spaCy

Features

Support for 60+ languages
Trained pipelines for different languages and tasks
Multi-task learning with pretrained transformers like BERT
Support for pretrained word vectors and embeddings
State-of-the-art speed
Production-ready training system
Linguistically-motivated tokenization
Components for named entity recognition, part-of-speech-tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more
Easily extensible with custom components and attributes
Support for custom models in PyTorch, TensorFlow and other frameworks
Built in visualizers for syntax and NER
Easy model packaging, deployment and workflow management
Robust, rigorously evaluated accuracy

Concepts/Glossary

NAME	DESCRIPTION
Tokenization	Segmenting text into words, punctuations marks etc.
Part-of-speech (POS) Tagging	Assigning word types to tokens, like verb or noun.
Dependency Parsing	Assigning syntactic dependency labels, describing the relations between individual tokens, like subject or object.
Lemmatization	Assigning the base forms of words. For example, the lemma of “was” is “be”, and the lemma of “rats” is “rat”.
Sentence Boundary Detection (SBD)	Finding and segmenting individual sentences.
Named Entity Recognition (NER)	Labelling named “real-world” objects, like persons, companies or locations.
Entity Linking (EL)	Disambiguating textual entities to unique identifiers in a knowledge base.
Similarity	Comparing words, text spans and documents and how similar they are to each other.
Text Classification	Assigning categories or labels to a whole document, or parts of a document.
Rule-based Matching	Finding sequences of tokens based on their texts and linguistic annotations, similar to regular expressions.
Training	Updating and improving a statistical model’s predictions.
Serialization	Saving objects to files or byte strings.

Tags: library python nlp

Last modified 14 November 2025