Update docs [ci skip]

website/docs/models/index.md

@@ -6,32 +6,18 @@ menu:
   - ['Conventions', 'conventions']
 ---
 
-<!-- Update page, refer to new /api/architectures and training docs -->
+<!-- TODO: include interactive demo -->
-
-This directory includes two types of packages:
-
-1. **Trained pipelines:** General-purpose spaCy pipelines to predict named
-   entities, part-of-speech tags and syntactic dependencies. Can be used
-   out-of-the-box and fine-tuned on more specific data.
-2. **Starters:** Transfer learning starter packs with pretrained weights you can
-   initialize your pipeline models with to achieve better accuracy. They can
-   include word vectors (which will be used as features during training) or
-   other pretrained representations like BERT. These packages don't include
-   components for specific tasks like NER or text classification and are
-   intended to be used as base models when training your own models.
 
 ### Quickstart {hidden="true"}
 
+> #### 📖 Installation and usage
+>
+> For more details on how to use trained pipelines with spaCy, see the
+> [usage guide](/usage/models).
+
 import QuickstartModels from 'widgets/quickstart-models.js'
 
-<QuickstartModels title="Quickstart" id="quickstart" description="Install a default model, get the code to load it from within spaCy and test it." />
+<QuickstartModels id="quickstart" />
-
-<Infobox title="Installation and usage" emoji="📖">
-
-For more details on how to use trained pipelines with spaCy, see the
-[usage guide](/usage/models).
-
-</Infobox>
 
 ## Package naming conventions {#conventions}
 

website/docs/usage/_benchmarks-models.md

@@ -1,13 +1,13 @@
 import { Help } from 'components/typography'; import Link from 'components/link'
 
-<!-- TODO: update numbers -->
+<!-- TODO: update numbers, add note on previous NER evaluation issues -->
 
 <figure>
 
 | Pipeline                                                   | Parser | Tagger |  NER | WPS<br />CPU <Help>words per second on CPU, higher is better</Help> | WPS<br/>GPU <Help>words per second on GPU, higher is better</Help> |
 | ---------------------------------------------------------- | -----: | -----: | ---: | ------------------------------------------------------------------: | -----------------------------------------------------------------: |
 | [`en_core_web_trf`](/models/en#en_core_web_trf) (spaCy v3) |        |        |      |                                                                     |                                                                 6k |
-| [`en_core_web_lg`](/models/en#en_core_web_lg) (spaCy v3)   |   92.1 |   97.4 | 87.0 |                                                                  7k |                                                                    |
+| [`en_core_web_lg`](/models/en#en_core_web_lg) (spaCy v3)   |   92.2 |   97.4 | 85.8 |                                                                  7k |                                                                    |
 | `en_core_web_lg` (spaCy v2)                                |   91.9 |   97.2 | 85.9 |                                                                 10k |                                                                    |
 
 <figcaption class="caption">

website/docs/usage/linguistic-features.md

@@ -970,8 +970,8 @@ import spacy
 from spacy.tokenizer import Tokenizer
 
 special_cases = {":)": [{"ORTH": ":)"}]}
-prefix_re = re.compile(r'''^[\[\("']''')
+prefix_re = re.compile(r'''^[\\[\\("']''')
-suffix_re = re.compile(r'''[\]\)"']$''')
+suffix_re = re.compile(r'''[\\]\\)"']$''')
 infix_re = re.compile(r'''[-~]''')
 simple_url_re = re.compile(r'''^https?://''')
 
@@ -1592,7 +1592,9 @@ print("After:", [(token.text, token._.is_musician) for token in doc])
 A [`Doc`](/api/doc) object's sentences are available via the `Doc.sents`
 property. To view a `Doc`'s sentences, you can iterate over the `Doc.sents`, a
 generator that yields [`Span`](/api/span) objects. You can check whether a `Doc`
-has sentence boundaries with the `doc.is_sentenced` attribute.
+has sentence boundaries by calling
+[`Doc.has_annotation`](/api/doc#has_annotation) with the attribute name
+`"SENT_START"`.
 
 ```python
 ### {executable="true"}
@@ -1600,7 +1602,7 @@ import spacy
 
 nlp = spacy.load("en_core_web_sm")
 doc = nlp("This is a sentence. This is another sentence.")
-assert doc.is_sentenced
+assert doc.has_annotation("SENT_START")
 for sent in doc.sents:
     print(sent.text)
 ```

website/src/templates/models.js

@@ -403,8 +403,8 @@ const Models = ({ pageContext, repo, children }) => {
                 <Section>
                     <p>
                         Starter packs are pretrained weights you can initialize your models with to
-                        achieve better accuracy. They can include word vectors (which will be used
+                        achieve better accuracy, like word vectors (which will be used as features
-                        as features during training) or other pretrained representations like BERT.
+                        during training).
                     </p>
                 </Section>
             )}