spaCy/website/api/token.jade
Ines Montani d33953037e
💫 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 70f4e8adf3.

* 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 bdebbef455.

* 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 62358dd867.

* 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 16:30:29 +01:00

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//- 💫 DOCS > API > TOKEN
include ../_includes/_mixins
p An individual token — i.e. a word, punctuation symbol, whitespace, etc.
+h(2, "init") Token.__init__
+tag method
p Construct a #[code Token] object.
+aside-code("Example").
doc = nlp(u'Give it back! He pleaded.')
token = doc[0]
assert token.text == u'Give'
+table(["Name", "Type", "Description"])
+row
+cell #[code vocab]
+cell #[code Vocab]
+cell A storage container for lexical types.
+row
+cell #[code doc]
+cell #[code Doc]
+cell The parent document.
+row
+cell #[code offset]
+cell int
+cell The index of the token within the document.
+row("foot")
+cell returns
+cell #[code Token]
+cell The newly constructed object.
+h(2, "len") Token.__len__
+tag method
p The number of unicode characters in the token, i.e. #[code token.text].
+aside-code("Example").
doc = nlp(u'Give it back! He pleaded.')
token = doc[0]
assert len(token) == 4
+table(["Name", "Type", "Description"])
+row("foot")
+cell returns
+cell int
+cell The number of unicode characters in the token.
+h(2, "set_extension") Token.set_extension
+tag classmethod
+tag-new(2)
p
| Define a custom attribute on the #[code Token] which becomes available
| via #[code Token._]. For details, see the documentation on
| #[+a("/usage/processing-pipelines#custom-components-attributes") custom attributes].
+aside-code("Example").
from spacy.tokens import Token
fruit_getter = lambda token: token.text in ('apple', 'pear', 'banana')
Token.set_extension('is_fruit', getter=fruit_getter)
doc = nlp(u'I have an apple')
assert doc[3]._.is_fruit
+table(["Name", "Type", "Description"])
+row
+cell #[code name]
+cell unicode
+cell
| Name of the attribute to set by the extension. For example,
| #[code 'my_attr'] will be available as #[code token._.my_attr].
+row
+cell #[code default]
+cell -
+cell
| Optional default value of the attribute if no getter or method
| is defined.
+row
+cell #[code method]
+cell callable
+cell
| Set a custom method on the object, for example
| #[code token._.compare(other_token)].
+row
+cell #[code getter]
+cell callable
+cell
| Getter function that takes the object and returns an attribute
| value. Is called when the user accesses the #[code ._] attribute.
+row
+cell #[code setter]
+cell callable
+cell
| Setter function that takes the #[code Token] and a value, and
| modifies the object. Is called when the user writes to the
| #[code Token._] attribute.
+h(2, "get_extension") Token.get_extension
+tag classmethod
+tag-new(2)
p
| Look up a previously registered extension by name. Returns a 4-tuple
| #[code.u-break (default, method, getter, setter)] if the extension is
| registered. Raises a #[code KeyError] otherwise.
+aside-code("Example").
from spacy.tokens import Token
Token.set_extension('is_fruit', default=False)
extension = Token.get_extension('is_fruit')
assert extension == (False, None, None, None)
+table(["Name", "Type", "Description"])
+row
+cell #[code name]
+cell unicode
+cell Name of the extension.
+row("foot")
+cell returns
+cell tuple
+cell
| A #[code.u-break (default, method, getter, setter)] tuple of the
| extension.
+h(2, "has_extension") Token.has_extension
+tag classmethod
+tag-new(2)
p Check whether an extension has been registered on the #[code Token] class.
+aside-code("Example").
from spacy.tokens import Token
Token.set_extension('is_fruit', default=False)
assert Token.has_extension('is_fruit')
+table(["Name", "Type", "Description"])
+row
+cell #[code name]
+cell unicode
+cell Name of the extension to check.
+row("foot")
+cell returns
+cell bool
+cell Whether the extension has been registered.
+h(2, "remove_extension") Token.remove_extension
+tag classmethod
+tag-new("2.0.11")
p Remove a previously registered extension.
+aside-code("Example").
from spacy.tokens import Token
Token.set_extension('is_fruit', default=False)
removed = Token.remove_extension('is_fruit')
assert not Token.has_extension('is_fruit')
+table(["Name", "Type", "Description"])
+row
+cell #[code name]
+cell unicode
+cell Name of the extension.
+row("foot")
+cell returns
+cell tuple
+cell
| A #[code.u-break (default, method, getter, setter)] tuple of the
| removed extension.
+h(2, "check_flag") Token.check_flag
+tag method
p Check the value of a boolean flag.
+aside-code("Example").
from spacy.attrs import IS_TITLE
doc = nlp(u'Give it back! He pleaded.')
token = doc[0]
assert token.check_flag(IS_TITLE) == True
+table(["Name", "Type", "Description"])
+row
+cell #[code flag_id]
+cell int
+cell The attribute ID of the flag to check.
+row("foot")
+cell returns
+cell bool
+cell Whether the flag is set.
+h(2, "similarity") Token.similarity
+tag method
+tag-model("vectors")
p Compute a semantic similarity estimate. Defaults to cosine over vectors.
+aside-code("Example").
apples, _, oranges = nlp(u'apples and oranges')
apples_oranges = apples.similarity(oranges)
oranges_apples = oranges.similarity(apples)
assert apples_oranges == oranges_apples
+table(["Name", "Type", "Description"])
+row
+cell other
+cell -
+cell
| The object to compare with. By default, accepts #[code Doc],
| #[code Span], #[code Token] and #[code Lexeme] objects.
+row("foot")
+cell returns
+cell float
+cell A scalar similarity score. Higher is more similar.
+h(2, "nbor") Token.nbor
+tag method
p Get a neighboring token.
+aside-code("Example").
doc = nlp(u'Give it back! He pleaded.')
give_nbor = doc[0].nbor()
assert give_nbor.text == u'it'
+table(["Name", "Type", "Description"])
+row
+cell #[code i]
+cell int
+cell The relative position of the token to get. Defaults to #[code 1].
+row("foot")
+cell returns
+cell #[code Token]
+cell The token at position #[code self.doc[self.i+i]].
+h(2, "is_ancestor") Token.is_ancestor
+tag method
+tag-model("parse")
p
| Check whether this token is a parent, grandparent, etc. of another
| in the dependency tree.
+aside-code("Example").
doc = nlp(u'Give it back! He pleaded.')
give = doc[0]
it = doc[1]
assert give.is_ancestor(it)
+table(["Name", "Type", "Description"])
+row
+cell descendant
+cell #[code Token]
+cell Another token.
+row("foot")
+cell returns
+cell bool
+cell Whether this token is the ancestor of the descendant.
+h(2, "ancestors") Token.ancestors
+tag property
+tag-model("parse")
p The rightmost token of this token's syntactic descendants.
+aside-code("Example").
doc = nlp(u'Give it back! He pleaded.')
it_ancestors = doc[1].ancestors
assert [t.text for t in it_ancestors] == [u'Give']
he_ancestors = doc[4].ancestors
assert [t.text for t in he_ancestors] == [u'pleaded']
+table(["Name", "Type", "Description"])
+row("foot")
+cell yields
+cell #[code Token]
+cell
| A sequence of ancestor tokens such that
| #[code ancestor.is_ancestor(self)].
+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].