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Update pipeline design docs [ci skip]

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Ines Montani 2021-04-06 14:13:22 +10:00
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@ -55,15 +55,15 @@ For a detailed compatibility overview, see the
This is also the source of spaCy's internal compatibility check, performed when This is also the source of spaCy's internal compatibility check, performed when
you run the [`download`](/api/cli#download) command. you run the [`download`](/api/cli#download) command.
## Pretrained pipeline design {#design} ## Trained pipeline design {#design}
The spaCy v3 pretrained pipelines are designed to be efficient and configurable. The spaCy v3 trained pipelines are designed to be efficient and configurable.
For example, multiple components can share a common "token-to-vector" model and For example, multiple components can share a common "token-to-vector" model and
it's easy to swap out or disable the lemmatizer. The pipelines are designed to it's easy to swap out or disable the lemmatizer. The pipelines are designed to
be efficient in terms of speed and size and work well when the pipeline is run be efficient in terms of speed and size and work well when the pipeline is run
in full. in full.
When modifying a pretrained pipeline, it's important to understand how the When modifying a trained pipeline, it's important to understand how the
components **depend on** each other. Unlike spaCy v2, where the `tagger`, components **depend on** each other. Unlike spaCy v2, where the `tagger`,
`parser` and `ner` components were all independent, some v3 components depend on `parser` and `ner` components were all independent, some v3 components depend on
earlier components in the pipeline. As a result, disabling or reordering earlier components in the pipeline. As a result, disabling or reordering
@ -84,6 +84,8 @@ Main changes from spaCy v2 models:
### CNN/CPU pipeline design ### CNN/CPU pipeline design
![Components and their dependencies in the CNN pipelines](../images/pipeline-design.svg)
In the `sm`/`md`/`lg` models: In the `sm`/`md`/`lg` models:
- The `tagger`, `morphologizer` and `parser` components listen to the `tok2vec` - The `tagger`, `morphologizer` and `parser` components listen to the `tok2vec`
@ -99,11 +101,9 @@ In the `sm`/`md`/`lg` models:
`tagger`+`attribute_ruler` or `morphologizer`. `tagger`+`attribute_ruler` or `morphologizer`.
- The `ner` component is independent with its own internal tok2vec layer. - The `ner` component is independent with its own internal tok2vec layer.
<!-- TODO: pretty diagram -->
### Transformer pipeline design ### Transformer pipeline design
In the tranformer (`trf`) models, the `tagger`, `parser` and `ner` (if present) In the transformer (`trf`) models, the `tagger`, `parser` and `ner` (if present)
all listen to the `transformer` component. The `attribute_ruler` and all listen to the `transformer` component. The `attribute_ruler` and
`lemmatizer` have the same configuration as in the CNN models. `lemmatizer` have the same configuration as in the CNN models.
@ -112,7 +112,7 @@ all listen to the `transformer` component. The `attribute_ruler` and
### Modifying the default pipeline ### Modifying the default pipeline
For faster processing, you may only want to run a subset of the components in a For faster processing, you may only want to run a subset of the components in a
pretrained pipeline. The `disable` and `exclude` arguments to trained pipeline. The `disable` and `exclude` arguments to
[`spacy.load`](/api/top-level#spacy.load) let you control which components are [`spacy.load`](/api/top-level#spacy.load) let you control which components are
loaded and run. Disabled components are loaded in the background so it's loaded and run. Disabled components are loaded in the background so it's
possible to reenable them in the same pipeline in the future with possible to reenable them in the same pipeline in the future with