docs: make Chinese README the default
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wehub-resource-sync
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<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
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> [English](./README.en.md) · [原始项目](https://github.com/explosion/spaCy) · [上游 README](https://github.com/explosion/spaCy/blob/HEAD/README.md)
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> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
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<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
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# spaCy: Industrial-strength NLP
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# spaCy：工业级 NLP
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spaCy is a library for **advanced Natural Language Processing** in Python and
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Cython. It's built on the very latest research, and was designed from day one to
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be used in real products.
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spaCy 是一个用于 Python 和 Cython 的**高级自然语言处理（Natural Language Processing）**库。它基于最前沿的研究成果，从诞生之初就面向真实产品场景而设计。
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spaCy comes with [pretrained pipelines](https://spacy.io/models) and currently
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supports tokenization and training for **70+ languages**. It features
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state-of-the-art speed and **neural network models** for tagging, parsing,
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**named entity recognition**, **text classification** and more, multi-task
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learning with pretrained **transformers** like BERT, as well as a
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production-ready [**training system**](https://spacy.io/usage/training) and easy
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model packaging, deployment and workflow management. spaCy is commercial
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open-source software, released under the
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[MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).
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spaCy 提供[预训练流水线（pretrained pipelines）](https://spacy.io/models)，目前支持 **70+** 种语言的词元化（tokenization）与训练。它在词性标注、句法分析、**命名实体识别（named entity recognition）**、**文本分类（text classification）**等任务上具备顶尖速度与**神经网络模型**，支持基于 BERT 等预训练 **Transformer** 的多任务学习，还提供生产就绪的[**训练系统（training system）**](https://spacy.io/usage/training)，以及便捷的模型打包、部署与工作流管理。spaCy 是商业开源软件，采用 [MIT 许可证（MIT license）](https://github.com/explosion/spaCy/blob/master/LICENSE).
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💫 **Version 3.8 out now!**
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[Check out the release notes here.](https://github.com/explosion/spaCy/releases)
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💫 **3.8 版本现已发布！**
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[在此查看发行说明。](https://github.com/explosion/spaCy/releases)
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[](https://github.com/explosion/spaCy/actions/workflows/tests.yml)
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[](https://github.com/explosion/spaCy/releases)
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@@ -29,30 +25,30 @@ open-source software, released under the
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[](https://pypi.org/project/spacy/)
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[](https://anaconda.org/conda-forge/spacy)
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## 📖 Documentation
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## 📖 文档
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| Documentation | |
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| 文档 | |
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| ⭐️ **[spaCy 101]** | New to spaCy? Here's everything you need to know! |
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| 📚 **[Usage Guides]** | How to use spaCy and its features. |
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| 🚀 **[New in v3.0]** | New features, backwards incompatibilities and migration guide. |
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| 🪐 **[Project Templates]** | End-to-end workflows you can clone, modify and run. |
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| 🎛 **[API Reference]** | The detailed reference for spaCy's API. |
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| ⏩ **[GPU Processing]** | Use spaCy with CUDA-compatible GPU processing. |
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| 📦 **[Models]** | Download trained pipelines for spaCy. |
