spaCy/spacy/tokens/__init__.py

from .doc import Doc
from .token import Token
from .span import Span
from .span_group import SpanGroup
from ._serialize import DocBin
from .morphanalysis import MorphAnalysis

__all__ = ["Doc", "Token", "Span", "SpanGroup", "DocBin", "MorphAnalysis"]
* Import classes in spacy.tokens.__init__ 2015-07-13 20:48:55 +03:00			`from .doc import Doc`
			`from .token import Token`
* Rename spans.pyx to span.pyx 2015-11-03 16:14:40 +03:00			`from .span import Span`
Fix SpanGroup import (#7182) * import SpanGroup from tokens module * revert edits from different PR * add to __all__ 2021-02-24 13:06:16 +03:00			`from .span_group import SpanGroup`
DocPallet -> DocBin 2019-09-18 16:15:37 +03:00			`from ._serialize import DocBin`
Modify morphology to support arbitrary features (#4932) * Restructure tag maps for MorphAnalysis changes Prepare tag maps for upcoming MorphAnalysis changes that allow arbritrary features. * Use default tag map rather than duplicating for ca / uk / vi * Import tag map into defaults for ga * Modify tag maps so all morphological fields and features are strings * Move features from `"Other"` to the top level * Rewrite tuples as strings separated by `","` * Rewrite morph symbols for fr lemmatizer as strings * Export MorphAnalysis under spacy.tokens * Modify morphology to support arbitrary features Modify `Morphology` and `MorphAnalysis` so that arbitrary features are supported. * Modify `MorphAnalysisC` so that it can support arbitrary features and multiple values per field. `MorphAnalysisC` is redesigned to contain: * key: hash of UD FEATS string of morphological features * array of `MorphFeatureC` structs that each contain a hash of `Field` and `Field=Value` for a given morphological feature, which makes it possible to: * find features by field * represent multiple values for a given field * `get_field()` is renamed to `get_by_field()` and is no longer `nogil`. Instead a new helper function `get_n_by_field()` is `nogil` and returns `n` features by field. * `MorphAnalysis.get()` returns all possible values for a field as a list of individual features such as `["Tense=Pres", "Tense=Past"]`. * `MorphAnalysis`'s `str()` and `repr()` are the UD FEATS string. * `Morphology.feats_to_dict()` converts a UD FEATS string to a dict where: * Each field has one entry in the dict * Multiple values remain separated by a separator in the value string * `Token.morph_` returns the UD FEATS string and you can set `Token.morph_` with a UD FEATS string or with a tag map dict. * Modify get_by_field to use np.ndarray Modify `get_by_field()` to use np.ndarray. Remove `max_results` from `get_n_by_field()` and always iterate over all the fields. * Rewrite without MorphFeatureC * Add shortcut for existing feats strings as keys Add shortcut for existing feats strings as keys in `Morphology.add()`. * Check for '_' as empty analysis when adding morphs * Extend helper converters in Morphology Add and extend helper converters that convert and normalize between: * UD FEATS strings (`"Case=dat,gen\|Number=sing"`) * per-field dict of feats (`{"Case": "dat,gen", "Number": "sing"}`) * list of individual features (`["Case=dat", "Case=gen", "Number=sing"]`) All converters sort fields and values where applicable. 2020-01-24 00:01:54 +03:00			`from .morphanalysis import MorphAnalysis`
* Import classes in spacy.tokens.__init__ 2015-07-13 20:48:55 +03:00
Fix SpanGroup import (#7182) * import SpanGroup from tokens module * revert edits from different PR * add to __all__ 2021-02-24 13:06:16 +03:00			`__all__ = ["Doc", "Token", "Span", "SpanGroup", "DocBin", "MorphAnalysis"]`