brk-code

spaCy の API リファレンス

spaCy (explosion/spaCy) の公開 API 194 件 —— クラス 22、関数 92、メソッド 80。実際のソースを静的解析して抽出した正確なシグネチャを掲載しています。

リポジトリ: explosion/spaCy

種別件数
クラス22
関数92
メソッド80

API 一覧

funcspacy.cli._util.setup_gpu(use_gpu:int, silent=None) -> None
Configure the GPU and log info.
classspacy.cli.benchmark_speed.time_context
Register the running time of a context.
funcspacy.cli.find_threshold.set_nested_item(config:Dict[str, Any], keys:List[str], value:float) -> Dict[str, Any]
Set item in nested dictionary.
funcspacy.cli.info.info_model(model:str, *silent:bool=True) -> Dict[str, Any]
Generate info about a specific model.
funcspacy.cli.info.info_model_url(model:str) -> Dict[str, Any]
Return the download URL for the latest version of a pipeline.
funcspacy.cli.info.info_spacy() -> Dict[str, Any]
Generate info about the current spaCy intallation.
classspacy.cli.init_config.InitValues
Default values for initialization.
funcspacy.cli.package.generate_readme(meta:Dict[str, Any]) -> str
Generate a Markdown-formatted README text from a model meta.json.
funcspacy.cli.validate.reformat_version(version:str) -> str
Hack to reformat old versions ending on '-alpha' to match pip format.
funcspacy.displacy.render(docs:Union[Iterable[Union[Doc, Span, dict]], Doc, Span, dict], style:str='dep', page:bool=False, minify:bool=False, jupyter:Optional[bool]=None, options:Dict[str, Any]={}, manual:bool=False) -> str
Render displaCy visualisation.
classspacy.displacy.render.DependencyRenderer
Render dependency parses as SVGs.
methodspacy.displacy.render.DependencyRenderer.get_arc(x_start:int, y:int, y_curve:int, x_end:int) -> str
Render individual arc.
methodspacy.displacy.render.DependencyRenderer.get_arrowhead(direction:str, x:int, y:int, end:int) -> str
Render individual arrow head.
methodspacy.displacy.render.DependencyRenderer.get_levels(arcs:List[Dict[str, Any]]) -> Dict[Tuple[int, int, str], int]
Calculate available arc height "levels".
methodspacy.displacy.render.DependencyRenderer.render(parsed:List[Dict[str, Any]], page:bool=False, minify:bool=False) -> str
Render complete markup.
methodspacy.displacy.render.DependencyRenderer.render_arrow(label:str, start:int, end:int, direction:str, i:int) -> str
Render individual arrow.
methodspacy.displacy.render.DependencyRenderer.render_svg(render_id:Union[int, str], words:List[Dict[str, Any]], arcs:List[Dict[str, Any]]) -> str
Render SVG.
methodspacy.displacy.render.DependencyRenderer.render_word(text:str, tag:str, lemma:Optional[str], i:int) -> str
Render individual word.
classspacy.displacy.render.EntityRenderer
Render named entities as HTML.
methodspacy.displacy.render.EntityRenderer.render(parsed:List[Dict[str, Any]], page:bool=False, minify:bool=False) -> str
Render complete markup.
methodspacy.displacy.render.EntityRenderer.render_ents(text:str, spans:List[Dict[str, Any]], title:Optional[str]) -> str
Render entities in text.
classspacy.displacy.render.SpanRenderer
Render Spans as SVGs.
methodspacy.displacy.render.SpanRenderer.render(parsed:List[Dict[str, Any]], page:bool=False, minify:bool=False) -> str
Render complete markup.
methodspacy.displacy.render.SpanRenderer.render_spans(tokens:List[str], spans:List[Dict[str, Any]], title:Optional[str]) -> str
Render span types in text.
funcspacy.displacy.serve(docs:Union[Iterable[Doc], Doc], style:str='dep', page:bool=True, minify:bool=False, options:Dict[str, Any]={}, manual:bool=False, port:int=5000, host:str='0.0.0.0', auto_select_port:bool=False) -> None
Serve displaCy visualisation.
