Tokenizer

Default config

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[nlp.tokenizer]
@tokenizers = "spacy.Tokenizer.v1"

Segment text, and create Doc objects with the discovered segment boundaries. For a deeper understanding, see the docs on how spaCy's tokenizer works. The tokenizer is typically created automatically when a Language subclass is initialized and it reads its settings like punctuation and special case rules from the Language.Defaults provided by the language subclass.

Tokenizer.__init__ {id="init",tag="method"}

Create a Tokenizer to create Doc objects given unicode text. For examples of how to construct a custom tokenizer with different tokenization rules, see the usage documentation.

Example

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# Construction 1
from spacy.tokenizer import Tokenizer
from spacy.lang.en import English
nlp = English()
# Create a blank Tokenizer with just the English vocab
tokenizer = Tokenizer(nlp.vocab)

# Construction 2
from spacy.lang.en import English
nlp = English()
# Create a Tokenizer with the default settings for English
# including punctuation rules and exceptions
tokenizer = nlp.tokenizer

Name	Description
vocab	A storage container for lexical types. Vocab
rules	Exceptions and special-cases for the tokenizer. Optional[Dict[str, List[Dict[int, str]]]]
prefix_search	A function matching the signature of re.compile(string).search to match prefixes. Optional[Callable[[str], Optional[Match]]]
suffix_search	A function matching the signature of re.compile(string).search to match suffixes. Optional[Callable[[str], Optional[Match]]]
infix_finditer	A function matching the signature of re.compile(string).finditer to find infixes. Optional[Callable[[str], Iterator[Match]]]
token_match	A function matching the signature of re.compile(string).match to find token matches. Optional[Callable[[str], Optional[Match]]]
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]]]
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

Tokenizer.__call__ {id="call",tag="method"}

Tokenize a string.

Example

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tokens = tokenizer("This is a sentence")
assert len(tokens) == 4

Name	Description
string	The string to tokenize. str
RETURNS	A container for linguistic annotations. Doc

Tokenizer.pipe {id="pipe",tag="method"}

Tokenize a stream of texts.

Example

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texts = ["One document.", "...", "Lots of documents"]
for doc in tokenizer.pipe(texts, batch_size=50):
    pass

Name	Description
texts	A sequence of unicode texts. Iterable[str]
batch_size	The number of texts to accumulate in an internal buffer. Defaults to 1000. int
YIELDS	The tokenized Doc objects, in order. Doc

Tokenizer.find_infix {id="find_infix",tag="method"}

Find internal split points of the string.

Name	Description
string	The string to split. str
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]

Tokenizer.find_prefix {id="find_prefix",tag="method"}

Find the length of a prefix that should be segmented from the string, or None if no prefix rules match.

Name	Description
string	The string to segment. str
RETURNS	The length of the prefix if present, otherwise None. Optional[int]

Tokenizer.find_suffix {id="find_suffix",tag="method"}

Find the length of a suffix that should be segmented from the string, or None if no suffix rules match.

Name	Description
string	The string to segment. str
RETURNS	The length of the suffix if present, otherwise None. Optional[int]

Tokenizer.add_special_case {id="add_special_case",tag="method"}

Add a special-case tokenization rule. This mechanism is also used to add custom tokenizer exceptions to the language data. See the usage guide on the languages data and tokenizer special cases for more details and examples.

Example

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from spacy.attrs import ORTH, NORM
case = [{ORTH: "do"}, {ORTH: "n't", NORM: "not"}]
tokenizer.add_special_case("don't", case)

Name	Description
string	The string to specially tokenize. str
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]]

Tokenizer.explain {id="explain",tag="method"}

Tokenize a string with a slow debugging tokenizer that provides information about which tokenizer rule or pattern was matched for each token. The tokens produced are identical to Tokenizer.__call__ except for whitespace tokens.

Example

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tok_exp = nlp.tokenizer.explain("(don't)")
assert [t[0] for t in tok_exp] == ["PREFIX", "SPECIAL-1", "SPECIAL-2", "SUFFIX"]
assert [t[1] for t in tok_exp] == ["(", "do", "n't", ")"]

Name	Description
string	The string to tokenize with the debugging tokenizer. str
RETURNS	A list of (pattern_string, token_string) tuples. List[Tuple[str, str]]

Tokenizer.to_disk {id="to_disk",tag="method"}

Serialize the tokenizer to disk.

Example

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tokenizer = Tokenizer(nlp.vocab)
tokenizer.to_disk("/path/to/tokenizer")

Name	Description
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]
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]

Tokenizer.from_disk {id="from_disk",tag="method"}

Load the tokenizer from disk. Modifies the object in place and returns it.

Example

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tokenizer = Tokenizer(nlp.vocab)
tokenizer.from_disk("/path/to/tokenizer")

Name	Description
path	A path to a directory. Paths may be either strings or Path-like objects. Union[str, Path]
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The modified Tokenizer object. Tokenizer

Tokenizer.to_bytes {id="to_bytes",tag="method"}

Example

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tokenizer = tokenizer(nlp.vocab)
tokenizer_bytes = tokenizer.to_bytes()

Serialize the tokenizer to a bytestring.

Name	Description
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The serialized form of the Tokenizer object. bytes

Tokenizer.from_bytes {id="from_bytes",tag="method"}

Load the tokenizer from a bytestring. Modifies the object in place and returns it.

Example

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tokenizer_bytes = tokenizer.to_bytes()
tokenizer = Tokenizer(nlp.vocab)
tokenizer.from_bytes(tokenizer_bytes)

Name	Description
bytes_data	The data to load from. bytes
keyword-only
exclude	String names of serialization fields to exclude. Iterable[str]
RETURNS	The Tokenizer object. Tokenizer

Attributes {id="attributes"}

Name	Description
vocab	The vocab object of the parent Doc. Vocab
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]]]
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]]]
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]]]
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]]]
rules	A dictionary of tokenizer exceptions and special cases. Optional[Dict[str, List[Dict[int, str]]]]

Serialization fields {id="serialization-fields"}

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

Example

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data = tokenizer.to_bytes(exclude=["vocab", "exceptions"])
tokenizer.from_disk("./data", exclude=["token_match"])

Name	Description
vocab	The shared Vocab.
prefix_search	The prefix rules.
suffix_search	The suffix rules.
infix_finditer	The infix rules.
token_match	The token match expression.
exceptions	The tokenizer exception rules.