mirror of https://github.com/explosion/spaCy.git
43 lines
2.7 KiB
Markdown
43 lines
2.7 KiB
Markdown
|
When you call `nlp` on a text, spaCy first tokenizes the text to produce a `Doc`
|
|||
|
|
object. The `Doc` is then processed in several different steps – this is also
|
|||
|
|
referred to as the **processing pipeline**. The pipeline used by the
|
|||
|
|
[default models](/models) consists of a tagger, a parser and an entity
|
|||
|
|
recognizer. Each pipeline component returns the processed `Doc`, which is then
|
|||
|
|
passed on to the next component.
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
> - **Name**: ID of the pipeline component.
|
|||
|
|
> - **Component:** spaCy's implementation of the component.
|
|||
|
|
> - **Creates:** Objects, attributes and properties modified and set by the
|
|||
|
|
> component.
|
|||
|
|
|
|||
|
|
| Name | Component | Creates | Description |
|
|||
|
|
| ------------- | ------------------------------------------------------------------ | ----------------------------------------------------------- | ------------------------------------------------ |
|
|||
|
|
| **tokenizer** | [`Tokenizer`](/api/tokenizer) | `Doc` | Segment text into tokens. |
|
|||
|
|
| **tagger** | [`Tagger`](/api/tagger) | `Doc[i].tag` | Assign part-of-speech tags. |
|
|||
|
|
| **parser** | [`DependencyParser`](/api/dependencyparser) | `Doc[i].head`, `Doc[i].dep`, `Doc.sents`, `Doc.noun_chunks` | Assign dependency labels. |
|
|||
|
|
| **ner** | [`EntityRecognizer`](/api/entityrecognizer) | `Doc.ents`, `Doc[i].ent_iob`, `Doc[i].ent_type` | Detect and label named entities. |
|
|||
|
|
| **textcat** | [`TextCategorizer`](/api/textcategorizer) | `Doc.cats` | Assign document labels. |
|
|||
|
|
| ... | [custom components](/usage/processing-pipelines#custom-components) | `Doc._.xxx`, `Token._.xxx`, `Span._.xxx` | Assign custom attributes, methods or properties. |
|
|||
|
|
|
|||
|
|
The processing pipeline always **depends on the statistical model** and its
|
|||
|
|
capabilities. For example, a pipeline can only include an entity recognizer
|
|||
|
|
component if the model includes data to make predictions of entity labels. This
|
|||
|
|
is why each model will specify the pipeline to use in its meta data, as a simple
|
|||
|
|
list containing the component names:
|
|||
|
|
|
|||
|
|
```json
|
|||
|
|
"pipeline": ["tagger", "parser", "ner"]
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
import Accordion from 'components/accordion.js'
|
|||
|
|
|
|||
|
|
<Accordion title="Does the order of pipeline components matter?">
|
|||
|
|
|
|||
|
|
No
|
|||
|
|
|
|||
|
|
</Accordion>
|
|||
|
|
|
|||
|
|
---
|