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Docs: displaCy documentation - data types, parse_{deps,ents,spans}, spans example (#10950)
* add in spans example and parse references * rm autoformatter * rm extra ents copy * TypedDict draft * type fixes * restore non-documentation files * docs update * fix spans example * fix hyperlinks * add parse example * example fix + argument fix * fix api arg in docs * fix bad variable replacement * fix spacing in style Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * fix spacing on table * fix spacing on table * rm temp files Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
This commit is contained in:
Peter Baumgartner
GitHub
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commit
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@ -123,7 +123,8 @@ def app(environ, start_response):
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def parse_deps(orig_doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
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"""Generate dependency parse in {'words': [], 'arcs': []} format.
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doc (Doc): Document do parse.
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orig_doc (Doc): Document to parse.
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options (Dict[str, Any]): Dependency parse specific visualisation options.
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RETURNS (dict): Generated dependency parse keyed by words and arcs.
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"""
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doc = Doc(orig_doc.vocab).from_bytes(
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@ -209,7 +210,7 @@ def parse_ents(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
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def parse_spans(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
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"""Generate spans in [{start: i, end: i, label: 'label'}] format.
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"""Generate spans in [{start_token: i, end_token: i, label: 'label'}] format.
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doc (Doc): Document to parse.
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options (Dict[str, any]): Span-specific visualisation options.
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@ -240,7 +240,7 @@ browser. Will run a simple web server.
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| Name | Description |
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| --------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `docs` | Document(s) or span(s) to visualize. ~~Union[Iterable[Union[Doc, Span]], Doc, Span]~~ |
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| `style` | Visualization style, `"dep"`, `"ent"` or `"span"` <Tag variant="new">3.3</Tag>. Defaults to `"dep"`. ~~str~~ |
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| `style` | Visualization style, `"dep"`, `"ent"` or `"span"` <Tag variant="new">3.3</Tag>. Defaults to `"dep"`. ~~str~~ |
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| `page` | Render markup as full HTML page. Defaults to `True`. ~~bool~~ |
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| `minify` | Minify HTML markup. Defaults to `False`. ~~bool~~ |
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| `options` | [Visualizer-specific options](#displacy_options), e.g. colors. ~~Dict[str, Any]~~ |
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@ -265,7 +265,7 @@ Render a dependency parse tree or named entity visualization.
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| Name | Description |
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| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `docs` | Document(s) or span(s) to visualize. ~~Union[Iterable[Union[Doc, Span, dict]], Doc, Span, dict]~~ |
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| `style` | Visualization style,`"dep"`, `"ent"` or `"span"` <Tag variant="new">3.3</Tag>. Defaults to `"dep"`. ~~str~~ |
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| `style` | Visualization style, `"dep"`, `"ent"` or `"span"` <Tag variant="new">3.3</Tag>. Defaults to `"dep"`. ~~str~~ |
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| `page` | Render markup as full HTML page. Defaults to `True`. ~~bool~~ |
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| `minify` | Minify HTML markup. Defaults to `False`. ~~bool~~ |
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| `options` | [Visualizer-specific options](#displacy_options), e.g. colors. ~~Dict[str, Any]~~ |
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@ -273,6 +273,73 @@ Render a dependency parse tree or named entity visualization.
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| `jupyter` | Explicitly enable or disable "[Jupyter](http://jupyter.org/) mode" to return markup ready to be rendered in a notebook. Detected automatically if `None` (default). ~~Optional[bool]~~ |
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| **RETURNS** | The rendered HTML markup. ~~str~~ |
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### displacy.parse_deps {#displacy.parse_deps tag="method" new="2"}
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Generate dependency parse in `{'words': [], 'arcs': []}` format.
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For use with the `manual=True` argument in `displacy.render`.
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> #### Example
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>
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> ```python
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> import spacy
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> from spacy import displacy
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> nlp = spacy.load("en_core_web_sm")
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> doc = nlp("This is a sentence.")
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> deps_parse = displacy.parse_deps(doc)
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> html = displacy.render(deps_parse, style="dep", manual=True)
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------------- |
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| `orig_doc` | Doc to parse dependencies. ~~Doc~~ |
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| `options` | Dependency parse specific visualisation options. ~~Dict[str, Any]~~ |
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| **RETURNS** | Generated dependency parse keyed by words and arcs. ~~dict~~ |
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### displacy.parse_ents {#displacy.parse_ents tag="method" new="2"}
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Generate named entities in `[{start: i, end: i, label: 'label'}]` format.
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For use with the `manual=True` argument in `displacy.render`.
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> #### Example
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>
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> ```python
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> import spacy
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> from spacy import displacy
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> nlp = spacy.load("en_core_web_sm")
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> doc = nlp("But Google is starting from behind.")