funcspacy.lang.ar.lex_attrs.like_num(text)
Check if text resembles a number
funcspacy.lang.bo.lex_attrs.like_num(text)
Check if text resembles a number
funcspacy.lang.ca.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.de.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.el.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
classspacy.lang.en.lemmatizer.EnglishLemmatizer
English lemmatizer.
funcspacy.lang.en.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.es.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.fa.lex_attrs.like_num(text)
check if text resembles a number
funcspacy.lang.fa.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.fi.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.fr.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
classspacy.lang.ht.lemmatizer.HaitianCreoleLemmatizer
Minimal Haitian Creole lemmatizer.
funcspacy.lang.id.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.it.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.ja.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.lb.lex_attrs.like_num(text)
check if text resembles a number
funcspacy.lang.ml.lex_attrs.like_num(text)
Check if text resembles a number
funcspacy.lang.ms.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.nb.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.nl.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.pt.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.sa.lex_attrs.like_num(text)
Check if text resembles a number
funcspacy.lang.sv.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
funcspacy.lang.tr.syntax_iterators.noun_chunks(doclike:Union[Doc, Span]) -> Iterator[Tuple[int, int, int]]
Detect base noun phrases from a dependency parse.
classspacy.language.BaseDefaults
Language data defaults, available via Language.Defaults.
classspacy.language.DisabledPipes
Manager for temporary pipeline disabling.
classspacy.language.Language
A text-processing pipeline.
methodspacy.language.Language.component(name:str, *assigns:Iterable[str]=SimpleFrozenList(), *requires:Iterable[str]=SimpleFrozenList(), *retokenizes:bool=False, *func:Optional[PipeCallable]=None) -> Callable[..., Any]
Register a new pipeline component.
methodspacy.language.Language.component_names() -> List[str]
Get the names of the available pipeline components.
methodspacy.language.Language.config() -> Config
Trainable config for the current language instance.
methodspacy.language.Language.create_pipe(factory_name:str, name:Optional[str]=None, *config:Dict[str, Any]=SimpleFrozenDict(), *raw_config:Optional[Config]=None, *validate:bool=True) -> PipeCallable
Create a pipeline component.
methodspacy.language.Language.disable_pipe(name:str) -> None
Disable a pipeline component.
methodspacy.language.Language.disable_pipes(*names) -> 'DisabledPipes'
Disable one or more pipeline components.
methodspacy.language.Language.disabled() -> List[str]
Get the names of all disabled components.
methodspacy.language.Language.factory_names() -> List[str]
Get names of all available factories.
methodspacy.language.Language.from_bytes(bytes_data:bytes, *exclude:Iterable[str]=SimpleFrozenList()) -> 'Language'
Load state from a binary string.
methodspacy.language.Language.from_disk(path:Union[str, Path], *exclude:Iterable[str]=SimpleFrozenList(), *overrides:Dict[str, Any]=SimpleFrozenDict()) -> 'Language'
Loads state from a directory.
methodspacy.language.Language.get_factory_meta(name:str) -> 'FactoryMeta'
Get the meta information for a given factory name.
methodspacy.language.Language.get_factory_name(name:str) -> str
Get the internal factory name based on the language subclass.
methodspacy.language.Language.get_pipe(name:str) -> PipeCallable
Get a pipeline component for a given component name.
methodspacy.language.Language.get_pipe_config(name:str) -> Config
Get the config used to create a pipeline component.
methodspacy.language.Language.get_pipe_meta(name:str) -> 'FactoryMeta'
Get the meta information for a given component name.
methodspacy.language.Language.has_factory(name:str) -> bool
RETURNS (bool): Whether a factory of that name is registered.
methodspacy.language.Language.has_pipe(name:str) -> bool
Check if a component name is present in the pipeline.
methodspacy.language.Language.make_doc(text:str) -> Doc
Turn a text into a Doc object.
methodspacy.language.Language.meta() -> Dict[str, Any]
Custom meta data of the language class.
methodspacy.language.Language.pipe_names() -> List[str]
Get names of available active pipeline components.
methodspacy.language.Language.remove_pipe(name:str) -> Tuple[str, PipeCallable]
Remove a component from the pipeline.
methodspacy.language.Language.rename_pipe(old_name:str, new_name:str) -> None
Rename a pipeline component.
