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<!--- Provide a general summary of your changes in the title. --> ## Description The new website is implemented using [Gatsby](https://www.gatsbyjs.org) with [Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This allows authoring content in **straightforward Markdown** without the usual limitations. Standard elements can be overwritten with powerful [React](http://reactjs.org/) components and wherever Markdown syntax isn't enough, JSX components can be used. Hopefully, this update will also make it much easier to contribute to the docs. Once this PR is merged, I'll implement auto-deployment via [Netlify](https://netlify.com) on a specific branch (to avoid building the website on every PR). There's a bunch of other cool stuff that the new setup will allow us to do – including writing front-end tests, service workers, offline support, implementing a search and so on. This PR also includes various new docs pages and content. Resolves #3270. Resolves #3222. Resolves #2947. Resolves #2837. ### Types of change enhancement ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
468 lines
19 KiB
Markdown
468 lines
19 KiB
Markdown
---
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title: Span
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tag: class
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source: spacy/tokens/span.pyx
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---
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A slice from a [`Doc`](/api/doc) object.
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## Span.\_\_init\_\_ {#init tag="method"}
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Create a Span object from the `slice doc[start : end]`.
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> #### Example
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>
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> ```python
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> doc = nlp(u"Give it back! He pleaded.")
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> span = doc[1:4]
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> assert [t.text for t in span] == [u"it", u"back", u"!"]
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> ```
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| Name | Type | Description |
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| ----------- | ---------------------------------------- | ------------------------------------------------------- |
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| `doc` | `Doc` | The parent document. |
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| `start` | int | The index of the first token of the span. |
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| `end` | int | The index of the first token after the span. |
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| `label` | int | A label to attach to the span, e.g. for named entities. |
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| `vector` | `numpy.ndarray[ndim=1, dtype='float32']` | A meaning representation of the span. |
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| **RETURNS** | `Span` | The newly constructed object. |
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## Span.\_\_getitem\_\_ {#getitem tag="method"}
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Get a `Token` object.
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> #### Example
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>
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> ```python
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> doc = nlp(u"Give it back! He pleaded.")
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> span = doc[1:4]
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> assert span[1].text == "back"
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> ```
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| Name | Type | Description |
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| ----------- | ------- | --------------------------------------- |
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| `i` | int | The index of the token within the span. |
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| **RETURNS** | `Token` | The token at `span[i]`. |
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Get a `Span` object.
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> #### Example
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>
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> ```python
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> doc = nlp(u"Give it back! He pleaded.")
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> span = doc[1:4]
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> assert span[1:3].text == u"back!"
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> ```
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| Name | Type | Description |
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| ----------- | ------ | -------------------------------- |
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| `start_end` | tuple | The slice of the span to get. |
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| **RETURNS** | `Span` | The span at `span[start : end]`. |
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## Span.\_\_iter\_\_ {#iter tag="method"}
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Iterate over `Token` objects.
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> #### Example
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>
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> ```python
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> doc = nlp(u"Give it back! He pleaded.")
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> span = doc[1:4]
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> assert [t.text for t in span] == [u"it", u"back", u"!"]
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> ```
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| Name | Type | Description |
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| ---------- | ------- | ----------------- |
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| **YIELDS** | `Token` | A `Token` object. |
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## Span.\_\_len\_\_ {#len tag="method"}
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Get the number of tokens in the span.
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> #### Example
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>
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> ```python
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> doc = nlp(u"Give it back! He pleaded.")
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> span = doc[1:4]
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> assert len(span) == 3
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> ```
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| Name | Type | Description |
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| ----------- | ---- | --------------------------------- |
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| **RETURNS** | int | The number of tokens in the span. |
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## Span.set_extension {#set_extension tag="classmethod" new="2"}
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Define a custom attribute on the `Span` which becomes available via `Span._`.
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For details, see the documentation on
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[custom attributes](/usage/processing-pipelines#custom-components-attributes).
