---
title: Trained Models & Pipelines
teaser: Downloadable trained pipelines and weights for spaCy
menu:
  - ['Quickstart', 'quickstart']
  - ['Conventions', 'conventions']
  - ['Pipeline Design', 'design']
---

<!-- TODO: include interactive demo -->

### Quickstart {hidden="true"}

> #### 📖 Installation and usage
>
> For more details on how to use trained pipelines with spaCy, see the
> [usage guide](/usage/models).

import QuickstartModels from 'widgets/quickstart-models.js'

<QuickstartModels id="quickstart" />

## Package naming conventions {#conventions}

In general, spaCy expects all pipeline packages to follow the naming convention
of `[lang]\_[name]`. For spaCy's pipelines, we also chose to divide the name
into three components:

1. **Type:** Capabilities (e.g. `core` for general-purpose pipeline with
   vocabulary, syntax, entities and word vectors, or `dep` for only vocab and
   syntax).
2. **Genre:** Type of text the pipeline is trained on, e.g. `web` or `news`.
3. **Size:** Package size indicator, `sm`, `md` or `lg`.

For example, [`en_core_web_sm`](/models/en#en_core_web_sm) is a small English
pipeline trained on written web text (blogs, news, comments), that includes
vocabulary, vectors, syntax and entities.

### Package versioning {#model-versioning}

Additionally, the pipeline package versioning reflects both the compatibility
with spaCy, as well as the major and minor version. A package version `a.b.c`
translates to:

- `a`: **spaCy major version**. For example, `2` for spaCy v2.x.
- `b`: **Package major version**. Pipelines with a different major version can't
  be loaded by the same code. For example, changing the width of the model,
  adding hidden layers or changing the activation changes the major version.
- `c`: **Package minor version**. Same pipeline structure, but different
  parameter values, e.g. from being trained on different data, for different
  numbers of iterations, etc.

For a detailed compatibility overview, see the
[`compatibility.json`](https://github.com/explosion/spacy-models/tree/master/compatibility.json).
This is also the source of spaCy's internal compatibility check, performed when
you run the [`download`](/api/cli#download) command.

## Trained pipeline design {#design}

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
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
in full.

When modifying a trained pipeline, it's important to understand how the
components **depend on** each other. Unlike spaCy v2, where the `tagger`,
`parser` and `ner` components were all independent, some v3 components depend on
earlier components in the pipeline. As a result, disabling or reordering
components can affect the annotation quality or lead to warnings and errors.

Main changes from spaCy v2 models:

- The [`Tok2Vec`](/api/tok2vec) component may be a separate, shared component. A
  component like a tagger or parser can
  [listen](/api/architectures#Tok2VecListener) to an earlier `tok2vec` or
  `transformer` rather than having its own separate tok2vec layer.
- Rule-based exceptions move from individual components to the
  `attribute_ruler`. Lemma and POS exceptions move from the tokenizer exceptions
  to the attribute ruler and the tag map and morph rules move from the tagger to
  the attribute ruler.
- The lemmatizer tables and processing move from the vocab and tagger to a
  separate `lemmatizer` component.

### CNN/CPU pipeline design {#design-cnn}

![Components and their dependencies in the CNN pipelines](../images/pipeline-design.svg)

In the `sm`/`md`/`lg` models:

- The `tagger`, `morphologizer` and `parser` components listen to the `tok2vec`
  component.
- The `attribute_ruler` maps `token.tag` to `token.pos` if there is no
  `morphologizer`. The `attribute_ruler` additionally makes sure whitespace is
  tagged consistently and copies `token.pos` to `token.tag` if there is no
  tagger. For English, the attribute ruler can improve its mapping from
  `token.tag` to `token.pos` if dependency parses from a `parser` are present,
  but the parser is not required.
- The rule-based `lemmatizer` (Dutch, English, French, Greek, Macedonian,
  Norwegian and Spanish) requires `token.pos` annotation from either
  `tagger`+`attribute_ruler` or `morphologizer`.
- The `ner` component is independent with its own internal tok2vec layer.

### Transformer pipeline design {#design-trf}

In the transformer (`trf`) models, the `tagger`, `parser` and `ner` (if present)
all listen to the `transformer` component. The `attribute_ruler` and
`lemmatizer` have the same configuration as in the CNN models.

<!-- TODO: pretty diagram -->

### Modifying the default pipeline {#design-modify}

For faster processing, you may only want to run a subset of the components in a
trained pipeline. The `disable` and `exclude` arguments to
[`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
possible to reenable them in the same pipeline in the future with
[`nlp.enable_pipe`](/api/language/#enable_pipe). To skip loading a component
completely, use `exclude` instead of `disable`.

#### Disable part-of-speech tagging and lemmatization

To disable part-of-speech tagging and lemmatization, disable the `tagger`,
`morphologizer`, `attribute_ruler` and `lemmatizer` components.

```python
# Note: English doesn't include a morphologizer
nlp = spacy.load("en_core_web_sm", disable=["tagger", "attribute_ruler", "lemmatizer"])
nlp = spacy.load("en_core_web_trf", disable=["tagger", "attribute_ruler", "lemmatizer"])
```

<Infobox variant="warning" title="Rule-based lemmatizers require Token.pos">

The lemmatizer depends on `tagger`+`attribute_ruler` or `morphologizer` for
Dutch, English, French, Greek, Macedonian, Norwegian and Spanish. If you disable
any of these components, you'll see lemmatizer warnings unless the lemmatizer is
also disabled.

</Infobox>

#### Use senter rather than parser for fast sentence segmentation

If you need fast sentence segmentation without dependency parses, disable the
`parser` use the `senter` component instead:

```python
nlp = spacy.load("en_core_web_sm")
nlp.disable_pipe("parser")
nlp.enable_pipe("senter")
```

The `senter` component is ~10&times; faster than the parser and more accurate
than the rule-based `sentencizer`.

#### Switch from rule-based to lookup lemmatization

For the Dutch, English, French, Greek, Macedonian, Norwegian and Spanish
pipelines, you can switch from the default rule-based lemmatizer to a lookup
lemmatizer:

```python
# Requirements: pip install spacy-lookups-data
nlp = spacy.load("en_core_web_sm")
nlp.remove_pipe("lemmatizer")
nlp.add_pipe("lemmatizer", config={"mode": "lookup"}).initialize()
```

#### Disable everything except NER

For the non-transformer models, the `ner` component is independent, so you can
disable everything else:

```python
nlp = spacy.load("en_core_web_sm", disable=["tok2vec", "tagger", "parser", "attribute_ruler", "lemmatizer"])
```

In the transformer models, `ner` listens to the `transformer` component, so you
can disable all components related tagging, parsing, and lemmatization.

```python
nlp = spacy.load("en_core_web_trf", disable=["tagger", "parser", "attribute_ruler", "lemmatizer"])
```

#### Move NER to the end of the pipeline

For access to `POS` and `LEMMA` features in an `entity_ruler`, move `ner` to the
end of the pipeline after `attribute_ruler` and `lemmatizer`:

```python
# load without NER
nlp = spacy.load("en_core_web_sm", exclude=["ner"])

# source NER from the same pipeline package as the last component
nlp.add_pipe("ner", source=spacy.load("en_core_web_sm"))

# insert the entity ruler
nlp.add_pipe("entity_ruler", before="ner")
```
