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---
title: Doc
tag: class
teaser: A container for accessing linguistic annotations.
source: spacy/tokens/doc.pyx
---
A `Doc` is a sequence of [`Token`](/api/token) objects. Access sentences and
named entities, export annotations to numpy arrays, losslessly serialize to
compressed binary strings. The `Doc` object holds an array of
[`TokenC`](/api/cython-structs#tokenc) structs. The Python-level `Token` and
[`Span`](/api/span) objects are views of this array, i.e. they don't own the
data themselves.
## Doc.\_\_init\_\_ {#init tag="method"}
Construct a `Doc` object. The most common way to get a `Doc` object is via the
`nlp` object.
> #### Example
>
> ```python
> # Construction 1
> doc = nlp("Some text")
>
> # Construction 2
> from spacy.tokens import Doc
>
> words = ["hello", "world", "!"]
> spaces = [True, False, False]
> doc = Doc(nlp.vocab, words=words, spaces=spaces)
> ```
| Name | Description |
| ---------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `vocab` | A storage container for lexical types. ~~Vocab~~ |
| `words` | A list of strings or integer hash values to add to the document as words. ~~Optional[List[Union[str,int]]]~~ |
| `spaces` | A list of boolean values indicating whether each word has a subsequent space. Must have the same length as `words`, if specified. Defaults to a sequence of `True`. ~~Optional[List[bool]]~~ |
| _keyword-only_ | |
| `user\_data` | Optional extra data to attach to the Doc. ~~Dict~~ |
| `tags` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.tag` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
| `pos` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.pos` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
| `morphs` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.morph` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
| `lemmas` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.lemma` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
| `heads` <Tag variant="new">3</Tag> | A list of values, of the same length as `words`, to assign as the head for each word. Head indices are the absolute position of the head in the `Doc`. Defaults to `None`. ~~Optional[List[int]]~~ |
| `deps` <Tag variant="new">3</Tag> | A list of strings, of the same length as `words`, to assign as `token.dep` for each word. Defaults to `None`. ~~Optional[List[str]]~~ |
| `sent_starts` <Tag variant="new">3</Tag> | A list of values, of the same length as `words`, to assign as `token.is_sent_start`. Will be overridden by heads if `heads` is provided. Defaults to `None`. ~~Optional[List[Optional[bool]]]~~ |
| `ents` <Tag variant="new">3</Tag> | A list of strings, of the same length of `words`, to assign the token-based IOB tag. Defaults to `None`. ~~Optional[List[str]]~~ |
## Doc.\_\_getitem\_\_ {#getitem tag="method"}
Get a [`Token`](/api/token) object at position `i`, where `i` is an integer.
Negative indexing is supported, and follows the usual Python semantics, i.e.
`doc[-2]` is `doc[len(doc) - 2]`.
> #### Example
>
> ```python
> doc = nlp("Give it back! He pleaded.")
> assert doc[0].text == "Give"
> assert doc[-1].text == "."
> span = doc[1:3]
> assert span.text == "it back"
> ```
| Name | Description |
| ----------- | -------------------------------- |
| `i` | The index of the token. ~~int~~ |
| **RETURNS** | The token at `doc[i]`. ~~Token~~ |
Get a [`Span`](/api/span) object, starting at position `start` (token index) and
ending at position `end` (token index). For instance, `doc[2:5]` produces a span
consisting of tokens 2, 3 and 4. Stepped slices (e.g. `doc[start : end : step]`)
are not supported, as `Span` objects must be contiguous (cannot have gaps). You
can use negative indices and open-ended ranges, which have their normal Python
semantics.
| Name | Description |
| ----------- | ----------------------------------------------------- |
| `start_end` | The slice of the document to get. ~~Tuple[int, int]~~ |
| **RETURNS** | The span at `doc[start:end]`. ~~Span~~ |
## Doc.\_\_iter\_\_ {#iter tag="method"}
Iterate over `Token` objects, from which the annotations can be easily accessed.
