Trained Models & Pipelines

Downloadable trained pipelines and weights for spaCy
Loading style
Select for
python -m spacy download en_core_web_smimport spacynlp = spacy.load("en_core_web_sm")import en_core_web_smnlp = en_core_web_sm.load()doc = nlp("This is a sentence.")print([(w.text, w.pos_) for w in doc])

Package naming 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 is a small English pipeline trained on written web text (blogs, news, comments), that includes vocabulary, vectors, syntax and entities.

Package 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. This is also the source of spaCy’s internal compatibility check, performed when you run the download command.

Trained pipeline 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 component may be a separate, shared component. A component like a tagger or parser can listen 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

Components and their dependencies in the CNN pipelines

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

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.

Modifying the default pipeline

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 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. 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.

# 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"])

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:

nlp = spacy.load("en_core_web_sm")

The senter component is ~10× 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:

# Requirements: pip install spacy-lookups-data
nlp = spacy.load("en_core_web_sm")
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:

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.

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:

# 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")