spaCy/website/docs/api/tokenizer.md

title	teaser	tag	source
Tokenizer	Segment text into words, punctuations marks etc.	class	spacy/tokenizer.pyx

Segment text, and create Doc objects with the discovered segment boundaries.

Tokenizer.__init__

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

# 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.Defaults.create_tokenizer(nlp)

Name	Type	Description
vocab	Vocab	A storage container for lexical types.
rules	dict	Exceptions and special-cases for the tokenizer.
prefix_search	callable	A function matching the signature of re.compile(string).search to match prefixes.
suffix_search	callable	A function matching the signature of re.compile(string).search to match suffixes.
infix_finditer	callable	A function matching the signature of re.compile(string).finditer to find infixes.
token_match	callable	A boolean function matching strings to be recognized as tokens.
RETURNS	Tokenizer	The newly constructed object.

Tokenizer.__call__

Tokenize a string.

Example

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

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

Tokenizer.pipe

Tokenize a stream of texts.

Example

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

Name	Type	Description
texts	-	A sequence of unicode texts.
batch_size	int	The number of texts to accumulate in an internal buffer. Defaults to 1000.
YIELDS	Doc	A sequence of Doc objects, in order.

Tokenizer.find_infix

Find internal split points of the string.

Name	Type	Description
string	unicode	The string to split.
RETURNS	list	A list of re.MatchObject objects that have .start() and .end() methods, denoting the placement of internal segment separators, e.g. hyphens.

Tokenizer.find_prefix

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

Name	Type	Description
string	unicode	The string to segment.
RETURNS	int	The length of the prefix if present, otherwise None.

Tokenizer.find_suffix

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

Name	Type	Description
string	unicode	The string to segment.
RETURNS	int / None	The length of the suffix if present, otherwise None.

Tokenizer.add_special_case

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 adding languages for more details and examples.

Example

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

Name	Type	Description
string	unicode	The string to specially tokenize.
token_attrs	iterable	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.

Tokenizer.to_disk

Serialize the tokenizer to disk.

Example

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

Name	Type	Description
path	unicode / Path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.
exclude	list	String names of serialization fields to exclude.

Tokenizer.from_disk

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

Example

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

Name	Type	Description
path	unicode / Path	A path to a directory. Paths may be either strings or Path-like objects.
exclude	list	String names of serialization fields to exclude.
RETURNS	Tokenizer	The modified Tokenizer object.

Tokenizer.to_bytes

Example

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

Serialize the tokenizer to a bytestring.

Name	Type	Description
exclude	list	String names of serialization fields to exclude.
RETURNS	bytes	The serialized form of the Tokenizer object.

Tokenizer.from_bytes

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

Example

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

Name	Type	Description
bytes_data	bytes	The data to load from.
exclude	list	String names of serialization fields to exclude.
RETURNS	Tokenizer	The Tokenizer object.

Attributes

Name	Type	Description
vocab	Vocab	The vocab object of the parent Doc.
prefix_search	-	A function to find segment boundaries from the start of a string. Returns the length of the segment, or None.
suffix_search	-	A function to find segment boundaries from the end of a string. Returns the length of the segment, or None.
infix_finditer	-	A function to find internal segment separators, e.g. hyphens. Returns a (possibly empty) list of re.MatchObject objects.

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

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.