> #### Example
>
> ```python
> doc = nlp("Give it back")
> assert [t.text for t in doc] == ["Give", "it", "back"]
> ```
This is the main way of accessing [`Token`](/api/token) objects, which are the
main way annotations are accessed from Python. If faster-than-Python speeds are
required, you can instead access the annotations as a numpy array, or access the
underlying C data directly from Cython.
| Name | Description |
| ---------- | --------------------------- |
| **YIELDS** | A `Token` object. ~~Token~~ |
## Doc.\_\_len\_\_ {#len tag="method"}
Get the number of tokens in the document.
> #### Example
>
> ```python
> doc = nlp("Give it back! He pleaded.")
> assert len(doc) == 7
> ```
| Name | Description |
| ----------- | --------------------------------------------- |
| **RETURNS** | The number of tokens in the document. ~~int~~ |
## Doc.set_extension {#set_extension tag="classmethod" new="2"}
Define a custom attribute on the `Doc` which becomes available via `Doc._`. For
details, see the documentation on
[custom attributes](/usage/processing-pipelines#custom-components-attributes).
> #### Example
>
> ```python
> from spacy.tokens import Doc
> city_getter = lambda doc: any(city in doc.text for city in ("New York", "Paris", "Berlin"))
> Doc.set_extension("has_city", getter=city_getter)
> doc = nlp("I like New York")
> assert doc._.has_city
> ```
| Name | Description |
| --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the attribute to set by the extension. For example, `"my_attr"` will be available as `doc._.my_attr`. ~~str~~ |
| `default` | Optional default value of the attribute if no getter or method is defined. ~~Optional[Any]~~ |
| `method` | Set a custom method on the object, for example `doc._.compare(other_doc)`. ~~Optional[Callable[[Doc, ...], Any]]~~ |
| `getter` | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute. ~~Optional[Callable[[Doc], Any]]~~ |
| `setter` | Setter function that takes the `Doc` and a value, and modifies the object. Is called when the user writes to the `Doc._` attribute. ~~Optional[Callable[[Doc, Any], None]]~~ |
| `force` | Force overwriting existing attribute. ~~bool~~ |
## Doc.get_extension {#get_extension tag="classmethod" new="2"}
Look up a previously registered extension by name. Returns a 4-tuple
`(default, method, getter, setter)` if the extension is registered. Raises a
`KeyError` otherwise.
> #### Example
>
> ```python
> from spacy.tokens import Doc
> Doc.set_extension("has_city", default=False)
> extension = Doc.get_extension("has_city")
> assert extension == (False, None, None, None)
> ```
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the extension. ~~str~~ |
| **RETURNS** | A `(default, method, getter, setter)` tuple of the extension. ~~Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]~~ |
## Doc.has_extension {#has_extension tag="classmethod" new="2"}
Check whether an extension has been registered on the `Doc` class.
> #### Example
>
> ```python
> from spacy.tokens import Doc
> Doc.set_extension("has_city", default=False)
> assert Doc.has_extension("has_city")
> ```
| Name | Description |
| ----------- | --------------------------------------------------- |
| `name` | Name of the extension to check. ~~str~~ |
| **RETURNS** | Whether the extension has been registered. ~~bool~~ |
## Doc.remove_extension {#remove_extension tag="classmethod" new="2.0.12"}
Remove a previously registered extension.
> #### Example
>
> ```python
> from spacy.tokens import Doc
> Doc.set_extension("has_city", default=False)
> removed = Doc.remove_extension("has_city")
> assert not Doc.has_extension("has_city")
> ```
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | Name of the extension. ~~str~~ |
| **RETURNS** | A `(default, method, getter, setter)` tuple of the removed extension. ~~Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]~~ |
## Doc.char_span {#char_span tag="method" new="2"}
Create a `Span` object from the slice `doc.text[start_idx:end_idx]`. Returns
`None` if the character indices don't map to a valid span using the default
alignment mode `"strict".
