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
Doc	A container for accessing linguistic annotations.
DocBin	A collection of Doc objects for efficient binary serialization. Also used for training data.
Example	A collection of training annotations, containing two Doc objects: the reference data and the predictions.
Language	Processing class that turns text into Doc objects. Different languages implement their own subclasses of it. The variable is typically called nlp.
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.
Span	A slice from a Doc object.
SpanGroup	A named collection of spans belonging to a Doc.
Token	An individual token — i.e. a word, punctuation symbol, whitespace, etc.

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.

Component name	Component class	Description
attribute_ruler	AttributeRuler	Set token attributes using matcher rules.
entity_linker	EntityLinker	Disambiguate named entities to nodes in a knowledge base.
entity_ruler	SpanRuler	Add entity spans to the Doc using token-based rules or exact phrase matches.
lemmatizer	Lemmatizer	Determine the base forms of words using rules and lookups.
morphologizer	Morphologizer	Predict morphological features and coarse-grained part-of-speech tags.
ner	EntityRecognizer	Predict named entities, e.g. persons or products.
parser	DependencyParser	Predict syntactic dependencies.
senter	SentenceRecognizer	Predict sentence boundaries.
sentencizer	Sentencizer	Implement rule-based sentence boundary detection that doesn't require the dependency parse.
span_ruler	SpanRuler	Add spans to the Doc using token-based rules or exact phrase matches.
tagger	Tagger	Predict part-of-speech tags.
textcat	TextCategorizer	Predict exactly one category or label over a whole document.
textcat_multilabel	MultiLabel_TextCategorizer	Predict 0, 1 or more categories or labels over a whole document.
tok2vec	Tok2Vec	Apply a "token-to-vector" model and set its outputs.
tokenizer	Tokenizer	Segment raw text and create Doc objects from the words.
trainable_lemmatizer	EditTreeLemmatizer	Predict base forms of words.
transformer	Transformer	Use a transformer model and set its outputs.
-	TrainablePipe	Class that all trainable pipeline components inherit from.
-	Other functions	Automatically apply something to the Doc, e.g. to merge spans of tokens.

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

Other classes

Name	Description
Corpus	Class for managing annotated corpora for training and evaluation data.
KnowledgeBase	Abstract base class for storage and retrieval of data for entity linking.
InMemoryLookupKB	Implementation of KnowledgeBase storing all data in memory.
Candidate	Object associating a textual mention with a specific entity contained in a KnowledgeBase.
Lookups	Container for convenient access to large lookup tables and dictionaries.
MorphAnalysis	A morphological analysis.
Morphology	Store morphological analyses and map them to and from hash values.
Scorer	Compute evaluation scores.
StringStore	Map strings to and from hash values.
Vectors	Container class for vector data keyed by string.
Vocab	The shared vocabulary that stores strings and gives you access to Lexeme objects.