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> ents_parse = displacy.parse_ents(doc)
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> html = displacy.render(ents_parse, style="ent", manual=True)
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------------- |
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| `doc` | Doc to parse entities. ~~Doc~~ |
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| `options` | NER-specific visualisation options. ~~Dict[str, Any]~~ |
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| **RETURNS** | Generated entities keyed by text (original text) and ents. ~~dict~~ |
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### displacy.parse_spans {#displacy.parse_spans tag="method" new="2"}
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Generate spans in `[{start_token: i, end_token: i, label: 'label'}]` format.
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For use with the `manual=True` argument in `displacy.render`.
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> #### Example
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>
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> ```python
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> import spacy
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> from spacy import displacy
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> nlp = spacy.load("en_core_web_sm")
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> doc = nlp("But Google is starting from behind.")
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> doc.spans['orgs'] = [doc[1:2]]
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> ents_parse = displacy.parse_spans(doc, options={"spans_key" : "orgs"})
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> html = displacy.render(ents_parse, style="span", manual=True)
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------------- |
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| `doc` | Doc to parse entities. ~~Doc~~ |
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| `options` | Span-specific visualisation options. ~~Dict[str, Any]~~ |
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| **RETURNS** | Generated entities keyed by text (original text) and ents. ~~dict~~ |
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### Visualizer options {#displacy_options}
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The `options` argument lets you specify additional settings for each visualizer.
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@ -198,12 +198,12 @@ import DisplacySpanHtml from 'images/displacy-span.html'
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The span visualizer lets you customize the following `options`:
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| Argument | Description |
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|-----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|
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| `spans_key` | Which spans key to render spans from. Default is `"sc"`. ~~str~~ |
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| Argument | Description |
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| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `spans_key` | Which spans key to render spans from. Default is `"sc"`. ~~str~~ |
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| `templates` | Dictionary containing the keys `"span"`, `"slice"`, and `"start"`. These dictate how the overall span, a span slice, and the starting token will be rendered. ~~Optional[Dict[str, str]~~ |
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| `kb_url_template` | Optional template to construct the KB url for the entity to link to. Expects a python f-string format with single field to fill in ~~Optional[str]~~ |
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| `colors` | Color overrides. Entity types should be mapped to color names or values. ~~Dict[str, str]~~ |
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| `kb_url_template` | Optional template to construct the KB url for the entity to link to. Expects a python f-string format with single field to fill in ~~Optional[str]~~ |
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| `colors` | Color overrides. Entity types should be mapped to color names or values. ~~Dict[str, str]~~ |
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Because spans can be stored across different keys in `doc.spans`, you need to specify
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which one displaCy should use with `spans_key` (`sc` is the default).
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@ -343,9 +343,21 @@ want to visualize output from other libraries, like [NLTK](http://www.nltk.org)
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or
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[SyntaxNet](https://github.com/tensorflow/models/tree/master/research/syntaxnet).
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If you set `manual=True` on either `render()` or `serve()`, you can pass in data
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in displaCy's format as a dictionary (instead of `Doc` objects).
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in displaCy's format as a dictionary (instead of `Doc` objects). There are helper
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functions for converting `Doc` objects to displaCy's format for use with `manual=True`:
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[`displacy.parse_deps`](/api/top-level#displacy.parse_deps),
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[`displacy.parse_ents`](/api/top-level#displacy.parse_ents),
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and [`displacy.parse_spans`](/api/top-level#displacy.parse_spans).
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> #### Example
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> #### Example with parse function
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>
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> ```python
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> doc = nlp("But Google is starting from behind.")
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> ex = displacy.parse_ents(doc)
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> html = displacy.render(ex, style="ent", manual=True)
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> ```
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> #### Example with raw data
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>
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> ```python
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> ex = [{"text": "But Google is starting from behind.",
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@ -354,6 +366,7 @@ in displaCy's format as a dictionary (instead of `Doc` objects).
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> html = displacy.render(ex, style="ent", manual=True)
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> ```
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```python
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### DEP input
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{
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@ -389,6 +402,18 @@ in displaCy's format as a dictionary (instead of `Doc` objects).
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}
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```
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```python
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### SPANS input
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{
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"text": "Welcome to the Bank of China.",
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"spans": [
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{"start_token": 3, "end_token": 6, "label": "ORG"},
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{"start_token": 5, "end_token": 6, "label": "GPE"},
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],
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"tokens": ["Welcome", "to", "the", "Bank", "of", "China", "."],
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}
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```
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## Using displaCy in a web application {#webapp}
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If you want to use the visualizers as part of a web application, for example to
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