0Pricing
NLP Academy · 课时

添加自定义实体规则

捕捉模型遗漏的领域术语

添加自定义实体规则 是 CoddyKit 上的免费 NLP Academy 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 NLP Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 NLP Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

When the Model Misses

Pre-trained NER never saw your product names or internal jargon. Custom rules let you catch the terms it always misses. 🎯

Meet the EntityRuler

spaCy's EntityRuler adds entities from patterns you define, no retraining required. It plugs straight into the pipeline.

Add It to the Pipe

Insert the ruler with add_pipe. Placing it before ner lets your rules take priority over the model's guesses.

ruler = nlp.add_pipe("entity_ruler", before="ner")

Patterns Are Dicts

Each rule is a dict with a label and a pattern. The label is the entity type you want assigned to a match.

{"label": "PRODUCT", "pattern": "CoddyKit"}

Add Your Patterns

Feed a list of pattern dicts to ruler.add_patterns. Now those exact phrases get tagged every time they appear.

ruler.add_patterns([{"label": "PRODUCT", "pattern": "CoddyKit"}])

Token-Based Patterns

Beyond plain strings, patterns can be token lists that match on attributes like lowercase text, giving flexible rules.

{"label": "ORG", "pattern": [{"LOWER": "acme"}]}

Match Multi-Word Terms

A token pattern with several entries catches phrases, so San Pedro Lab is tagged as one entity, not three words.

[{"LOWER": "san"}, {"LOWER": "pedro"}, {"LOWER": "lab"}]

Rules Run First

Because the ruler sits before ner, your custom labels win on conflicts, while the model still handles everything else.

Test Your Rules

Run a sentence through the updated pipeline and loop doc.ents to confirm your new terms now show the right label.

doc = nlp("We shipped CoddyKit today.")
# CoddyKit -> PRODUCT

Save the Patterns

Export rules to disk with to_disk so your whole team reuses the same custom entities instead of redefining them.

ruler.to_disk("patterns.jsonl")

Rules Plus Learning

Custom rules are a fast first step. Later you can train the model on labeled examples for tricky, fuzzy cases. 🚀

Quick Check

How do you make custom rules override the model?

Recap

The EntityRuler adds entities from string or token patterns, runs before ner, and saves to disk for reuse. ✅

常见问题解答

「添加自定义实体规则」课时是免费的吗？

是的 — 「添加自定义实体规则」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 NLP Academy 课程的其余内容，请升级到 CoddyKit PRO。 NLP Academy 课程共包含 4 节课。

「添加自定义实体规则」这节课中我会学到什么？

捕捉模型遗漏的领域术语 你通过在浏览器中直接运行的动手代码来练习 NLP Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 NLP Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 NLP Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。

「添加自定义实体规则」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 NLP Academy 课中编写并运行代码吗？

能。每节 NLP Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. 什么算作实体
  2. 使用 spaCy 提取实体
  3. 使用 displaCy 可视化实体
  4. 添加自定义实体规则
← 返回 NLP Academy