Remove docs references to starters for now (see #6262) [ci skip]

netlify.toml

@@ -58,5 +58,7 @@ redirects = [
     {from = "/universe", to = "/universe/project/:id", query = {id = ":id"}, force = true},
     {from = "/universe", to = "/universe/category/:category", query = {category = ":category"}, force = true},
     # Renamed universe projects
-    {from = "/universe/project/spacy-pytorch-transformers", to = "/universe/project/spacy-transformers", force = true}
+    {from = "/universe/project/spacy-pytorch-transformers", to = "/universe/project/spacy-transformers", force = true},
+    # Old model pages
+    {from = "/models/en-starters", to = "/models/en", force = true},
 ]

website/docs/usage/101/_vectors-similarity.md

@@ -68,8 +68,8 @@ representation consists of 300 dimensions of `0`, which means it's practically
 nonexistent. If your application will benefit from a **large vocabulary** with
 more vectors, you should consider using one of the larger pipeline packages or
 loading in a full vector package, for example,
-[`en_vectors_web_lg`](/models/en-starters#en_vectors_web_lg), which includes
-over **1 million unique vectors**.
+[`en_core_web_lg`](/models/en#en_core_web_lg), which includes **685k unique
+vectors**.
 
 spaCy is able to compare two objects, and make a prediction of **how similar
 they are**. Predicting similarity is useful for building recommendation systems

website/docs/usage/linguistic-features.md

@@ -1859,9 +1859,8 @@ pruning the vectors will be taken care of automatically if you set the `--prune`
 flag. You can also do it manually in the following steps:
 
 1. Start with a **word vectors package** that covers a huge vocabulary. For
-   instance, the [`en_vectors_web_lg`](/models/en-starters#en_vectors_web_lg)
-   starter provides 300-dimensional GloVe vectors for over 1 million terms of
-   English.
+   instance, the [`en_core_web_lg`](/models/en#en_core_web_lg) package provides
+   300-dimensional GloVe vectors for 685k terms of English.
 2. If your vocabulary has values set for the `Lexeme.prob` attribute, the
    lexemes will be sorted by descending probability to determine which vectors
    to prune. Otherwise, lexemes will be sorted by their order in the `Vocab`.
@@ -1869,7 +1868,7 @@ flag. You can also do it manually in the following steps:
    vectors you want to keep.
 
 ```python
-nlp = spacy.load('en_vectors_web_lg')
+nlp = spacy.load("en_core_web_lg")
 n_vectors = 105000  # number of vectors to keep
 removed_words = nlp.vocab.prune_vectors(n_vectors)
 

website/docs/usage/v2-1.md

@@ -22,7 +22,7 @@ For more details and a behind-the-scenes look at the new release,
 >
 > ```bash
 > $ python -m spacy pretrain ./raw_text.jsonl
-> en_vectors_web_lg ./pretrained-model
+> en_core_web_lg ./pretrained-model
 > ```
 
 spaCy v2.1 introduces a new CLI command, `spacy pretrain`, that can make your

website/gatsby-node.js

@@ -226,8 +226,6 @@ exports.createPages = ({ graphql, actions }) => {
 
                 const langs = result.data.site.siteMetadata.languages
                 const modelLangs = langs.filter(({ models }) => models && models.length)
-                const starterLangs = langs.filter(({ starters }) => starters && starters.length)
-
                 modelLangs.forEach(({ code, name, models, example, has_examples }, i) => {
                     const slug = `/models/${code}`
                     const next = i < modelLangs.length - 1 ? modelLangs[i + 1] : null
@@ -247,28 +245,6 @@ exports.createPages = ({ graphql, actions }) => {
                         },
                     })
                 })
-
-                starterLangs.forEach(({ code, name, starters }, i) => {
-                    const slug = `/models/${code}-starters`
-                    const next = i < starterLangs.length - 1 ? starterLangs[i + 1] : null
-                    createPage({
-                        path: slug,
-                        component: DEFAULT_TEMPLATE,
-                        context: {
-                            id: `${code}-starters`,
-                            slug: slug,
-                            isIndex: false,
-                            title: name,
-                            section: 'models',
-                            sectionTitle: sections.models.title,
-                            theme: sections.models.theme,
-                            next: next
-                                ? { title: next.name, slug: `/models/${next.code}-starters` }
-                                : null,
-                            meta: { models: starters, isStarters: true },
-                        },
-                    })
-                })
             })
         )
     })

website/src/templates/docs.js

@@ -52,19 +52,6 @@ const Docs = ({ pageContext, children }) => (
                             id: model,
                         })),
                     }))
-                if (sidebar.items.length > 2) {
-                    sidebar.items[2].items = languages
-                        .filter(({ starters }) => starters && starters.length)
-                        .map(lang => ({
-                            text: lang.name,
-                            url: `/models/${lang.code}-starters`,
-                            isActive: id === `${lang.code}-starters`,
-                            menu: lang.starters.map(model => ({
-                                text: model,
-                                id: model,
-                            })),
-                        }))
-                }
             }
             const sourcePath = source ? github(source) : null
             const currentSource = getCurrentSource(slug, isIndex)

website/src/templates/models.js

@@ -374,7 +374,7 @@ const Models = ({ pageContext, repo, children }) => {
     const [initialized, setInitialized] = useState(false)
     const [compatibility, setCompatibility] = useState({})
     const { id, title, meta } = pageContext
-    const { models, isStarters } = meta
+    const { models } = meta
     const baseUrl = `https://raw.githubusercontent.com/${repo}/master`
 
     useEffect(() => {
@@ -388,26 +388,9 @@ const Models = ({ pageContext, repo, children }) => {
         }
     }, [initialized, baseUrl])
 
-    const modelTitle = title
-    const modelTeaser = `Available trained pipelines for ${title}`
-    const starterTitle = `${title} starters`
-    const starterTeaser = `Available transfer learning starter packs for ${title}`
-
     return (
         <>
-            <Title
-                title={isStarters ? starterTitle : modelTitle}
-                teaser={isStarters ? starterTeaser : modelTeaser}
-            />
-            {isStarters && (
-                <Section>
-                    <p>
-                        Starter packs are pretrained weights you can initialize your models with to
-                        achieve better accuracy, like word vectors (which will be used as features
-                        during training).
-                    </p>
-                </Section>
-            )}
+            <Title title={title} teaser={`Available trained pipelines for ${title}`} />
             <StaticQuery
                 query={query}
                 render={({ site }) =>