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| 🦙 **[Large Language Models]** | Integrate LLMs into spaCy pipelines. |
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| 🌌 **[Universe]** | Plugins, extensions, demos and books from the spaCy ecosystem. |
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| ⚙️ **[spaCy VS Code Extension]** | Additional tooling and features for working with spaCy's config files. |
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| 👩🏫 **[Online Course]** | Learn spaCy in this free and interactive online course. |
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| 📰 **[Blog]** | Read about current spaCy and Prodigy development, releases, talks and more from Explosion. |
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| 📺 **[Videos]** | Our YouTube channel with video tutorials, talks and more. |
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| 🔴 **[Live Stream]** | Join Matt as he works on spaCy and chat about NLP. |
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| 🛠 **[Changelog]** | Changes and version history. |
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| 💝 **[Contribute]** | How to contribute to the spaCy project and code base. |
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| 👕 **[Swag]** | Support us and our work with unique, custom-designed swag! |
|
||||
| <a href="https://explosion.ai/tailored-solutions"><img src="https://github.com/explosion/spaCy/assets/13643239/36d2a42e-98c0-4599-90e1-788ef75181be" width="150" alt="Tailored Solutions"/></a> | Custom NLP consulting, implementation and strategic advice by spaCy’s core development team. Streamlined, production-ready, predictable and maintainable. Send us an email or take our 5-minute questionnaire, and well'be in touch! **[Learn more →](https://explosion.ai/tailored-solutions)** |
|
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| ⭐️ **[spaCy 101]** | 初次接触 spaCy？这里有你需要了解的一切！ |
|
||||
| 📚 **[Usage Guides]** | 如何使用 spaCy 及其功能。 |
|
||||
| 🚀 **[New in v3.0]** | 新功能、向后不兼容变更与迁移指南。 |
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| 🪐 **[Project Templates]** | 可克隆、修改并运行的端到端工作流。 |
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| 🎛 **[API Reference]** | spaCy API 的详细参考文档。 |
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| ⏩ **[GPU Processing]** | 在兼容 CUDA 的 GPU 上运行 spaCy。 |
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| 📦 **[Models]** | 下载 spaCy 的训练流水线。 |
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| 🦙 **[Large Language Models]** | 将大语言模型（LLM）集成到 spaCy 流水线中。 |
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| 🌌 **[Universe]** | spaCy 生态中的插件、扩展、演示与书籍。 |
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| ⚙️ **[spaCy VS Code Extension]** | 用于处理 spaCy 配置文件的额外工具与功能。 |
|
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| 👩🏫 **[Online Course]** | 在这门免费互动在线课程中学习 spaCy。 |
|
||||
| 📰 **[Blog]** | 阅读 Explosion 关于 spaCy 与 Prodigy 的最新开发动态、版本发布、演讲等内容。 |
|
||||
| 📺 **[Videos]** | 我们的 YouTube 频道，提供视频教程、演讲等内容。 |
|
||||
| 🔴 **[Live Stream]** | 与 Matt 一起直播开发 spaCy，畅聊 NLP。 |
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| 🛠 **[Changelog]** | 变更与版本历史。 |
|
||||
| 💝 **[Contribute]** | 如何为 spaCy 项目与代码库做贡献。 |
|
||||
| 👕 **[Swag]** | 购买独特定制周边，支持我们及我们的工作！ |
|
||||
| <a href="https://explosion.ai/tailored-solutions"><img src="https://github.com/explosion/spaCy/assets/13643239/36d2a42e-98c0-4599-90e1-788ef75181be" width="150" alt="定制解决方案"/></a> | 由 spaCy 核心开发团队提供的定制 NLP 咨询、实施与战略建议。精简、生产就绪、可预测且易于维护。给我们发邮件或填写 5 分钟问卷，我们会尽快与您联系！**[了解更多 →](https://explosion.ai/tailored-solutions)** |
|
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|
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[spacy 101]: https://spacy.io/usage/spacy-101
|
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[spaCy 101]: https://spacy.io/usage/spacy-101
|
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[new in v3.0]: https://spacy.io/usage/v3
|
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[usage guides]: https://spacy.io/usage/
|
||||
[api reference]: https://spacy.io/api/
|
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@@ -70,79 +66,73 @@ open-source software, released under the
|
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[contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md
|
||||
[swag]: https://explosion.ai/merch
|
||||
|
||||
## 💬 Where to ask questions
|
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## 💬 在哪里提问
|
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|
||||
The spaCy project is maintained by the [spaCy team](https://explosion.ai/about).
|
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Please understand that we won't be able to provide individual support via email.
|
||||
We also believe that help is much more valuable if it's shared publicly, so that
|
||||
more people can benefit from it.