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> #### Example
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>
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> ```python
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> from spacy.tokens import Span
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> city_getter = lambda span: any(city in span.text for city in (u"New York", u"Paris", u"Berlin"))
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> Span.set_extension("has_city", getter=city_getter)
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> doc = nlp(u"I like New York in Autumn")
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> assert doc[1:4]._.has_city
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> ```
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| Name | Type | Description |
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| --------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------- |
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| `name` | unicode | Name of the attribute to set by the extension. For example, `'my_attr'` will be available as `span._.my_attr`. |
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| `default` | - | Optional default value of the attribute if no getter or method is defined. |
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| `method` | callable | Set a custom method on the object, for example `span._.compare(other_span)`. |
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| `getter` | callable | Getter function that takes the object and returns an attribute value. Is called when the user accesses the `._` attribute. |
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| `setter` | callable | Setter function that takes the `Span` and a value, and modifies the object. Is called when the user writes to the `Span._` attribute. |
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## Span.get_extension {#get_extension tag="classmethod" new="2"}
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Look up a previously registered extension by name. Returns a 4-tuple
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`(default, method, getter, setter)` if the extension is registered. Raises a
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`KeyError` otherwise.
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> #### Example
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>
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> ```python
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> from spacy.tokens import Span
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> Span.set_extension("is_city", default=False)
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> extension = Span.get_extension("is_city")
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> assert extension == (False, None, None, None)
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> ```
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| Name | Type | Description |
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| ----------- | ------- | ------------------------------------------------------------- |
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| `name` | unicode | Name of the extension. |
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| **RETURNS** | tuple | A `(default, method, getter, setter)` tuple of the extension. |
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## Span.has_extension {#has_extension tag="classmethod" new="2"}
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Check whether an extension has been registered on the `Span` class.
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> #### Example
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>
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> ```python
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> from spacy.tokens import Span
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> Span.set_extension("is_city", default=False)
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> assert Span.has_extension("is_city")
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> ```
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| Name | Type | Description |
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| ----------- | ------- | ------------------------------------------ |
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| `name` | unicode | Name of the extension to check. |
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| **RETURNS** | bool | Whether the extension has been registered. |
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## Span.remove_extension {#remove_extension tag="classmethod" new="2.0.12"}
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Remove a previously registered extension.
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> #### Example
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>
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> ```python
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> from spacy.tokens import Span
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> Span.set_extension("is_city", default=False)
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> removed = Span.remove_extension("is_city")
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> assert not Span.has_extension("is_city")
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> ```
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| Name | Type | Description |
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| ----------- | ------- | --------------------------------------------------------------------- |
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| `name` | unicode | Name of the extension. |
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| **RETURNS** | tuple | A `(default, method, getter, setter)` tuple of the removed extension. |
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## Span.similarity {#similarity tag="method" model="vectors"}
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Make a semantic similarity estimate. The default estimate is cosine similarity
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using an average of word vectors.
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> #### Example
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>
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> ```python
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> doc = nlp(u"green apples and red oranges")
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> green_apples = doc[:2]
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> red_oranges = doc[3:]
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> apples_oranges = green_apples.similarity(red_oranges)
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> oranges_apples = red_oranges.similarity(green_apples)
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> assert apples_oranges == oranges_apples
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> ```
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| Name | Type | Description |
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| ----------- | ----- | -------------------------------------------------------------------------------------------- |
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| `other` | - | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
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| **RETURNS** | float | A scalar similarity score. Higher is more similar. |
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## Span.get_lca_matrix {#get_lca_matrix tag="method"}
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Calculates the lowest common ancestor matrix for a given `Span`. Returns LCA
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matrix containing the integer index of the ancestor, or `-1` if no common
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ancestor is found, e.g. if span excludes a necessary ancestor.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like New York in Autumn")
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> span = doc[1:4]
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> matrix = span.get_lca_matrix()
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> # array([[0, 0, 0], [0, 1, 2], [0, 2, 2]], dtype=int32)
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> ```
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| Name | Type | Description |
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| ----------- | -------------------------------------- | ------------------------------------------------ |
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| **RETURNS** | `numpy.ndarray[ndim=2, dtype='int32']` | The lowest common ancestor matrix of the `Span`. |
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## Span.to_array {#to_array tag="method" new="2"}
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Given a list of `M` attribute IDs, export the tokens to a numpy `ndarray` of
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shape `(N, M)`, where `N` is the length of the document. The values will be
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32-bit integers.
