mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 10:26:35 +03:00
d33953037e
* 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
890 lines
23 KiB
Plaintext
890 lines
23 KiB
Plaintext
//- 💫 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].
|