|
||||
spaCy 项目由 [spaCy 团队](https://explosion.ai/about).
|
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维护。请注意，我们无法通过电子邮件提供一对一支持。
|
||||
我们也认为，公开分享的帮助更有价值，这样更多人可以从中受益。
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|
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| Type | Platforms |
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| 类型 | 平台 |
|
||||
| ------------------------------- | --------------------------------------- |
|
||||
| 🚨 **Bug Reports** | [GitHub Issue Tracker] |
|
||||
| 🎁 **Feature Requests & Ideas** | [GitHub Discussions] · [Live Stream] |
|
||||
| 👩💻 **Usage Questions** | [GitHub Discussions] · [Stack Overflow] |
|
||||
| 🗯 **General Discussion** | [GitHub Discussions] · [Live Stream] |
|
||||
| 🚨 **缺陷报告** | [GitHub Issue Tracker] |
|
||||
| 🎁 **功能请求与想法** | [GitHub Discussions] · [Live Stream] |
|
||||
| 👩💻 **使用问题** | [GitHub Discussions] · [Stack Overflow] |
|
||||
| 🗯 **一般讨论** | [GitHub Discussions] · [Live Stream] |
|
||||
|
||||
[github issue tracker]: https://github.com/explosion/spaCy/issues
|
||||
[github discussions]: https://github.com/explosion/spaCy/discussions
|
||||
[stack overflow]: https://stackoverflow.com/questions/tagged/spacy
|
||||
[live stream]: https://www.youtube.com/playlist?list=PLBmcuObd5An5_iAxNYLJa_xWmNzsYce8c
|
||||
|
||||
## Features
|
||||
## 特性
|
||||
|
||||
- Support for **70+ languages**
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- **Trained pipelines** for different languages and tasks
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- Multi-task learning with pretrained **transformers** like BERT
|
||||
- Support for pretrained **word vectors** and embeddings
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||||
- State-of-the-art speed
|
||||
- Production-ready **training system**
|
||||
- Linguistically-motivated **tokenization**
|
||||
- Components for named **entity recognition**, part-of-speech-tagging,
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dependency parsing, sentence segmentation, **text classification**,
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||||
lemmatization, morphological analysis, entity linking and more
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- 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
|
||||
- 支持 **70+ 种语言**
|
||||
- 适用于不同语言和任务的 **训练流水线（pipeline）**
|
||||
- 多任务学习，使用 BERT 等预训练 **transformer**
|
||||
- 支持预训练 **词向量（word vectors）** 和嵌入（embeddings）
|
||||
- 业界领先的运行速度
|
||||
- 可用于生产的 **训练系统**
|
||||
- 基于语言学动机的 **分词（tokenization）**
|
||||
- 命名 **实体识别（NER）**、词性标注、依存句法分析、句子分割、**文本分类**、词形还原、形态学分析、实体链接等组件
|
||||
- 可通过 **自定义组件** 和属性轻松扩展
|
||||
- 支持在 **PyTorch**、**TensorFlow** 及其他框架中使用自定义模型
|
||||
- 内置句法和 NER 的 **可视化工具（visualizer）**
|
||||
- 便捷的 **模型打包**、部署与工作流管理
|
||||
- 稳健、经过严格评估的准确度
|
||||
|
||||
📖 **For more details, see the
|
||||
[facts, figures and benchmarks](https://spacy.io/usage/facts-figures).**
|
||||
📖 **更多详情请参阅
|
||||
[事实、数据与基准测试](https://spacy.io/usage/facts-figures).**
|
||||
|
||||
## ⏳ Install spaCy
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## ⏳ 安装 spaCy
|
||||
|
||||
For detailed installation instructions, see the
|
||||
[documentation](https://spacy.io/usage).
|
||||
详细安装说明请参阅
|
||||
[文档](https://spacy.io/usage).
