spaCy/website/docs/usage/101/_architecture.md

The central data structures in spaCy are the Language class, the Vocab and the Doc object. The Language class is used to process a text and turn it into a Doc object. It's typically stored as a variable called nlp. The Doc object owns the sequence of tokens and all their annotations. By centralizing strings, word vectors and lexical attributes in the Vocab, we avoid storing multiple copies of this data. This saves memory, and ensures there's a single source of truth.

Text annotations are also designed to allow a single source of truth: the Doc object owns the data, and Span and Token are views that point into it. The Doc object is constructed by the Tokenizer, and then modified in place by the components of the pipeline. The Language object coordinates these components. It takes raw text and sends it through the pipeline, returning an annotated document. It also orchestrates training and serialization.

Container objects

Name	Description
Language	Processing class that turns text into Doc objects. Different languages implement their own subclasses of it. The variable is typically called nlp.
Doc	A container for accessing linguistic annotations.
Span	A slice from a Doc object.
Token	An individual token — i.e. a word, punctuation symbol, whitespace, etc.
Lexeme	An entry in the vocabulary. It's a word type with no context, as opposed to a word token. It therefore has no part-of-speech tag, dependency parse etc.
Example	A collection of training annotations, containing two Doc objects: the reference data and the predictions.
DocBin	A collection of Doc objects for efficient binary serialization. Also used for training data.

Processing pipeline

The processing pipeline consists of one or more pipeline components that are called on the Doc in order. The tokenizer runs before the components. Pipeline components can be added using Language.add_pipe. They can contain a statistical model and trained weights, or only make rule-based modifications to the Doc. spaCy provides a range of built-in components for different language processing tasks and also allows adding custom components.

Name	Description
Tokenizer	Segment raw text and create Doc objects from the words.
Tok2Vec	Apply a "token-to-vector" model and set its outputs.
Transformer	Use a transformer model and set its outputs.
Lemmatizer	Determine the base forms of words.
Morphologizer	Predict morphological features and coarse-grained part-of-speech tags.
Tagger	Predict part-of-speech tags.
AttributeRuler	Set token attributes using matcher rules.
DependencyParser	Predict syntactic dependencies.
EntityRecognizer	Predict named entities, e.g. persons or products.
EntityRuler	Add entity spans to the Doc using token-based rules or exact phrase matches.
EntityLinker	Disambiguate named entities to nodes in a knowledge base.
TextCategorizer	Predict categories or labels over the whole document.
Sentencizer	Implement rule-based sentence boundary detection that doesn't require the dependency parse.
SentenceRecognizer	Predict sentence boundaries.
Other functions	Automatically apply something to the Doc, e.g. to merge spans of tokens.
Pipe	Base class that pipeline components may inherit from.
TrainablePipe	Class that all trainable pipeline components inherit from.

Matchers

Matchers help you find and extract information from Doc objects based on match patterns describing the sequences you're looking for. A matcher operates on a Doc and gives you access to the matched tokens in context.

Name	Description
Matcher	Match sequences of tokens, based on pattern rules, similar to regular expressions.
PhraseMatcher	Match sequences of tokens based on phrases.
DependencyMatcher	Match sequences of tokens based on dependency trees using Semgrex operators.

Other classes

Name	Description
Vocab	The shared vocabulary that stores strings and gives you access to Lexeme objects.
StringStore	Map strings to and from hash values.
Vectors	Container class for vector data keyed by string.
Lookups	Container for convenient access to large lookup tables and dictionaries.
Morphology	Store morphological analyses and map them to and from hash values.
MorphAnalysis	A morphological analysis.
KnowledgeBase	Storage for entities and aliases of a knowledge base for entity linking.
Scorer	Compute evaluation scores.
Corpus	Class for managing annotated corpora for training and evaluation data.