> #### Example
>
> ```python
> doc = nlp("I like New York")
> span = doc.char_span(7, 15, label="GPE")
> assert span.text == "New York"
> ```
| Name | Description |
| ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `start` | The index of the first character of the span. ~~int~~ |
| `end` | The index of the last character after the span. ~~int~~ |
| `label` | A label to attach to the span, e.g. for named entities. ~~Union[int, str]~~ |
| `kb_id` <Tag variant="new">2.2</Tag> | An ID from a knowledge base to capture the meaning of a named entity. ~~Union[int, str]~~ |
| `vector` | A meaning representation of the span. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
| `alignment_mode` | How character indices snap to token boundaries. Options: `"strict"` (no snapping), `"contract"` (span of all tokens completely within the character span), `"expand"` (span of all tokens at least partially covered by the character span). Defaults to `"strict"`. ~~str~~ |
| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ |
## Doc.set_ents {#set_ents tag="method" new="3"}
Set the named entities in the document.
> #### Example
>
> ```python
> from spacy.tokens import Span
> doc = nlp("Mr. Best flew to New York on Saturday morning.")
> doc.set_ents([Span(doc, 0, 2, "PERSON")])
> ents = list(doc.ents)
> assert ents[0].label_ == "PERSON"
> assert ents[0].text == "Mr. Best"
> ```
| Name | Description |
| -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `entities` | Spans with labels to set as entities. ~~List[Span]~~ |
| _keyword-only_ | |
| `blocked` | Spans to set as "blocked" (never an entity) for spacy's built-in NER component. Other components may ignore this setting. ~~Optional[List[Span]]~~ |
| `missing` | Spans with missing/unknown entity information. ~~Optional[List[Span]]~~ |
| `outside` | Spans outside of entities (O in IOB). ~~Optional[List[Span]]~~ |
| `default` | How to set entity annotation for tokens outside of any provided spans. Options: `"blocked"`, `"missing"`, `"outside"` and `"unmodified"` (preserve current state). Defaults to `"outside"`. ~~str~~ |
## Doc.similarity {#similarity tag="method" model="vectors"}
Make a semantic similarity estimate. The default estimate is cosine similarity
using an average of word vectors.
> #### Example
>
> ```python
> apples = nlp("I like apples")
> oranges = nlp("I like oranges")
> apples_oranges = apples.similarity(oranges)
> oranges_apples = oranges.similarity(apples)
> assert apples_oranges == oranges_apples
> ```
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------------------------------------- |
| `other` | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. ~~Union[Doc, Span, Token, Lexeme]~~ |
| **RETURNS** | A scalar similarity score. Higher is more similar. ~~float~~ |
## Doc.count_by {#count_by tag="method"}
Count the frequencies of a given attribute. Produces a dict of
`{attr (int): count (ints)}` frequencies, keyed by the values of the given
attribute ID.
> #### Example
>
> ```python
> from spacy.attrs import ORTH
> doc = nlp("apple apple orange banana")
> assert doc.count_by(ORTH) == {7024: 1, 119552: 1, 2087: 2}
> doc.to_array([ORTH])
> # array([[11880], [11880], [7561], [12800]])
> ```
| Name | Description |
| ----------- | --------------------------------------------------------------------- |
| `attr_id` | The attribute ID. ~~int~~ |
| **RETURNS** | A dictionary mapping attributes to integer counts. ~~Dict[int, int]~~ |
## Doc.get_lca_matrix {#get_lca_matrix tag="method"}
Calculates the lowest common ancestor matrix for a given `Doc`. Returns LCA
matrix containing the integer index of the ancestor, or `-1` if no common
ancestor is found, e.g. if span excludes a necessary ancestor.
> #### Example
>
> ```python
> doc = nlp("This is a test")
> matrix = doc.get_lca_matrix()
> # array([[0, 1, 1, 1], [1, 1, 1, 1], [1, 1, 2, 3], [1, 1, 3, 3]], dtype=int32)
> ```
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------- |
| **RETURNS** | The lowest common ancestor matrix of the `Doc`. ~~numpy.ndarray[ndim=2, dtype=int32]~~ |
## Doc.has_annotation {#has_annotation tag="method"}
Check whether the doc contains annotation on a token attribute.