|
||||
|
||||
- **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual
|
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Studio)
|
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- **Python version**: Python >=3.7, <3.13 (only 64 bit)
|
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- **Package managers**: [pip] · [conda] (via `conda-forge`)
|
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- **操作系统**：macOS / OS X · Linux · Windows（Cygwin、MinGW、Visual
|
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Studio）
|
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- **Python 版本**：Python >=3.7，<3.13（仅 64 位）
|
||||
- **包管理器**：[pip] · [conda]（通过 `conda-forge`）
|
||||
|
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[pip]: https://pypi.org/project/spacy/
|
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[conda]: https://anaconda.org/conda-forge/spacy
|
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|
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### pip
|
||||
|
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Using pip, spaCy releases are available as source packages and binary wheels.
|
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Before you install spaCy and its dependencies, make sure that your `pip`,
|
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`setuptools` and `wheel` are up to date.
|
||||
使用 pip 时，spaCy 发布包提供源码包和二进制 wheel 包。
|
||||
在安装 spaCy 及其依赖之前，请确保你的 `pip`、
|
||||
`setuptools` 和 `wheel` 已是最新版本。
|
||||
|
||||
```bash
|
||||
pip install -U pip setuptools wheel
|
||||
pip install spacy
|
||||
```
|
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|
||||
To install additional data tables for lemmatization and normalization you can
|
||||
run `pip install spacy[lookups]` or install
|
||||
[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)
|
||||
separately. The lookups package is needed to create blank models with
|
||||
lemmatization data, and to lemmatize in languages that don't yet come with
|
||||
pretrained models and aren't powered by third-party libraries.
|
||||
要安装用于词形还原和规范化的额外数据表，可以
|
||||
运行 `pip install spacy[lookups]`，或单独安装
|
||||
[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)。
|
||||
lookups 包用于创建带有词形还原数据的空白模型，以及在尚未提供预训练模型、且不依赖第三方库的语言中进行词形还原。
|
||||
|
||||
When using pip it is generally recommended to install packages in a virtual
|
||||
environment to avoid modifying system state:
|
||||
使用 pip 时，通常建议在虚拟环境中安装软件包，以避免修改系统状态：
|
||||
|
||||
```bash
|
||||
python -m venv .env
|
||||
@@ -153,45 +143,38 @@ pip install spacy
|
||||
|
||||
### conda
|
||||
|
||||
You can also install spaCy from `conda` via the `conda-forge` channel. For the
|
||||
feedstock including the build recipe and configuration, check out
|
||||
[this repository](https://github.com/conda-forge/spacy-feedstock).
|
||||
你也可以通过 `conda-forge` 频道从 `conda` 安装 spaCy。有关包含构建配方和配置的 feedstock，请参阅
|
||||
[此仓库](https://github.com/conda-forge/spacy-feedstock).
|
||||
|
||||
```bash
|
||||
conda install -c conda-forge spacy
|
||||
```
|
||||
|
||||
### Updating spaCy
|
||||
### 更新 spaCy
|
||||
|
||||
Some updates to spaCy may require downloading new statistical models. If you're
|
||||
running spaCy v2.0 or higher, you can use the `validate` command to check if
|
||||
your installed models are compatible and if not, print details on how to update
|
||||
them:
|
||||
spaCy 的某些更新可能需要下载新的统计模型。如果你运行的是 spaCy v2.0 或更高版本，可以使用 `validate` 命令检查已安装模型是否兼容；若不兼容，会输出如何更新它们的详细信息：
|
||||
|
||||
```bash
|
||||
pip install -U spacy
|
||||
python -m spacy validate
|
||||
```
|
||||
|
||||
If you've trained your own models, keep in mind that your training and runtime
|
||||
inputs must match. After updating spaCy, we recommend **retraining your models**
|
||||
with the new version.