methodspacy.language.Language.replace_pipe(name:str, factory_name:str, *config:Dict[str, Any]=SimpleFrozenDict(), *validate:bool=True) -> PipeCallable
Replace a component in the pipeline.
methodspacy.language.Language.resume_training(*sgd:Optional[Optimizer]=None) -> Optimizer
Continue training a pretrained model.
methodspacy.language.Language.select_pipes(*disable:Optional[Union[str, Iterable[str]]]=None, *enable:Optional[Union[str, Iterable[str]]]=None) -> 'DisabledPipes'
Disable one or more pipeline components.
methodspacy.language.Language.set_factory_meta(name:str, value:'FactoryMeta') -> None
Set the meta information for a given factory name.
methodspacy.language.Language.to_bytes(*exclude:Iterable[str]=SimpleFrozenList()) -> bytes
Serialize the current state to a binary string.
methodspacy.language.Language.to_disk(path:Union[str, Path], *exclude:Iterable[str]=SimpleFrozenList()) -> None
Save the current state to a directory.
funcspacy.language.create_tokenizer() -> Callable[['Language'], Tokenizer]
Registered function to create a tokenizer.
classspacy.lookups.Lookups
Container for large lookup tables and dictionaries, e.g.
methodspacy.lookups.Lookups.add_table(name:str, data:dict=SimpleFrozenDict()) -> Table
Add a new table to the lookups.
methodspacy.lookups.Lookups.from_bytes(bytes_data:bytes, **kwargs) -> 'Lookups'
Load the lookups from a bytestring.
methodspacy.lookups.Lookups.from_disk(path:Union[str, Path], filename:str='lookups.bin', **kwargs) -> 'Lookups'
Load lookups from a directory containing a lookups.bin.
methodspacy.lookups.Lookups.get_table(name:str, default:Any=UNSET) -> Table
Get a table.
methodspacy.lookups.Lookups.has_table(name:str) -> bool
Check if the lookups contain a table of a given name.
methodspacy.lookups.Lookups.remove_table(name:str) -> Table
Remove a table.
methodspacy.lookups.Lookups.set_table(name:str, table:Table) -> None
Set a table.
methodspacy.lookups.Lookups.tables() -> List[str]
RETURNS (List[str]): Names of all tables in the lookups.
methodspacy.lookups.Lookups.to_bytes(**kwargs) -> bytes
Serialize the lookups to a bytestring.
methodspacy.lookups.Lookups.to_disk(path:Union[str, Path], filename:str='lookups.bin', **kwargs) -> None
Save the lookups to a directory as lookups.bin.
classspacy.lookups.Table
A table in the lookups.
methodspacy.lookups.Table.from_bytes(bytes_data:bytes) -> 'Table'
Load a table from a bytestring.
methodspacy.lookups.Table.from_dict(data:dict, name:Optional[str]=None) -> 'Table'
Initialize a new table from a dict.
methodspacy.lookups.Table.get(key:Union[str, int], default:Optional[Any]=None) -> Any
Get the value for a given key.
methodspacy.lookups.Table.set(key:Union[str, int], value:Any) -> None
Set new key/value pair.
methodspacy.lookups.Table.to_bytes() -> bytes
Serialize table to a bytestring.
funcspacy.ml.extract_spans.forward(model:Model, source_spans:Tuple[Ragged, Ragged], is_train:bool) -> Tuple[Ragged, Callable]
Get subsequences from source vectors.
funcspacy.ml.models.tok2vec.BiLSTMEncoder(width:int, depth:int, dropout:float) -> Model[List[Floats2d], List[Floats2d]]
Encode context using bidirectonal LSTM layers.
funcspacy.pipeline._edit_tree_internals.schemas.validate_edit_tree(obj:Dict[str, Any]) -> List[str]
Validate edit tree.
classspacy.pipeline.entity_linker.EntityLinker
Pipeline component for named entity linking.
methodspacy.pipeline.entity_linker.EntityLinker.from_disk(path:Union[str, Path], *exclude:Iterable[str]=SimpleFrozenList()) -> 'EntityLinker'
Load the pipe from disk.