<Infobox title="Changed in v3.0" variant="warning">
This method replaces the previous boolean attributes like `Doc.is_tagged`,
`Doc.is_parsed` or `Doc.is_sentenced`.
```diff
doc = nlp("This is a text")
- assert doc.is_parsed
+ assert doc.has_annotation("DEP")
```
</Infobox>
| Name | Description |
| ------------------ | --------------------------------------------------------------------------------------------------- |
| `attr` | The attribute string name or int ID. ~~Union[int, str]~~ |
| _keyword-only_ | |
| `require_complete` | Whether to check that the attribute is set on every token in the doc. Defaults to `False`. ~~bool~~ |
| **RETURNS** | Whether specified annotation is present in the doc. ~~bool~~ |
## Doc.to_array {#to_array tag="method"}
Export given token attributes to a numpy `ndarray`. If `attr_ids` is a sequence
of `M` attributes, the output array will be of shape `(N, M)`, where `N` is the
length of the `Doc` (in tokens). If `attr_ids` is a single attribute, the output
shape will be `(N,)`. You can specify attributes by integer ID (e.g.
`spacy.attrs.LEMMA`) or string name (e.g. "LEMMA" or "lemma"). The values will
be 64-bit integers.
Returns a 2D array with one row per token and one column per attribute (when
`attr_ids` is a list), or as a 1D numpy array, with one item per attribute (when
`attr_ids` is a single value).
> #### Example
>
> ```python
> from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
> doc = nlp(text)
> # All strings mapped to integers, for easy export to numpy
> np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
> np_array = doc.to_array("POS")
> ```
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| `attr_ids` | A list of attributes (int IDs or string names) or a single attribute (int ID or string name). ~~Union[int, str, List[Union[int, str]]]~~ |
| **RETURNS** | The exported attributes as a numpy array. ~~Union[numpy.ndarray[ndim=2, dtype=uint64], numpy.ndarray[ndim=1, dtype=uint64]]~~ |
## Doc.from_array {#from_array tag="method"}
Load attributes from a numpy array. Write to a `Doc` object, from an `(M, N)`
array of attributes.
> #### Example
>
> ```python
> from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
> from spacy.tokens import Doc
> doc = nlp("Hello world!")
> np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
> doc2 = Doc(doc.vocab, words=[t.text for t in doc])
> doc2.from_array([LOWER, POS, ENT_TYPE, IS_ALPHA], np_array)
> assert doc[0].pos_ == doc2[0].pos_
> ```
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------- |
| `attrs` | A list of attribute ID ints. ~~List[int]~~ |
| `array` | The attribute values to load. ~~numpy.ndarray[ndim=2, dtype=int32]~~ |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Doc` itself. ~~Doc~~ |
## Doc.from_docs {#from_docs tag="staticmethod" new="3"}
Concatenate multiple `Doc` objects to form a new one. Raises an error if the
`Doc` objects do not all share the same `Vocab`.
> #### Example
>
> ```python
> from spacy.tokens import Doc
> texts = ["London is the capital of the United Kingdom.",
> "The River Thames flows through London.",
> "The famous Tower Bridge crosses the River Thames."]
> docs = list(nlp.pipe(texts))
> c_doc = Doc.from_docs(docs)
> assert str(c_doc) == " ".join(texts)
> assert len(list(c_doc.sents)) == len(docs)
> assert [str(ent) for ent in c_doc.ents] == \
> [str(ent) for doc in docs for ent in doc.ents]
> ```
| Name | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------- |
| `docs` | A list of `Doc` objects. ~~List[Doc]~~ |
| `ensure_whitespace` | Insert a space between two adjacent docs whenever the first doc does not end in whitespace. ~~bool~~ |
| `attrs` | Optional list of attribute ID ints or attribute name strings. ~~Optional[List[Union[str, int]]]~~ |
| **RETURNS** | The new `Doc` object that is containing the other docs or `None`, if `docs` is empty or `None`. ~~Optional[Doc]~~ |
## Doc.to_disk {#to_disk tag="method" new="2"}
Save the current state to a directory.