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> #### Example
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>
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> ```python
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> from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
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> doc = nlp(u"I like New York in Autumn.")
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> span = doc[2:3]
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> # All strings mapped to integers, for easy export to numpy
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> np_array = span.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
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> ```
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| Name | Type | Description |
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| ----------- | ----------------------------- | -------------------------------------------------------------------------------------------------------- |
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| `attr_ids` | list | A list of attribute ID ints. |
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| **RETURNS** | `numpy.ndarray[long, ndim=2]` | A feature matrix, with one row per word, and one column per attribute indicated in the input `attr_ids`. |
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## Span.merge {#merge tag="method"}
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<Infobox title="Deprecation note" variant="danger">
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As of v2.1.0, `Span.merge` still works but is considered deprecated. You should
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use the new and less error-prone [`Doc.retokenize`](/api/doc#retokenize)
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instead.
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</Infobox>
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Retokenize the document, such that the span is merged into a single token.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like New York in Autumn.")
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> span = doc[2:4]
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> span.merge()
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> assert len(doc) == 6
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> assert doc[2].text == u"New York"
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> ```
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| Name | Type | Description |
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| -------------- | ------- | ------------------------------------------------------------------------------------------------------------------------- |
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| `**attributes` | - | Attributes to assign to the merged token. By default, attributes are inherited from the syntactic root token of the span. |
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| **RETURNS** | `Token` | The newly merged token. |
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## Span.ents {#ents tag="property" new="2.0.12" model="ner"}
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Iterate over the entities in the span. Yields named-entity `Span` objects, if
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the entity recognizer has been applied to the parent document.
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> #### Example
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>
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> ```python
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> doc = nlp(u"Mr. Best flew to New York on Saturday morning.")
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> span = doc[0:6]
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> ents = list(span.ents)
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> assert ents[0].label == 346
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> assert ents[0].label_ == "PERSON"
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> assert ents[0].text == u"Mr. Best"
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> ```
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| Name | Type | Description |
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| ---------- | ------ | ------------------------- |
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| **YIELDS** | `Span` | Entities in the document. |
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## Span.as_doc {#as_doc tag="method"}
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Create a new `Doc` object corresponding to the `Span`, with a copy of the data.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like New York in Autumn.")
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> span = doc[2:4]
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> doc2 = span.as_doc()
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> assert doc2.text == u"New York"
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> ```
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| Name | Type | Description |
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| ----------- | ----- | --------------------------------------- |
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| **RETURNS** | `Doc` | A `Doc` object of the `Span`'s content. |
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## Span.root {#root tag="property" model="parser"}
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The token within the span that's highest in the parse tree. If there's a tie,
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the earliest is preferred.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like New York in Autumn.")
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> i, like, new, york, in_, autumn, dot = range(len(doc))
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> assert doc[new].head.text == u"York"
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> assert doc[york].head.text == u"like"
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> new_york = doc[new:york+1]
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> assert new_york.root.text == u"York"
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> ```
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| Name | Type | Description |
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| ----------- | ------- | --------------- |
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| **RETURNS** | `Token` | The root token. |
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## Span.lefts {#lefts tag="property" model="parser"}
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Tokens that are to the left of the span, whose heads are within the span.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like New York in Autumn.")
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> lefts = [t.text for t in doc[3:7].lefts]
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> assert lefts == [u"New"]
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> ```
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| Name | Type | Description |
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| ---------- | ------- | ------------------------------------ |
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| **YIELDS** | `Token` | A left-child of a token of the span. |
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## Span.rights {#rights tag="property" model="parser"}
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Tokens that are to the right of the span, whose heads are within the span.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like New York in Autumn.")
