mirror of https://github.com/explosion/spaCy.git
265 lines
15 KiB
Markdown
265 lines
15 KiB
Markdown
---
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title: Tokenizer
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teaser: Segment text into words, punctuations marks, etc.
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tag: class
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source: spacy/tokenizer.pyx
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---
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> #### Default config
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>
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> ```ini
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> [nlp.tokenizer]
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> @tokenizers = "spacy.Tokenizer.v1"
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> ```
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Segment text, and create `Doc` objects with the discovered segment boundaries.
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For a deeper understanding, see the docs on
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[how spaCy's tokenizer works](/usage/linguistic-features#how-tokenizer-works).
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The tokenizer is typically created automatically when a
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[`Language`](/api/language) subclass is initialized and it reads its settings
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like punctuation and special case rules from the
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[`Language.Defaults`](/api/language#defaults) provided by the language subclass.
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## Tokenizer.\_\_init\_\_ {#init tag="method"}
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Create a `Tokenizer` to create `Doc` objects given unicode text. For examples of
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how to construct a custom tokenizer with different tokenization rules, see the
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[usage documentation](https://spacy.io/usage/linguistic-features#native-tokenizers).
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> #### Example
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>
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> ```python
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> # Construction 1
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> from spacy.tokenizer import Tokenizer
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> from spacy.lang.en import English
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> nlp = English()
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> # Create a blank Tokenizer with just the English vocab
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> tokenizer = Tokenizer(nlp.vocab)
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>
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> # Construction 2
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> from spacy.lang.en import English
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> nlp = English()
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> # Create a Tokenizer with the default settings for English
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> # including punctuation rules and exceptions
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> tokenizer = nlp.tokenizer
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> ```
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| Name | Description |
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| -------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | A storage container for lexical types. ~~Vocab~~ |
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| `rules` | Exceptions and special-cases for the tokenizer. ~~Optional[Dict[str, List[Dict[int, str]]]]~~ |
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| `prefix_search` | A function matching the signature of `re.compile(string).search` to match prefixes. ~~Optional[Callable[[str], Optional[Match]]]~~ |
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| `suffix_search` | A function matching the signature of `re.compile(string).search` to match suffixes. ~~Optional[Callable[[str], Optional[Match]]]~~ |
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| `infix_finditer` | A function matching the signature of `re.compile(string).finditer` to find infixes. ~~Optional[Callable[[str], Iterator[Match]]]~~ |
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| `token_match` | A function matching the signature of `re.compile(string).match` to find token matches. ~~Optional[Callable[[str], Optional[Match]]]~~ |
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| `url_match` | A function matching the signature of `re.compile(string).match` to find token matches after considering prefixes and suffixes. ~~Optional[Callable[[str], Optional[Match]]]~~ |
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| `faster_heuristics` <Tag variant="new">3.3.0</Tag> | Whether to restrict the final `Matcher`-based pass for rules to those containing affixes or space. Defaults to `True`. ~~bool~~ |
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## Tokenizer.\_\_call\_\_ {#call tag="method"}
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Tokenize a string.
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> #### Example
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>
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> ```python
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> tokens = tokenizer("This is a sentence")
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> assert len(tokens) == 4
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> ```
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| Name | Description |
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| ----------- | ----------------------------------------------- |
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| `string` | The string to tokenize. ~~str~~ |
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| **RETURNS** | A container for linguistic annotations. ~~Doc~~ |
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## Tokenizer.pipe {#pipe tag="method"}
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Tokenize a stream of texts.
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> #### Example
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>
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> ```python
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> texts = ["One document.", "...", "Lots of documents"]
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> for doc in tokenizer.pipe(texts, batch_size=50):
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> pass
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> ```
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| Name | Description |
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| ------------ | ------------------------------------------------------------------------------------ |
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| `texts` | A sequence of unicode texts. ~~Iterable[str]~~ |
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| `batch_size` | The number of texts to accumulate in an internal buffer. Defaults to `1000`. ~~int~~ |
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| **YIELDS** | The tokenized `Doc` objects, in order. ~~Doc~~ |
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## Tokenizer.find_infix {#find_infix tag="method"}
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Find internal split points of the string.
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `string` | The string to split. ~~str~~ |
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| **RETURNS** | A list of `re.MatchObject` objects that have `.start()` and `.end()` methods, denoting the placement of internal segment separators, e.g. hyphens. ~~List[Match]~~ |
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## Tokenizer.find_prefix {#find_prefix tag="method"}
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Find the length of a prefix that should be segmented from the string, or `None`
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if no prefix rules match.
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------ |
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| `string` | The string to segment. ~~str~~ |
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| **RETURNS** | The length of the prefix if present, otherwise `None`. ~~Optional[int]~~ |
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## Tokenizer.find_suffix {#find_suffix tag="method"}
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Find the length of a suffix that should be segmented from the string, or `None`
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if no suffix rules match.