> #### Example
>
> ```python
> doc.to_disk("/path/to/doc")
> ```
| 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]~~ |
## Doc.from_disk {#from_disk tag="method" new="2"}
Loads state from a directory. Modifies the object in place and returns it.
> #### Example
>
> ```python
> from spacy.tokens import Doc
> from spacy.vocab import Vocab
> doc = Doc(Vocab()).from_disk("/path/to/doc")
> ```
| 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 `Doc` object. ~~Doc~~ |
## Doc.to_bytes {#to_bytes tag="method"}
Serialize, i.e. export the document contents to a binary string.
> #### Example
>
> ```python
> doc = nlp("Give it back! He pleaded.")
> doc_bytes = doc.to_bytes()
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | A losslessly serialized copy of the `Doc`, including all annotations. ~~bytes~~ |
## Doc.from_bytes {#from_bytes tag="method"}
Deserialize, i.e. import the document contents from a binary string.
> #### Example
>
> ```python
> from spacy.tokens import Doc
> doc = nlp("Give it back! He pleaded.")
> doc_bytes = doc.to_bytes()
> doc2 = Doc(doc.vocab).from_bytes(doc_bytes)
> assert doc.text == doc2.text
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `data` | The string to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `Doc` object. ~~Doc~~ |
## Doc.retokenize {#retokenize tag="contextmanager" new="2.1"}
Context manager to handle retokenization of the `Doc`. Modifications to the
`Doc`'s tokenization are stored, and then made all at once when the context
manager exits. This is much more efficient, and less error-prone. All views of
the `Doc` (`Span` and `Token`) created before the retokenization are
invalidated, although they may accidentally continue to work.
> #### Example
>
> ```python
> doc = nlp("Hello world!")
> with doc.retokenize() as retokenizer:
> retokenizer.merge(doc[0:2])
> ```
| Name | Description |
| ----------- | -------------------------------- |
| **RETURNS** | The retokenizer. ~~Retokenizer~~ |
### Retokenizer.merge {#retokenizer.merge tag="method"}
Mark a span for merging. The `attrs` will be applied to the resulting token (if
they're context-dependent token attributes like `LEMMA` or `DEP`) or to the
underlying lexeme (if they're context-independent lexical attributes like
`LOWER` or `IS_STOP`). Writable custom extension attributes can be provided
using the `"_"` key and specifying a dictionary that maps attribute names to
values.
> #### Example
>
> ```python
> doc = nlp("I like David Bowie")
> with doc.retokenize() as retokenizer:
> attrs = {"LEMMA": "David Bowie"}
> retokenizer.merge(doc[2:4], attrs=attrs)
> ```
| Name | Description |
| ------- | --------------------------------------------------------------------- |
| `span` | The span to merge. ~~Span~~ |
| `attrs` | Attributes to set on the merged token. ~~Dict[Union[str, int], Any]~~ |
### Retokenizer.split {#retokenizer.split tag="method"}
Mark a token for splitting, into the specified `orths`. The `heads` are required
to specify how the new subtokens should be integrated into the dependency tree.
The list of per-token heads can either be a token in the original document, e.g.
`doc[2]`, or a tuple consisting of the token in the original document and its
subtoken index. For example, `(doc[3], 1)` will attach the subtoken to the
second subtoken of `doc[3]`.
This mechanism allows attaching subtokens to other newly created subtokens,
without having to keep track of the changing token indices. If the specified
head token will be split within the retokenizer block and no subtoken index is
specified, it will default to `0`. Attributes to set on subtokens can be
provided as a list of values. They'll be applied to the resulting token (if
they're context-dependent token attributes like `LEMMA` or `DEP`) or to the
underlying lexeme (if they're context-independent lexical attributes like
`LOWER` or `IS_STOP`).
