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.

![The processing pipeline](../../images/pipeline.svg)

> - **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>

---