methodspacy.pipeline.entity_linker.EntityLinker.set_annotations(docs:Iterable[Doc], kb_ids:List[str]) -> None
Modify a batch of documents, using pre-computed scores.
methodspacy.pipeline.entity_linker.EntityLinker.to_disk(path:Union[str, Path], *exclude:Iterable[str]=SimpleFrozenList()) -> None
Serialize the pipe to disk.
funcspacy.pipeline.factories.register_factories() -> None
Register all factories with the registry.
funcspacy.pipeline.functions.merge_entities(doc:Doc)
Merge entities into a single token.
funcspacy.pipeline.functions.merge_noun_chunks(doc:Doc) -> Doc
Merge noun chunks into a single token.
funcspacy.pipeline.functions.merge_subtokens(doc:Doc, label:str='subtok') -> Doc
Merge subtokens into a single token.
classspacy.pipeline.legacy.entity_linker.EntityLinker_v1
Pipeline component for named entity linking.
methodspacy.pipeline.legacy.entity_linker.EntityLinker_v1.from_disk(path:Union[str, Path], *exclude:Iterable[str]=SimpleFrozenList()) -> 'EntityLinker_v1'
Load the pipe from disk.
methodspacy.pipeline.legacy.entity_linker.EntityLinker_v1.to_disk(path:Union[str, Path], *exclude:Iterable[str]=SimpleFrozenList()) -> None
Serialize the pipe to disk.
classspacy.pipeline.span_finder.SpanFinder
Pipeline that learns span boundaries.
methodspacy.pipeline.span_finder.SpanFinder.set_annotations(docs:Iterable[Doc], scores:Floats2d) -> None
Modify a batch of Doc objects, using pre-computed scores.
classspacy.pipeline.spancat.SpanCategorizer
Pipeline component to label spans of text.
methodspacy.pipeline.spancat.SpanCategorizer.add_label(label:str) -> int
Add a new label to the pipe.
methodspacy.pipeline.spancat.SpanCategorizer.key() -> str
Key of the doc.spans dict to save the spans under.
methodspacy.pipeline.spancat.SpanCategorizer.set_annotations(docs:Iterable[Doc], indices_scores) -> None
Modify a batch of Doc objects, using pre-computed scores.
funcspacy.pipeline.spancat.build_ngram_suggester(sizes:List[int]) -> Suggester
Suggest all spans of the given lengths.
classspacy.pipeline.textcat.TextCategorizer
Pipeline component for single-label text classification.
methodspacy.pipeline.textcat.TextCategorizer.add_label(label:str) -> int
Add a new label to the pipe.
methodspacy.pipeline.textcat.TextCategorizer.set_annotations(docs:Iterable[Doc], scores) -> None
Modify a batch of Doc objects, using pre-computed scores.
funcspacy.registrations.populate_registry() -> None
Populate the registry with all necessary components.
classspacy.schemas.DocJSONSchema
JSON/dict format for JSON representation of Doc objects.
funcspacy.schemas.get_arg_model(func:Callable, *exclude:Iterable[str]=tuple(), *name:str='ArgModel', *strict:bool=True) -> type[BaseModel]
Generate a pydantic model for function arguments.
funcspacy.schemas.validate(schema:Type[BaseModel], obj:Dict[str, Any]) -> List[str]
Validate data against a given pydantic schema.
classspacy.scorer.PRFScore
A precision / recall / F score.
classspacy.scorer.ROCAUCScore
An AUC ROC score.
classspacy.scorer.Scorer
Compute evaluation scores.
methodspacy.scorer.Scorer.score(examples:Iterable[Example], *per_component:bool=False) -> Dict[str, Any]
Evaluate a list of Examples.
methodspacy.scorer.Scorer.score_links(examples:Iterable[Example], *negative_labels:Iterable[str], **cfg) -> Dict[str, Any]
Returns PRF for predicted links on the entity level.
methodspacy.scorer.Scorer.score_tokenization(examples:Iterable[Example], **cfg) -> Dict[str, Any]
Returns accuracy and PRF scores for tokenization.
classspacy.tokens._serialize.DocBin
Pack Doc objects for binary serialization.
methodspacy.tokens._serialize.DocBin.add(doc:Doc) -> None
Add a Doc's annotations to the DocBin for serialization.
