Facts & Figures - Spacy

Comparison {id="comparison",hidden="true"}

spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python. It's designed specifically for production use and helps you build applications that process and "understand" large volumes of text. It can be used to build information extraction or natural language understanding systems.

Feature overview {id="comparison-features"}

When should I use spaCy? {id="comparison-usage"}

✅ I'm a beginner and just getting started with NLP. – spaCy makes it easy to get started and comes with extensive documentation, including a beginner-friendly 101 guide, a free interactive online course and a range of video tutorials.
✅ I want to build an end-to-end production application. – spaCy is specifically designed for production use and lets you build and train powerful NLP pipelines and package them for easy deployment.
✅ I want my application to be efficient on GPU and CPU. – While spaCy lets you train modern NLP models that are best run on GPU, it also offers CPU-optimized pipelines, which are less accurate but much cheaper to run.
✅ I want to try out different neural network architectures for NLP. – spaCy lets you customize and swap out the model architectures powering its components, and implement your own using a framework like PyTorch or TensorFlow. The declarative configuration system makes it easy to mix and match functions and keep track of your hyperparameters to make sure your experiments are reproducible.
❌ I want to build a language generation application. – spaCy's focus is natural language processing and extracting information from large volumes of text. While you can use it to help you re-write existing text, it doesn't include any specific functionality for language generation tasks.
❌ I want to research machine learning algorithms. spaCy is built on the latest research, but it's not a research library. If your goal is to write papers and run benchmarks, spaCy is probably not a good choice. However, you can use it to make the results of your research easily available for others to use, e.g. via a custom spaCy component.

Benchmarks {id="benchmarks"}

spaCy v3.0 introduces transformer-based pipelines that bring spaCy's accuracy right up to current state-of-the-art. You can also use a CPU-optimized pipeline, which is less accurate but much cheaper to run.

Evaluation details

OntoNotes 5.0: spaCy's English models are trained on this corpus, as it's several times larger than other English treebanks. However, most systems do not report accuracies on it.

Penn Treebank: The "classic" parsing evaluation for research. However, it's quite far removed from actual usage: it uses sentences with gold-standard segmentation and tokenization, from a pretty specific type of text (articles from a single newspaper, 1984-1989).

Dependency Parsing System	UAS	LAS
spaCy RoBERTa (2020)	95.1	93.7
Mrini et al. (2019)	97.4	96.3
Zhou and Zhao (2019)	97.2	95.7

Dependency parsing accuracy on the Penn Treebank. See NLP-progress for more results. Project template: benchmarks/parsing_penn_treebank.

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Speed comparison {id="benchmarks-speed"}

We compare the speed of different NLP libraries, measured in words per second (WPS) - higher is better. The evaluation was performed on 10,000 Reddit comments.

Library	Pipeline	WPS CPU <Help>words per second on CPU, higher is better</Help>	WPS GPU <Help>words per second on GPU, higher is better</Help>
spaCy	`en_core_web_lg`	10,014	14,954
spaCy	`en_core_web_trf`	684	3,768
Stanza	`en_ewt`	878	2,180
Flair	`pos`(`-fast`) & `ner`(`-fast`)	323	1,184
UDPipe	`english-ewt-ud-2.5`	1,101	n/a

End-to-end processing speed on raw unannotated text. Project template: benchmarks/speed.

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