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123 lines
6.0 KiB
Markdown
123 lines
6.0 KiB
Markdown
---
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title: Facts & Figures
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teaser: The hard numbers for spaCy and how it compares to other tools
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next: /usage/spacy-101
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menu:
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- ['Feature Comparison', 'comparison']
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- ['Benchmarks', 'benchmarks']
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# TODO: - ['Citing spaCy', 'citation']
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---
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## Comparison {#comparison hidden="true"}
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spaCy is a **free, open-source library** for advanced **Natural Language
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Processing** (NLP) in Python. It's designed specifically for **production use**
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and helps you build applications that process and "understand" large volumes of
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text. It can be used to build information extraction or natural language
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understanding systems.
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### Feature overview {#comparison-features}
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import Features from 'widgets/features.js'
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<Features />
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### When should I use spaCy? {#comparison-usage}
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- ✅ **I'm a beginner and just getting started with NLP.** – spaCy makes it easy
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to get started and comes with extensive documentation, including a
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beginner-friendly [101 guide](/usage/spacy-101), a free interactive
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[online course](https://course.spacy.io) and a range of
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[video tutorials](https://www.youtube.com/c/ExplosionAI).
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- ✅ **I want to build an end-to-end production application.** – spaCy is
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specifically designed for production use and lets you build and train powerful
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NLP pipelines and package them for easy deployment.
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- ✅ **I want my application to be efficient on GPU _and_ CPU.** – While spaCy
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lets you train modern NLP models that are best run on GPU, it also offers
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CPU-optimized pipelines, which are less accurate but much cheaper to run.
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- ✅ **I want to try out different neural network architectures for NLP.** –
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spaCy lets you customize and swap out the model architectures powering its
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components, and implement your own using a framework like PyTorch or
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TensorFlow. The declarative configuration system makes it easy to mix and
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match functions and keep track of your hyperparameters to make sure your
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experiments are reproducible.
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- ❌ **I want to build a language generation application.** – spaCy's focus is
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natural language _processing_ and extracting information from large volumes of
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text. While you can use it to help you re-write existing text, it doesn't
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include any specific functionality for language generation tasks.
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- ❌ **I want to research machine learning algorithms.** spaCy is built on the
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latest research, but it's not a research library. If your goal is to write
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papers and run benchmarks, spaCy is probably not a good choice. However, you
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can use it to make the results of your research easily available for others to
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use, e.g. via a custom spaCy component.
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## Benchmarks {#benchmarks}
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spaCy v3.0 introduces transformer-based pipelines that bring spaCy's accuracy
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right up to **current state-of-the-art**. You can also use a CPU-optimized
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pipeline, which is less accurate but much cheaper to run.
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<!-- TODO: update benchmarks and intro -->
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> #### Evaluation details
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>
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> - **OntoNotes 5.0:** spaCy's English models are trained on this corpus, as
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> it's several times larger than other English treebanks. However, most
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> systems do not report accuracies on it.
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> - **Penn Treebank:** The "classic" parsing evaluation for research. However,
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> it's quite far removed from actual usage: it uses sentences with
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> gold-standard segmentation and tokenization, from a pretty specific type of
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> text (articles from a single newspaper, 1984-1989).
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import Benchmarks from 'usage/\_benchmarks-models.md'
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<Benchmarks />
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<figure>
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| Dependency Parsing System | UAS | LAS |
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| ------------------------------------------------------------------------------ | ---: | ---: |
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| spaCy RoBERTa (2020) | 95.1 | 93.7 |
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| [Mrini et al.](https://khalilmrini.github.io/Label_Attention_Layer.pdf) (2019) | 97.4 | 96.3 |
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| [Zhou and Zhao](https://www.aclweb.org/anthology/P19-1230/) (2019) | 97.2 | 95.7 |
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<figcaption class="caption">
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**Dependency parsing accuracy** on the Penn Treebank. See
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[NLP-progress](http://nlpprogress.com/english/dependency_parsing.html) for more
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results. Project template:
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[`benchmarks/parsing_penn_treebank`](%%GITHUB_PROJECTS/benchmarks/parsing_penn_treebank).
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</figcaption>
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</figure>
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### Speed comparison {#benchmarks-speed}
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We compare the speed of different NLP libraries, measured in words per second
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(WPS) - higher is better. The evaluation was performed on 10,000 Reddit
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comments.
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<figure>
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| 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> |
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| ------- | ----------------------------------------------- | -------------------------------------------------------------: | -------------------------------------------------------------: |
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| spaCy | [`en_core_web_lg`](/models/en#en_core_web_lg) | 10,014 | 14,954 |
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| spaCy | [`en_core_web_trf`](/models/en#en_core_web_trf) | 684 | 3,768 |
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| Stanza | `en_ewt` | 878 | 2,180 |
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| Flair | `pos`(`-fast`) & `ner`(`-fast`) | 323 | 1,184 |
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| UDPipe | `english-ewt-ud-2.5` | 1,101 | _n/a_ |
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<figcaption class="caption">
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**End-to-end processing speed** on raw unannotated text. Project template:
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[`benchmarks/speed`](%%GITHUB_PROJECTS/benchmarks/speed).
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</figcaption>
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</figure>
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<!-- TODO: ## Citing spaCy {#citation}
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-->
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