|
||||
如果你训练了自己的模型，请注意训练时与运行时的输入必须一致。更新 spaCy 后，我们建议使用新版本 **重新训练你的模型**。
|
||||
|
||||
📖 **For details on upgrading from spaCy 2.x to spaCy 3.x, see the
|
||||
[migration guide](https://spacy.io/usage/v3#migrating).**
|
||||
📖 **有关从 spaCy 2.x 升级到 spaCy 3.x 的详情，请参阅
|
||||
[迁移指南](https://spacy.io/usage/v3#migrating).**
|
||||
|
||||
## 📦 Download model packages
|
||||
## 📦 下载模型包
|
||||
|
||||
Trained pipelines for spaCy can be installed as **Python packages**. This means
|
||||
that they're a component of your application, just like any other module. Models
|
||||
can be installed using spaCy's [`download`](https://spacy.io/api/cli#download)
|
||||
command, or manually by pointing pip to a path or URL.
|
||||
spaCy 的训练流水线可作为 **Python 软件包** 安装。这意味着
|
||||
它们是你应用程序的组成部分，就像任何其他模块一样。可以使用 spaCy 的 [`download`](https://spacy.io/api/cli#download)
|
||||
命令安装模型，也可以通过让 pip 指向路径或 URL 手动安装。
|
||||
|
||||
| Documentation | |
|
||||
| 文档 | |
|
||||
| -------------------------- | ---------------------------------------------------------------- |
|
||||
| **[Available Pipelines]** | Detailed pipeline descriptions, accuracy figures and benchmarks. |
|
||||
| **[Models Documentation]** | Detailed usage and installation instructions. |
|
||||
| **[Training]** | How to train your own pipelines on your data. |
|
||||
| **[可用流水线]** | 详细的流水线说明、准确度数据与基准测试。 |
|
||||
| **[模型文档]** | 详细的使用与安装说明。 |
|
||||
| **[训练]** | 如何在自己的数据上训练自定义流水线。 |
|
||||
|
||||
[available pipelines]: https://spacy.io/models
|
||||
[models documentation]: https://spacy.io/usage/models
|
||||
@@ -207,10 +190,10 @@ pip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whl
|
||||
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz
|
||||
```
|
||||
|
||||
### Loading and using models
|
||||
### 加载和使用模型
|
||||
|
||||
To load a model, use [`spacy.load()`](https://spacy.io/api/top-level#spacy.load)
|
||||
with the model name or a path to the model data directory.
|
||||
要加载模型，请使用 [`spacy.load()`](https://spacy.io/api/top-level#spacy.load)
|
||||
，并传入模型名称或模型数据目录的路径。
|
||||
|
||||
```python
|
||||
import spacy
|
||||
@@ -218,8 +201,8 @@ nlp = spacy.load("en_core_web_sm")
|
||||
doc = nlp("This is a sentence.")
|
||||
```
|
||||
|
||||
You can also `import` a model directly via its full name and then call its
|
||||
`load()` method with no arguments.
|
||||
你也可以通过模型的完整名称直接 `import`，然后无参调用其
|
||||
`load()` 方法。
|
||||
|
||||
```python
|
||||
import spacy
|
||||
@@ -229,31 +212,28 @@ nlp = en_core_web_sm.load()
|
||||
doc = nlp("This is a sentence.")
|
||||
```
|
||||
|
||||
📖 **For more info and examples, check out the
|
||||
[models documentation](https://spacy.io/docs/usage/models).**
|
||||
📖 **更多信息和示例，请参阅
|
||||
[模型文档](https://spacy.io/docs/usage/models).**
|
||||
|
||||
## ⚒ Compile from source
|
||||
## ⚒ 从源码编译
|
||||
|
||||
The other way to install spaCy is to clone its
|
||||
[GitHub repository](https://github.com/explosion/spaCy) and build it from
|
||||
source. That is the common way if you want to make changes to the code base.