methodspacy.tokens._serialize.DocBin.from_bytes(bytes_data:bytes) -> 'DocBin'
Deserialize the DocBin's annotations from a bytestring.
methodspacy.tokens._serialize.DocBin.from_disk(path:Union[str, Path]) -> 'DocBin'
Load the DocBin from a file (typically called .spacy).
methodspacy.tokens._serialize.DocBin.get_docs(vocab:Vocab) -> Iterator[Doc]
Recover Doc objects from the annotations, using the given vocab.
methodspacy.tokens._serialize.DocBin.merge(other:'DocBin') -> None
Extend the annotations of this DocBin with the annotations from another.
methodspacy.tokens._serialize.DocBin.to_bytes() -> bytes
Serialize the DocBin's annotations to a bytestring.
methodspacy.tokens._serialize.DocBin.to_disk(path:Union[str, Path]) -> None
Save the DocBin to a file (typically called .spacy).
funcspacy.tokens.underscore.get_ext_args(**kwargs:Any)
Validate and convert arguments.
methodspacy.training.corpus.Corpus.read_docbin(vocab:Vocab, locs:Iterable[Union[str, Path]]) -> Iterator[Doc]
Yield training examples as example dicts
funcspacy.training.initialize.open_file(loc:Union[str, Path]) -> IO
Handle .gz, .tar.gz or unzipped files
funcspacy.training.loop.clean_output_dir(path:Optional[Path]) -> None
Remove an existing output directory.
funcspacy.training.loop.train(nlp:'Language', output_path:Optional[Path]=None, *use_gpu:int=-1, *stdout:IO=sys.stdout, *stderr:IO=sys.stderr) -> Tuple['Language', Optional[Path]]
Train a pipeline.
funcspacy.training.pretrain.make_update(model:Model, docs:Iterable[Doc], optimizer:Optimizer, objective_func:Callable) -> float
Perform an update over a single batch of documents.
funcspacy.util.add_lookups(default_func:Callable[[str], Any], *lookups) -> Callable[[str], Any]
Extend an attribute function with special cases.
funcspacy.util.check_bool_env_var(env_var:str) -> bool
Convert the value of an environment variable to a boolean.
funcspacy.util.compile_infix_regex(entries:Iterable[Union[str, Pattern]]) -> Pattern
Compile a sequence of infix rules into a regex object.
funcspacy.util.compile_prefix_regex(entries:Iterable[Union[str, Pattern]]) -> Pattern
Compile a sequence of prefix rules into a regex object.
funcspacy.util.compile_suffix_regex(entries:Iterable[Union[str, Pattern]]) -> Pattern
Compile a sequence of suffix rules into a regex object.
funcspacy.util.copy_config(config:Union[Dict[str, Any], Config]) -> Config
Deep copy a Config.
funcspacy.util.dict_to_dot(obj:Dict[str, dict], *for_overrides:bool=False) -> Dict[str, Any]
Convert dot notation to a dict.
funcspacy.util.dot_to_dict(values:Dict[str, Any]) -> Dict[str, dict]
Convert dot notation to a dict.
funcspacy.util.ensure_path(path:Any) -> Any
Ensure string is converted to a Path.
funcspacy.util.escape_html(text:str) -> str
Replace <, >, &, " with their HTML encoded representation.
funcspacy.util.filter_spans(spans:Iterable['Span']) -> List['Span']
Filter a sequence of spans and remove duplicates or overlaps.
funcspacy.util.find_available_port(start:int, host:str, auto_select:bool=False) -> int
Given a starting port and a host, handle finding a port.
funcspacy.util.get_arg_names(func:Callable) -> List[str]
Get a list of all named arguments of a function (regular, keyword-only).
funcspacy.util.get_base_version(version:str) -> str
Generate the base version without any prerelease identifiers.
funcspacy.util.get_lang_class(lang:str) -> Type['Language']
Import and load a Language class.
funcspacy.util.get_model_lower_version(constraint:str) -> Optional[str]
From a version range like >=1.2.3,<1.3.0 return the lower pin.
funcspacy.util.get_model_meta(path:Union[str, Path]) -> Dict[str, Any]
Get model meta.json from a directory path and validate its contents.
