spaCy · Industrial-strength Natural Language Processing in Python
Industrial-Strength
Natural Language
Processing
in Python
Get things done
spaCy is designed to help you do real work — to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive.
Blazing fast
spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using.
Awesome ecosystem
Since its release in 2015, spaCy has become an industry standard with a huge ecosystem. Choose from a variety of plugins, integrate with your machine learning stack and build custom components and workflows.
Edit the code & try spaCyspaCy v3.7 · Python 3 · via Binder
Features
- Support for 75+ languages
- 84 trained pipelines for 25 languages
- Multi-task learning with pretrained transformers like BERT
- Pretrained word vectors
- 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
Reproducible training for custom pipelines
spaCy v3.0 introduces a comprehensive and extensible system for configuring your training runs. Your configuration file will describe every detail of your training run, with no hidden defaults, making it easy to rerun your experiments and track changes. You can use the quickstart widget or the init config command to get started, or clone a project template for an end-to-end workflow.
Components
Hardware
Optimize for
# This is an auto-generated partial config. To use it with 'spacy train'
# you can run spacy init fill-config to auto-fill all default settings:
# python -m spacy init fill-config ./base_config.cfg ./config.cfg
[paths]
train = null
dev = null
vectors = null
[system]
gpu_allocator = null
[nlp]
lang = "en"
pipeline = []
batch_size = 1000
[components]
[corpora]
[corpora.train]
@readers = "spacy.Corpus.v1"
path = ${paths.train}
max_length = 0
[corpora.dev]
@readers = "spacy.Corpus.v1"
path = ${paths.dev}
max_length = 0
[training]
dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
[training.optimizer]
@optimizers = "Adam.v1"
[training.batcher]
@batchers = "spacy.batch_by_words.v1"
discard_oversize = false
tolerance = 0.2
[training.batcher.size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001
[initialize]
vectors = ${paths.vectors}
End-to-end workflows from prototype to production
spaCy's new project system gives you a smooth path from prototype to production. It lets you keep track of all those data transformation, preprocessing and training steps, so you can make sure your project is always ready to hand over for automation. It features source asset download, command execution, checksum verification, and caching with a variety of backends and integrations.
Benchmarks
spaCy v3.0 introduces transformer-based pipelines that bring spaCy's accuracy right up to the current state-of-the-art. You can also use a CPU-optimized pipeline, which is less accurate but much cheaper to run.