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---
title: Tokenizer
teaser: Segment text into words, punctuations marks, etc.
tag: class
source: spacy/tokenizer.pyx
---
> #### Default config
>
> ```ini
> [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](/usage/linguistic-features#how-tokenizer-works).
The tokenizer is typically created automatically when a
[`Language`](/api/language) subclass is initialized and it reads its settings
like punctuation and special case rules from the
[`Language.Defaults`](/api/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](https://spacy.io/usage/linguistic-features#native-tokenizers).
> #### Example
>
> ```python
> # 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
>
> ```python
> 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
>
> ```python
> 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](/usage/linguistic-features#language-data) and
[tokenizer special cases](/usage/linguistic-features#special-cases) for more
details and examples.
> #### Example
>
> ```python
> 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
>
> ```python
> 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
>
> ```python
> 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](#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
>
> ```python
> 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](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `Tokenizer` object. ~~Tokenizer~~ |
## Tokenizer.to_bytes {id="to_bytes",tag="method"}
> #### Example
>
> ```python
> 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](#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
>
> ```python
> 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](#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
>
> ```python
> data = tokenizer.to_bytes(exclude=["vocab", "exceptions"])
> tokenizer.from_disk("./data", exclude=["token_match"])
> ```
| Name | Description |
| ---------------- | --------------------------------- |
| `vocab` | The shared [`Vocab`](/api/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. |