funcspacy.util.get_module_path(module:ModuleType) -> Path
Get the path of a Python module.
funcspacy.util.get_object_name(obj:Any) -> str
Get a human-readable name of a Python object, e.g.
funcspacy.util.get_package_path(name:str) -> Path
Get the path to an installed package.
funcspacy.util.get_package_version(name:str) -> Optional[str]
Get the version of an installed package.
funcspacy.util.import_file(name:str, loc:Union[str, Path]) -> ModuleType
Import module from a file.
funcspacy.util.is_compatible_version(version:str, constraint:str, prereleases:bool=True) -> Optional[bool]
Check if a version (e.g.
funcspacy.util.is_cython_func(func:Callable) -> bool
Slightly hacky check for whether a callable is implemented in Cython.
funcspacy.util.is_package(name:str) -> bool
Check if string maps to a package installed via pip.
funcspacy.util.is_prerelease_version(version:str) -> bool
Check whether a version is a prerelease version.
funcspacy.util.lang_class_is_loaded(lang:str) -> bool
Check whether a Language class is already loaded.
funcspacy.util.load_config(path:Union[str, Path], overrides:Dict[str, Any]=SimpleFrozenDict(), interpolate:bool=False) -> Config
Load a config file.
funcspacy.util.load_config_from_str(text:str, overrides:Dict[str, Any]=SimpleFrozenDict(), interpolate:bool=False)
Load a full config from a string.
funcspacy.util.load_language_data(path:Union[str, Path]) -> Union[dict, list]
Load JSON language data using the given path as a base.
funcspacy.util.load_meta(path:Union[str, Path]) -> Dict[str, Any]
Load a model meta.json from a path and validate its contents.
funcspacy.util.minibatch(items, size)
Iterate over batches of items.
funcspacy.util.minify_html(html:str) -> str
Perform a template-specific, rudimentary HTML minification for displaCy.
methodspacy.util.registry.ensure_populated() -> None
Ensure the registry is populated with all necessary components.
methodspacy.util.registry.get(registry_name:str, func_name:str) -> Callable
Get a registered function from the registry.
methodspacy.util.registry.get_registry_names() -> List[str]
List all available registries.
methodspacy.util.registry.has(registry_name:str, func_name:str) -> bool
Check whether a function is available in a registry.
funcspacy.util.replace_model_node(model:Model, target:Model, replacement:Model) -> None
Replace a node within a model with a new one, updating refs.
funcspacy.util.resolve_dot_names(config:Config, dot_names:List[Optional[str]]) -> Tuple[Any, ...]
Resolve one or more "dot notation" names, e.g.
funcspacy.util.run_command(command:Union[str, List[str]], *stdin:Optional[Any]=None, *capture:bool=False) -> subprocess.CompletedProcess
Run a command on the command line as a subprocess.
funcspacy.util.set_dot_to_object(config:Config, section:str, value:Any) -> None
Update a config at a given position from a dot notation.
funcspacy.util.set_lang_class(name:str, cls:Type['Language']) -> None
Set a custom Language class name that can be loaded via get_lang_class.
funcspacy.util.split_command(command:str) -> List[str]
Split a string command using shlex.
funcspacy.util.split_requirement(requirement:str) -> Tuple[str, str]
Split a requirement like spacy>=1.2.3 into ("spacy", ">=1.2.3").
funcspacy.util.update_exc(base_exceptions:Dict[str, List[dict]], *addition_dicts) -> Dict[str, List[dict]]
Update and validate tokenizer exceptions.
funcspacy.util.walk_dict(node:Dict[str, Any], parent:List[str]=[], *for_overrides:bool=False) -> Iterator[Tuple[List[str], Any]]
Walk a dict and yield the path and values of the leaves.
funcspacy.util.working_dir(path:Union[str, Path]) -> Iterator[Path]
Change current working directory and returns to previous on exit.

この情報について

掲載しているシグネチャは explosion/spaCy の公開ソースコードを Python の ast モジュールで静的解析し、引数名・デフォルト値・ 型注釈・戻り値型をそのまま抽出したものです。実装コードは保存していません。 詳しくは仕組みの解説をご覧ください。

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