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> rights = [t.text for t in doc[2:4].rights]
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> assert rights == [u"in"]
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> ```
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| Name | Type | Description |
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| ---------- | ------- | ------------------------------------- |
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| **YIELDS** | `Token` | A right-child of a token of the span. |
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## Span.n_lefts {#n_lefts tag="property" model="parser"}
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The number of tokens that are to the left of the span, whose heads are within
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the span.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like New York in Autumn.")
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> assert doc[3:7].n_lefts == 1
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> ```
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| Name | Type | Description |
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| ----------- | ---- | -------------------------------- |
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| **RETURNS** | int | The number of left-child tokens. |
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## Span.n_rights {#n_rights tag="property" model="parser"}
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The number of tokens that are to the right of the span, whose heads are within
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the span.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like New York in Autumn.")
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> assert doc[2:4].n_rights == 1
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> ```
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| Name | Type | Description |
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| ----------- | ---- | --------------------------------- |
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| **RETURNS** | int | The number of right-child tokens. |
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## Span.subtree {#subtree tag="property" model="parser"}
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Tokens within the span and tokens which descend from them.
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> #### Example
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>
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> ```python
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> doc = nlp(u"Give it back! He pleaded.")
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> subtree = [t.text for t in doc[:3].subtree]
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> assert subtree == [u"Give", u"it", u"back", u"!"]
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> ```
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| Name | Type | Description |
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| ---------- | ------- | ------------------------------------------------- |
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| **YIELDS** | `Token` | A token within the span, or a descendant from it. |
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## Span.has_vector {#has_vector tag="property" model="vectors"}
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A boolean value indicating whether a word vector is associated with the object.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like apples")
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> assert doc[1:].has_vector
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> ```
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| Name | Type | Description |
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| ----------- | ---- | -------------------------------------------- |
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| **RETURNS** | bool | Whether the span has a vector data attached. |
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## Span.vector {#vector tag="property" model="vectors"}
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A real-valued meaning representation. Defaults to an average of the token
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vectors.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like apples")
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> assert doc[1:].vector.dtype == "float32"
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> assert doc[1:].vector.shape == (300,)
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> ```
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| Name | Type | Description |
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| ----------- | ---------------------------------------- | --------------------------------------------------- |
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| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A 1D numpy array representing the span's semantics. |
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## Span.vector_norm {#vector_norm tag="property" model="vectors"}
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The L2 norm of the span's vector representation.
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> #### Example
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>
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> ```python
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> doc = nlp(u"I like apples")
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> doc[1:].vector_norm # 4.800883928527915
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> doc[2:].vector_norm # 6.895897646384268
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> assert doc[1:].vector_norm != doc[2:].vector_norm
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> ```
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| Name | Type | Description |
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| ----------- | ----- | ----------------------------------------- |
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| **RETURNS** | float | The L2 norm of the vector representation. |
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## Attributes {#attributes}
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| Name | Type | Description |
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| -------------- | ------------ | -------------------------------------------------------------------------------------------------------------- |
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| `doc` | `Doc` | The parent document. |
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| `sent` | `Span` | The sentence span that this span is a part of. |
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| `start` | int | The token offset for the start of the span. |
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| `end` | int | The token offset for the end of the span. |
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| `start_char` | int | The character offset for the start of the span. |
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| `end_char` | int | The character offset for the end of the span. |
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| `text` | unicode | A unicode representation of the span text. |
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| `text_with_ws` | unicode | The text content of the span with a trailing whitespace character if the last token has one. |
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| `orth` | int | ID of the verbatim text content. |
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| `orth_` | unicode | Verbatim text content (identical to `Span.text`). Exists mostly for consistency with the other attributes. |
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| `label` | int | The span's label. |
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| `label_` | unicode | The span's label. |
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| `lemma_` | unicode | The span's lemma. |
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| `ent_id` | int | The hash value of the named entity the token is an instance of. |
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| `ent_id_` | unicode | The string ID of the named entity the token is an instance of. |
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| `sentiment` | float | A scalar value indicating the positivity or negativity of the span. |
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| `_` | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). |
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