> #### Example
>
> ```python
> doc = nlp("I live in NewYork")
> with doc.retokenize() as retokenizer:
> heads = [(doc[3], 1), doc[2]]
> attrs = {"POS": ["PROPN", "PROPN"],
> "DEP": ["pobj", "compound"]}
> retokenizer.split(doc[3], ["New", "York"], heads=heads, attrs=attrs)
> ```
| Name | Description |
| ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
| `token` | The token to split. ~~Token~~ |
| `orths` | The verbatim text of the split tokens. Needs to match the text of the original token. ~~List[str]~~ |
| `heads` | List of `token` or `(token, subtoken)` tuples specifying the tokens to attach the newly split subtokens to. ~~List[Union[Token, Tuple[Token, int]]]~~ |
| `attrs` | Attributes to set on all split tokens. Attribute names mapped to list of per-token attribute values. ~~Dict[Union[str, int], List[Any]]~~ |
## Doc.ents {#ents tag="property" model="NER"}
The named entities in the document. Returns a tuple of named entity `Span`
objects, if the entity recognizer has been applied.
> #### Example
>
> ```python
> doc = nlp("Mr. Best flew to New York on Saturday morning.")
> ents = list(doc.ents)
> assert ents[0].label_ == "PERSON"
> assert ents[0].text == "Mr. Best"
> ```
| Name | Description |
| ----------- | ---------------------------------------------------------------- |
| **RETURNS** | Entities in the document, one `Span` per entity. ~~Tuple[Span]~~ |
## Doc.spans {#spans tag="property"}
A dictionary of named span groups, to store and access additional span
annotations. You can write to it by assigning a list of [`Span`](/api/span)
objects or a [`SpanGroup`](/api/spangroup) to a given key.
> #### Example
>
> ```python
> doc = nlp("Their goi ng home")
> doc.spans["errors"] = [doc[0:1], doc[2:4]]
> ```
| Name | Description |
| ----------- | ------------------------------------------------------------------ |
| **RETURNS** | The span groups assigned to the document. ~~Dict[str, SpanGroup]~~ |
## Doc.cats {#cats tag="property" model="text classifier"}
Maps a label to a score for categories applied to the document. Typically set by
the [`TextCategorizer`](/api/textcategorizer).
> #### Example
>
> ```python
> doc = nlp("This is a text about football.")
> print(doc.cats)
> ```
| Name | Description |
| ----------- | ---------------------------------------------------------- |
| **RETURNS** | The text categories mapped to scores. ~~Dict[str, float]~~ |
## Doc.noun_chunks {#noun_chunks tag="property" model="parser"}
Iterate over the base noun phrases in the document. Yields base noun-phrase
`Span` objects, if the document has been syntactically parsed. A base noun
phrase, or "NP chunk", is a noun phrase that does not permit other NPs to be
nested within it – so no NP-level coordination, no prepositional phrases, and no
relative clauses.
To customize the noun chunk iterator in a loaded pipeline, modify
[`nlp.vocab.get_noun_chunks`](/api/vocab#attributes). If the `noun_chunk`
[syntax iterator](/usage/adding-languages#language-data) has not been
implemented for the given language, a `NotImplementedError` is raised.
> #### Example
>
> ```python
> doc = nlp("A phrase with another phrase occurs.")
> chunks = list(doc.noun_chunks)
> assert len(chunks) == 2
> assert chunks[0].text == "A phrase"
> assert chunks[1].text == "another phrase"
> ```
| Name | Description |
| ---------- | ------------------------------------- |
| **YIELDS** | Noun chunks in the document. ~~Span~~ |
## Doc.sents {#sents tag="property" model="sentences"}
Iterate over the sentences in the document. Sentence spans have no label.