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------ |
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| `string` | The string to segment. ~~str~~ |
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| **RETURNS** | The length of the suffix if present, otherwise `None`. ~~Optional[int]~~ |
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## Tokenizer.add_special_case {#add_special_case tag="method"}
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Add a special-case tokenization rule. This mechanism is also used to add custom
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tokenizer exceptions to the language data. See the usage guide on the
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[languages data](/usage/linguistic-features#language-data) and
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[tokenizer special cases](/usage/linguistic-features#special-cases) for more
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details and examples.
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> #### Example
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>
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> ```python
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> from spacy.attrs import ORTH, NORM
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> case = [{ORTH: "do"}, {ORTH: "n't", NORM: "not"}]
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> tokenizer.add_special_case("don't", case)
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> ```
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| Name | Description |
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| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `string` | The string to specially tokenize. ~~str~~ |
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| `token_attrs` | A sequence of dicts, where each dict describes a token and its attributes. The `ORTH` fields of the attributes must exactly match the string when they are concatenated. ~~Iterable[Dict[int, str]]~~ |
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## Tokenizer.explain {#explain tag="method"}
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Tokenize a string with a slow debugging tokenizer that provides information
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about which tokenizer rule or pattern was matched for each token. The tokens
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produced are identical to `Tokenizer.__call__` except for whitespace tokens.
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> #### Example
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>
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> ```python
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> tok_exp = nlp.tokenizer.explain("(don't)")
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> assert [t[0] for t in tok_exp] == ["PREFIX", "SPECIAL-1", "SPECIAL-2", "SUFFIX"]
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> assert [t[1] for t in tok_exp] == ["(", "do", "n't", ")"]
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> ```
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| Name | Description |
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| ----------- | ---------------------------------------------------------------------------- |
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| `string` | The string to tokenize with the debugging tokenizer. ~~str~~ |
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| **RETURNS** | A list of `(pattern_string, token_string)` tuples. ~~List[Tuple[str, str]]~~ |
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## Tokenizer.to_disk {#to_disk tag="method"}
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Serialize the tokenizer to disk.
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> #### Example
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>
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> ```python
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> tokenizer = Tokenizer(nlp.vocab)
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> tokenizer.to_disk("/path/to/tokenizer")
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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## Tokenizer.from_disk {#from_disk tag="method"}
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Load the tokenizer from disk. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> tokenizer = Tokenizer(nlp.vocab)
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> tokenizer.from_disk("/path/to/tokenizer")
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> ```
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| Name | Description |
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| -------------- | ----------------------------------------------------------------------------------------------- |
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| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The modified `Tokenizer` object. ~~Tokenizer~~ |
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## Tokenizer.to_bytes {#to_bytes tag="method"}
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> #### Example
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>
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> ```python
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> tokenizer = tokenizer(nlp.vocab)
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> tokenizer_bytes = tokenizer.to_bytes()
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> ```
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Serialize the tokenizer to a bytestring.
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The serialized form of the `Tokenizer` object. ~~bytes~~ |
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## Tokenizer.from_bytes {#from_bytes tag="method"}
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Load the tokenizer from a bytestring. Modifies the object in place and returns
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it.
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> #### Example
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>
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> ```python
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> tokenizer_bytes = tokenizer.to_bytes()
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> tokenizer = Tokenizer(nlp.vocab)
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> tokenizer.from_bytes(tokenizer_bytes)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| `bytes_data` | The data to load from. ~~bytes~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The `Tokenizer` object. ~~Tokenizer~~ |
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## Attributes {#attributes}
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| Name | Description |
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| ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The vocab object of the parent `Doc`. ~~Vocab~~ |
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| `prefix_search` | A function to find segment boundaries from the start of a string. Returns the length of the segment, or `None`. ~~Optional[Callable[[str], Optional[Match]]]~~ |
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| `suffix_search` | A function to find segment boundaries from the end of a string. Returns the length of the segment, or `None`. ~~Optional[Callable[[str], Optional[Match]]]~~ |
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| `infix_finditer` | A function to find internal segment separators, e.g. hyphens. Returns a (possibly empty) sequence of `re.MatchObject` objects. ~~Optional[Callable[[str], Iterator[Match]]]~~ |
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| `token_match` | A function matching the signature of `re.compile(string).match` to find token matches. Returns an `re.MatchObject` or `None`. ~~Optional[Callable[[str], Optional[Match]]]~~ |
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| `rules` | A dictionary of tokenizer exceptions and special cases. ~~Optional[Dict[str, List[Dict[int, str]]]]~~ |
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## Serialization fields {#serialization-fields}
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During serialization, spaCy will export several data fields used to restore
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different aspects of the object. If needed, you can exclude them from
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serialization by passing in the string names via the `exclude` argument.
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> #### Example
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>
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> ```python
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> data = tokenizer.to_bytes(exclude=["vocab", "exceptions"])
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> tokenizer.from_disk("./data", exclude=["token_match"])
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> ```
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| Name | Description |
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| ---------------- | --------------------------------- |
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| `vocab` | The shared [`Vocab`](/api/vocab). |
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| `prefix_search` | The prefix rules. |
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| `suffix_search` | The suffix rules. |
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| `infix_finditer` | The infix rules. |
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| `token_match` | The token match expression. |
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| `exceptions` | The tokenizer exception rules. |
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