Fix links [ci skip]

website/docs/usage/models.md

@@ -295,7 +295,7 @@ If you've trained your own model, for example for
 convenient to deploy, we recommend wrapping it as a Python package.
 
 For more information and a detailed guide on how to package your model, see the
-documentation on [saving and loading models](/usage/training#saving-loading).
+documentation on [saving and loading models](/usage/saving-loading#models).
 
 ## Using models in production {#production}
 

website/docs/usage/processing-pipelines.md

@@ -18,10 +18,10 @@ spaCy makes it very easy to create your own pipelines consisting of reusable
 components – this includes spaCy's default tagger, parser and entity recognizer,
 but also your own custom processing functions. A pipeline component can be added
 to an already existing `nlp` object, specified when initializing a `Language`
-class, or defined within a [model package](/usage/training#saving-loading).
+class, or defined within a [model package](/usage/saving-loading#models).
 
 When you load a model, spaCy first consults the model's
-[`meta.json`](/usage/training#saving-loading). The meta typically includes the
+[`meta.json`](/usage/saving-loading#models). The meta typically includes the
 model details, the ID of a language class, and an optional list of pipeline
 components. spaCy then does the following:
 

website/docs/usage/spacy-101.md

@@ -379,7 +379,7 @@ import Serialization101 from 'usage/101/\_serialization.md'
 <Infobox title="📖 Saving and loading">
 
 To learn more about how to **save and load your own models**, see the usage
-guide on [saving and loading](/usage/training#saving-loading).
+guide on [saving and loading](/usage/saving-loading#models).
 
 </Infobox>
 
@@ -655,7 +655,7 @@ new_doc = Doc(Vocab()).from_disk("/tmp/customer_feedback_627.bin")
 <Infobox>
 
 **API:** [`Language`](/api/language), [`Doc`](/api/doc) **Usage:**
-[Saving and loading models](/usage/models#saving-loading)
+[Saving and loading models](/usage/saving-loading#models)
 
 </Infobox>
 
@@ -690,7 +690,7 @@ print("Sentiment", doc.sentiment)
 <Infobox>
 
 **API:** [`Matcher`](/api/matcher) **Usage:**
-[Rule-based matching](/usage/linguistic-features#rule-based-matching)
+[Rule-based matching](/usage/rule-based-matching)
 
 </Infobox>
 

website/docs/usage/training.md

@@ -7,12 +7,11 @@ menu:
   - ['Tagger & Parser', 'tagger-parser']
   - ['Text Classification', 'textcat']
   - ['Tips and Advice', 'tips']
-  - ['Saving & Loading', 'saving-loading']
 ---
 
 This guide describes how to train new statistical models for spaCy's
 part-of-speech tagger, named entity recognizer and dependency parser. Once the
-model is trained, you can then [save and load](/usage/models#saving-loading) it.
+model is trained, you can then [save and load](/usage/saving-loading#models) it.
 
 ## Training basics {#basics}
 
@@ -97,8 +96,7 @@ may end up with the following entities, some correct, some incorrect.
 | Spotify steps up Asia expansion    | Spotify | `0`   | `8`  | `ORG`    | ✅  |
 | Spotify steps up Asia expansion    | Asia    | `17`  | `21` | `NORP`   | ❌  |
 
-Alternatively, the
-[rule-based matcher](/usage/linguistic-features#rule-based-matching) can be a
+Alternatively, the [rule-based matcher](/usage/rule-based-matching) can be a
 useful tool to extract tokens or combinations of tokens, as well as their start
 and end index in a document. In this case, we'll extract mentions of Google and
 assume they're an `ORG`.
@@ -473,9 +471,9 @@ for hotels with high ratings for their wifi offerings.
 > To achieve even better accuracy, try merging multi-word tokens and entities
 > specific to your domain into one token before parsing your text. You can do
 > this by running the entity recognizer or
-> [rule-based matcher](/usage/linguistic-features#rule-based-matching) to find
-> relevant spans, and merging them using
-> [`Doc.retokenize`](/api/doc#retokenize). You could even add your own custom
+> [rule-based matcher](/usage/rule-based-matching) to find relevant spans, and
+> merging them using [`Doc.retokenize`](/api/doc#retokenize). You could even add
+> your own custom
 > [pipeline component](/usage/processing-pipelines#custom-components) to do this
 > automatically – just make sure to add it `before='parser'`.
 

website/docs/usage/v2.md

@@ -265,7 +265,7 @@ language, you can import the class directly, e.g.
 **API:** [`spacy.load`](/api/top-level#spacy.load),
 [`Language.to_disk`](/api/language#to_disk) **Usage:**
 [Models](/usage/models#usage),
-[Saving and loading](/usage/training#saving-loading)
+[Saving and loading](/usage/saving-loading#models)
 
 </Infobox>
 
@@ -337,7 +337,7 @@ patterns.
 <Infobox>
 
 **API:** [`Matcher`](/api/matcher), [`PhraseMatcher`](/api/phrasematcher)
-**Usage:** [Rule-based matching](/usage/linguistic-features#rule-based-matching)
+**Usage:** [Rule-based matching](/usage/rule-based-matching)
 
 </Infobox>
 

website/meta/universe.json

@@ -384,7 +384,7 @@
             "id": "matcher-explorer",
             "title": "Rule-based Matcher Explorer",
             "slogan": "Test spaCy's rule-based Matcher by creating token patterns interactively",
-            "description": "Test spaCy's rule-based `Matcher` by creating token patterns interactively and running them over your text. Each token can set multiple attributes like text value, part-of-speech tag or boolean flags. The token-based view lets you explore how spaCy processes your text – and why your pattern matches, or why it doesn't. For more details on rule-based matching, see the [documentation](https://spacy.io/usage/linguistic-features#rule-based-matching).",
+            "description": "Test spaCy's rule-based `Matcher` by creating token patterns interactively and running them over your text. Each token can set multiple attributes like text value, part-of-speech tag or boolean flags. The token-based view lets you explore how spaCy processes your text – and why your pattern matches, or why it doesn't. For more details on rule-based matching, see the [documentation](https://spacy.io/usage/rule-based-matching).",
             "image": "https://explosion.ai/assets/img/demos/matcher.png",
             "thumb": "https://i.imgur.com/rPK4AGt.jpg",
             "url": "https://explosion.ai/demos/matcher",