spaCy/website/docs/usage/facts-figures.md

title	teaser	next	menu
Facts & Figures	The hard numbers for spaCy and how it compares to other tools	/usage/spacy-101	Feature Comparison comparison Benchmarks benchmarks

Comparison

When should I use spaCy?

• ✅ 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

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).

import Benchmarks from 'usage/_benchmarks-models.md'

The easiest way to reproduce spaCy's benchmarks on the Penn Treebank is to clone our project template.