spaCy/website/docs/models/index.md

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Models	Downloadable pretrained models for spaCy	Quickstart quickstart Conventions conventions

The models directory includes two types of pretrained models:

1. Core models: General-purpose pretrained models to predict named entities, part-of-speech tags and syntactic dependencies. Can be used out-of-the-box and fine-tuned on more specific data.
2. Starter models: Transfer learning starter packs with pretrained weights you can initialize your models with to achieve better accuracy. They can include word vectors (which will be used as features during training) or other pretrained representations like BERT. These models don't include components for specific tasks like NER or text classification and are intended to be used as base models when training your own models.

Quickstart

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

For more details on how to use models with spaCy, see the usage guide on models.

Model naming conventions

In general, spaCy expects all model packages to follow the naming convention of [lang_[name]]. For spaCy's models, we also chose to divide the name into three components:

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

For example, en_core_web_sm is a small English model trained on written web text (blogs, news, comments), that includes vocabulary, vectors, syntax and entities.

Model versioning

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

• a: spaCy major version. For example, 2 for spaCy v2.x.
• b: Model major version. Models 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 model major version.
• c: Model minor version. Same model 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 in the models repository. This is also the source of spaCy's internal compatibility check, performed when you run the download command.