This property is only available when
[sentence boundaries](/usage/linguistic-features#sbd) have been set on the
document by the `parser`, `senter`, `sentencizer` or some custom function. It
will raise an error otherwise.
> #### Example
>
> ```python
> doc = nlp("This is a sentence. Here's another...")
> sents = list(doc.sents)
> assert len(sents) == 2
> assert [s.root.text for s in sents] == ["is", "'s"]
> ```
| Name | Description |
| ---------- | ----------------------------------- |
| **YIELDS** | Sentences in the document. ~~Span~~ |
## Doc.has_vector {#has_vector tag="property" model="vectors"}
A boolean value indicating whether a word vector is associated with the object.
> #### Example
>
> ```python
> doc = nlp("I like apples")
> assert doc.has_vector
> ```
| Name | Description |
| ----------- | --------------------------------------------------------- |
| **RETURNS** | Whether the document has a vector data attached. ~~bool~~ |
## Doc.vector {#vector tag="property" model="vectors"}
A real-valued meaning representation. Defaults to an average of the token
vectors.
> #### Example
>
> ```python
> doc = nlp("I like apples")
> assert doc.vector.dtype == "float32"
> assert doc.vector.shape == (300,)
> ```
| Name | Description |
| ----------- | -------------------------------------------------------------------------------------------------- |
| **RETURNS** | A 1-dimensional array representing the document's vector. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Doc.vector_norm {#vector_norm tag="property" model="vectors"}
The L2 norm of the document's vector representation.
> #### Example
>
> ```python
> doc1 = nlp("I like apples")
> doc2 = nlp("I like oranges")
> doc1.vector_norm # 4.54232424414368
> doc2.vector_norm # 3.304373298575751
> assert doc1.vector_norm != doc2.vector_norm
> ```
| Name | Description |
| ----------- | --------------------------------------------------- |
| **RETURNS** | The L2 norm of the vector representation. ~~float~~ |
## Attributes {#attributes}
| Name | Description |
| ------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------- |
| `text` | A string representation of the document text. ~~str~~ |
| `text_with_ws` | An alias of `Doc.text`, provided for duck-type compatibility with `Span` and `Token`. ~~str~~ |
| `mem` | The document's local memory heap, for all C data it owns. ~~cymem.Pool~~ |
| `vocab` | The store of lexical types. ~~Vocab~~ |
| `tensor` <Tag variant="new">2</Tag> | Container for dense vector representations. ~~numpy.ndarray~~ |
| `user_data` | A generic storage area, for user custom data. ~~Dict[str, Any]~~ |
| `lang` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~int~~ |
| `lang_` <Tag variant="new">2.1</Tag> | Language of the document's vocabulary. ~~str~~ |
| `sentiment` | The document's positivity/negativity score, if available. ~~float~~ |
| `user_hooks` | A dictionary that allows customization of the `Doc`'s properties. ~~Dict[str, Callable]~~ |
| `user_token_hooks` | A dictionary that allows customization of properties of `Token` children. ~~Dict[str, Callable]~~ |
| `user_span_hooks` | A dictionary that allows customization of properties of `Span` children. ~~Dict[str, Callable]~~ |
| `has_unknown_spaces` | Whether the document was constructed without known spacing between tokens (typically when created from gold tokenization). ~~bool~~ |
| `_` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). ~~Underscore~~ |
## Serialization fields {#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 = doc.to_bytes(exclude=["text", "tensor"])
> doc.from_disk("./doc.bin", exclude=["user_data"])
> ```
| Name | Description |
| ------------------ | --------------------------------------------- |
| `text` | The value of the `Doc.text` attribute. |
| `sentiment` | The value of the `Doc.sentiment` attribute. |
| `tensor` | The value of the `Doc.tensor` attribute. |
| `user_data` | The value of the `Doc.user_data` dictionary. |
| `user_data_keys` | The keys of the `Doc.user_data` dictionary. |
| `user_data_values` | The values of the `Doc.user_data` dictionary. |
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