|
||||
You'll need to make sure that you have a development environment consisting of a
|
||||
Python distribution including header files, a compiler,
|
||||
安装 spaCy 的另一种方式是克隆其
|
||||
[GitHub 仓库](https://github.com/explosion/spaCy)，并从源码构建。
|
||||
如果你想修改代码库，这通常是常用的方式。
|
||||
你需要确保已具备开发环境，包括带头文件的 Python 发行版、编译器、
|
||||
[pip](https://pip.pypa.io/en/latest/installing/),
|
||||
[virtualenv](https://virtualenv.pypa.io/en/latest/) and
|
||||
[git](https://git-scm.com) installed. The compiler part is the trickiest. How to
|
||||
do that depends on your system.
|
||||
[virtualenv](https://virtualenv.pypa.io/en/latest/) 和
|
||||
[git](https://git-scm.com) 已安装。编译器部分最为棘手，具体做法取决于你的系统。
|
||||
|
||||
| Platform | |
|
||||
| 平台 | |
|
||||
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| **Ubuntu** | Install system-level dependencies via `apt-get`: `sudo apt-get install build-essential python-dev git` . |
|
||||
| **Mac** | Install a recent version of [XCode](https://developer.apple.com/xcode/), including the so-called "Command Line Tools". macOS and OS X ship with Python and git preinstalled. |
|
||||
| **Windows** | Install a version of the [Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) or [Visual Studio Express](https://visualstudio.microsoft.com/vs/express/) that matches the version that was used to compile your Python interpreter. |
|
||||
| **Ubuntu** | 通过 `apt-get` 安装系统级依赖：`sudo apt-get install build-essential python-dev git`。 |
|
||||
| **Mac** | 安装最新版 [XCode](https://developer.apple.com/xcode/),，其中包含所谓的「Command Line Tools」。macOS 和 OS X 预装了 Python 和 git。 |
|
||||
| **Windows** | 安装与你的 Python 解释器编译所用版本相匹配的 [Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) 或 [Visual Studio Express](https://visualstudio.microsoft.com/vs/express/)。 |
|
||||
|
||||
For more details and instructions, see the documentation on
|
||||
[compiling spaCy from source](https://spacy.io/usage#source) and the
|
||||
[quickstart widget](https://spacy.io/usage#section-quickstart) to get the right
|
||||
commands for your platform and Python version.
|
||||
如需更多详情和操作说明，请参阅
|
||||
[从源码编译 spaCy](https://spacy.io/usage#source) 以及
|
||||
[快速入门 widget](https://spacy.io/usage#section-quickstart)，以获取适用于你的平台与 Python 版本的正确命令。
|
||||
|
||||
```bash
|
||||
git clone https://github.com/explosion/spaCy
|
||||
@@ -269,22 +249,17 @@ pip install -r requirements.txt
|
||||
pip install --no-build-isolation --editable .
|
||||
```
|
||||
|
||||
To install with extras:
|
||||
安装 extras 扩展：
|
||||
|
||||
```bash
|
||||
pip install --no-build-isolation --editable .[lookups,cuda102]
|
||||
```
|
||||
|
||||
## 🚦 Run tests
|
||||
## 🚦 运行测试
|
||||
|
||||
spaCy comes with an [extensive test suite](spacy/tests). In order to run the
|
||||
tests, you'll usually want to clone the repository and build spaCy from source.
|
||||
This will also install the required development dependencies and test utilities
|
||||
defined in the [`requirements.txt`](requirements.txt).
|
||||
spaCy 附带[完善的测试套件](spacy/tests)。要运行测试，你通常需要克隆仓库并从源码构建 spaCy。这也会安装 [`requirements.txt`](requirements.txt) 中定义的必要开发依赖与测试工具。
|
||||
|
||||
Alternatively, you can run `pytest` on the tests from within the installed
|
||||
`spacy` package. Don't forget to also install the test utilities via spaCy's
|
||||
[`requirements.txt`](requirements.txt):
|
||||
或者，你也可以在已安装的 `spacy` 包内对测试运行 `pytest`。别忘了通过 spaCy 的 [`requirements.txt`](requirements.txt) 安装测试工具：
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
|
||||
Reference in New Issue
Block a user