usually set in the language's
 | |
|             |  #[+a("/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions] or
 | |
|             |  #[+a("/usage/adding-languages#norm-exceptions") norm exceptions].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code norm_]
 | |
|         +cell unicode
 | |
|         +cell
 | |
|             |  The token's norm, i.e. a normalised form of the token text.
 | |
|             |  Usually set in the language's
 | |
|             |  #[+a("/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions] or
 | |
|             |  #[+a("/usage/adding-languages#norm-exceptions") norm exceptions].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code lower]
 | |
|         +cell int
 | |
|         +cell Lowercase form of the token.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code lower_]
 | |
|         +cell unicode
 | |
|         +cell
 | |
|             |  Lowercase form of the token text. Equivalent to
 | |
|             |  #[code Token.text.lower()].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code shape]
 | |
|         +cell int
 | |
|         +cell
 | |
|             |  Transform of the tokens's string, to show orthographic features.
 | |
|             |  For example, "Xxxx" or "dd".
 | |
| 
 | |
|     +row
 | |
|         +cell #[code shape_]
 | |
|         +cell unicode
 | |
|         +cell
 | |
|             |  Transform of the tokens's string, to show orthographic features.
 | |
|             |  For example, "Xxxx" or "dd".
 | |
| 
 | |
|     +row
 | |
|         +cell #[code prefix]
 | |
|         +cell int
 | |
|         +cell
 | |
|             |  Hash value of a length-N substring from the start of the
 | |
|             |  token. Defaults to #[code N=1].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code prefix_]
 | |
|         +cell unicode
 | |
|         +cell
 | |
|             |  A length-N substring from the start of the token. Defaults to
 | |
|             |  #[code N=1].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code suffix]
 | |
|         +cell int
 | |
|         +cell
 | |
|             |  Hash value of a length-N substring from the end of the token.
 | |
|             |  Defaults to #[code N=3].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code suffix_]
 | |
|         +cell unicode
 | |
|         +cell
 | |
|             |  Length-N substring from the end of the token. Defaults to
 | |
|             |  #[code N=3].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_alpha]
 | |
|         +cell bool
 | |
|         +cell
 | |
|             |  Does the token consist of alphabetic characters? Equivalent to
 | |
|             |  #[code token.text.isalpha()].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_ascii]
 | |
|         +cell bool
 | |
|         +cell
 | |
|             |  Does the token consist of ASCII characters? Equivalent to
 | |
|             |  #[code [any(ord(c) >= 128 for c in token.text)]].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_digit]
 | |
|         +cell bool
 | |
|         +cell
 | |
|             |  Does the token consist of digits? Equivalent to
 | |
|             |  #[code token.text.isdigit()].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_lower]
 | |
|         +cell bool
 | |
|         +cell
 | |
|             |  Is the token in lowercase? Equivalent to
 | |
|             |  #[code token.text.islower()].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_upper]
 | |
|         +cell bool
 | |
|         +cell
 | |
|             |  Is the token in uppercase? Equivalent to
 | |
|             |  #[code token.text.isupper()].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_title]
 | |
|         +cell bool
 | |
|         +cell
 | |
|             |  Is the token in titlecase? Equivalent to
 | |
|             |  #[code token.text.istitle()].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_punct]
 | |
|         +cell bool
 | |
|         +cell Is the token punctuation?
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_left_punct]
 | |
|         +cell bool
 | |
|         +cell Is the token a left punctuation mark, e.g. #[code (]?
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_right_punct]
 | |
|         +cell bool
 | |
|         +cell Is the token a right punctuation mark, e.g. #[code )]?
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_space]
 | |
|         +cell bool
 | |
|         +cell
 | |
|             |  Does the token consist of whitespace characters? Equivalent to
 | |
|             |  #[code token.text.isspace()].
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_bracket]
 | |
|         +cell bool
 | |
|         +cell Is the token a bracket?
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_quote]
 | |
|         +cell bool
 | |
|         +cell Is the token a quotation mark?
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_currency]
 | |
|             +tag-new("2.0.8")
 | |
|         +cell bool
 | |
|         +cell Is the token a currency symbol?
 | |
| 
 | |
|     +row
 | |
|         +cell #[code like_url]
 | |
|         +cell bool
 | |
|         +cell Does the token resemble a URL?
 | |
| 
 | |
|     +row
 | |
|         +cell #[code like_num]
 | |
|         +cell bool
 | |
|         +cell Does the token represent a number? e.g. "10.9", "10", "ten", etc.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code like_email]
 | |
|         +cell bool
 | |
|         +cell Does the token resemble an email address?
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_oov]
 | |
|         +cell bool
 | |
|         +cell Is the token out-of-vocabulary?
 | |
| 
 | |
|     +row
 | |
|         +cell #[code is_stop]
 | |
|         +cell bool
 | |
|         +cell Is the token part of a "stop list"?
 | |
| 
 | |
|     +row
 | |
|         +cell #[code pos]
 | |
|         +cell int
 | |
|         +cell Coarse-grained part-of-speech.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code pos_]
 | |
|         +cell unicode
 | |
|         +cell Coarse-grained part-of-speech.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code tag]
 | |
|         +cell int
 | |
|         +cell Fine-grained part-of-speech.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code tag_]
 | |
|         +cell unicode
 | |
|         +cell Fine-grained part-of-speech.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code dep]
 | |
|         +cell int
 | |
|         +cell Syntactic dependency relation.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code dep_]
 | |
|         +cell unicode
 | |
|         +cell Syntactic dependency relation.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code lang]
 | |
|         +cell int
 | |
|         +cell Language of the parent document's vocabulary.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code lang_]
 | |
|         +cell unicode
 | |
|         +cell Language of the parent document's vocabulary.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code prob]
 | |
|         +cell float
 | |
|         +cell Smoothed log probability estimate of token's type.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code idx]
 | |
|         +cell int
 | |
|         +cell The character offset of the token within the parent document.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code sentiment]
 | |
|         +cell float
 | |
|         +cell
 | |
|             |  A scalar value indicating the positivity or negativity of the
 | |
|             |  token.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code lex_id]
 | |
|         +cell int
 | |
|         +cell Sequential ID of the token's lexical type.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code rank]
 | |
|         +cell int
 | |
|         +cell
 | |
|             |  Sequential ID of the token's lexical type, used to index into
 | |
|             |  tables, e.g. for word vectors.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code cluster]
 | |
|         +cell int
 | |
|         +cell Brown cluster ID.
 | |
| 
 | |
|     +row
 | |
|         +cell #[code _]
 | |
|         +cell #[code Underscore]
 | |
|         +cell
 | |
|             |  User space for adding custom
 | |
|             |  #[+a("/usage/processing-pipelines#custom-components-attributes") attribute extensions].
 |