diff --git a/netlify.toml b/netlify.toml
index 3c17b876c..e860e4bf4 100644
--- a/netlify.toml
+++ b/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},
]
diff --git a/website/docs/usage/101/_vectors-similarity.md b/website/docs/usage/101/_vectors-similarity.md
index cf5b70af2..2a8733f41 100644
--- a/website/docs/usage/101/_vectors-similarity.md
+++ b/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
diff --git a/website/docs/usage/linguistic-features.md b/website/docs/usage/linguistic-features.md
index 4077cf293..af07a438f 100644
--- a/website/docs/usage/linguistic-features.md
+++ b/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)
diff --git a/website/docs/usage/v2-1.md b/website/docs/usage/v2-1.md
index 4a8ef5a37..8d310f1a4 100644
--- a/website/docs/usage/v2-1.md
+++ b/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
diff --git a/website/gatsby-node.js b/website/gatsby-node.js
index 56a65aeae..b5d8c22c3 100644
--- a/website/gatsby-node.js
+++ b/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 },
- },
- })
- })
})
)
})
diff --git a/website/src/templates/docs.js b/website/src/templates/docs.js
index 7bb62fd21..8343a16a8 100644
--- a/website/src/templates/docs.js
+++ b/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)
diff --git a/website/src/templates/models.js b/website/src/templates/models.js
index 9c6f595da..b9658dacd 100644
--- a/website/src/templates/models.js
+++ b/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 (
<>
-
- {isStarters && (
-
-
- 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).
-
-
- )}
+