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Merge branch 'develop' into nightly.spacy.io

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106
.github/contributors/jmargeta.md vendored Normal file
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@ -0,0 +1,106 @@
# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Jan Margeta |
| Company name (if applicable) | KardioMe |
| Title or role (if applicable) | Founder |
| Date | 2020-10-16 |
| GitHub username | jmargeta |
| Website (optional) | kardio.me |

4
netlify.toml
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@ -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},
]

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spacy/cli/templates/quickstart_training_recommendations.yml
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@ -1,17 +1,20 @@
# Recommended settings and available resources for each language, if available.
# Not all languages have recommended word vectors or transformers and for some,
# the recommended transformer for efficiency and accuracy may be the same.
en:
word_vectors: en_vectors_web_lg
ar:
word_vectors: null
transformer:
efficiency:
name: roberta-base
name: asafaya/bert-base-arabic
size_factor: 3
accuracy:
name: roberta-base
name: asafaya/bert-base-arabic
size_factor: 3
da:
word_vectors: da_core_news_lg
transformer: null
de:
word_vectors: null
word_vectors: de_core_news_lg
transformer:
efficiency:
name: bert-base-german-cased
@ -19,17 +22,26 @@ de:
accuracy:
name: bert-base-german-cased
size_factor: 3
fr:
word_vectors: null
el:
word_vectors: el_core_news_lg
transformer:
efficiency:
name: camembert-base
name: nlpaueb/bert-base-greek-uncased-v1
size_factor: 3
accuracy:
name: camembert-base
name: nlpaueb/bert-base-greek-uncased-v1
size_factor: 3
en:
word_vectors: en_core_web_lg
transformer:
efficiency:
name: roberta-base
size_factor: 3
accuracy:
name: roberta-base
size_factor: 3
es:
word_vectors: null
word_vectors: es_core_news_lg
transformer:
efficiency:
name: dccuchile/bert-base-spanish-wwm-cased
@ -37,15 +49,6 @@ es:
accuracy:
name: dccuchile/bert-base-spanish-wwm-cased
size_factor: 3
sv:
word_vectors: null
transformer:
efficiency:
name: KB/bert-base-swedish-cased
size_factor: 3
accuracy:
name: KB/bert-base-swedish-cased
size_factor: 3
fi:
word_vectors: null
transformer:
@ -55,14 +58,65 @@ fi:
accuracy:
name: TurkuNLP/bert-base-finnish-cased-v1
size_factor: 3
el:
fr:
word_vectors: fr_core_news_lg
transformer:
efficiency:
name: camembert-base
size_factor: 3
accuracy:
name: camembert-base
size_factor: 3
it:
word_vectors: it_core_news_lg
transformers: null
ja:
word_vectors: ja_core_news_lg
transformers: null
lt:
word_vectors: lt_core_news_lg
transformers: null
nb:
word_vectors: nb_core_news_lg
transformers: null
nl:
word_vectors: nl_core_news_lg
transformer:
efficiency:
name: pdelobelle/robbert-v2-dutch-base
size_factor: 3
accuracy:
name: pdelobelle/robbert-v2-dutch-base
size_factor: 3
pl:
word_vectors: pl_core_news_lg
transformer:
efficiency:
name: dkleczek/bert-base-polish-cased-v1
size_factor: 3
accuracy:
name: dkleczek/bert-base-polish-cased-v1
size_factor: 3
pt:
word_vectors: pt_core_news_lg
transformer:
efficiency:
name: neuralmind/bert-base-portuguese-cased
size_factor: 3
accuracy:
name: neuralmind/bert-base-portuguese-cased
size_factor: 3
ro:
word_vectors: ro_core_news_lg
transformers: null
sv:
word_vectors: null
transformer:
efficiency:
name: nlpaueb/bert-base-greek-uncased-v1
name: KB/bert-base-swedish-cased
size_factor: 3
accuracy:
name: nlpaueb/bert-base-greek-uncased-v1
name: KB/bert-base-swedish-cased
size_factor: 3
tr:
word_vectors: null
@ -74,7 +128,7 @@ tr:
name: dbmdz/bert-base-turkish-cased
size_factor: 3
zh:
word_vectors: null
word_vectors: zh_core_web_lg
transformer:
efficiency:
name: bert-base-chinese
@ -83,39 +137,3 @@ zh:
name: bert-base-chinese
size_factor: 3
has_letters: false
ar:
word_vectors: null
transformer:
efficiency:
name: asafaya/bert-base-arabic
size_factor: 3
accuracy:
name: asafaya/bert-base-arabic
size_factor: 3
pl:
word_vectors: null
transformer:
efficiency:
name: dkleczek/bert-base-polish-cased-v1
size_factor: 3
accuracy:
name: dkleczek/bert-base-polish-cased-v1
size_factor: 3
nl:
word_vectors: null
transformer:
efficiency:
name: pdelobelle/robbert-v2-dutch-base
size_factor: 3
accuracy:
name: pdelobelle/robbert-v2-dutch-base
size_factor: 3
pt:
word_vectors: null
transformer:
efficiency:
name: neuralmind/bert-base-portuguese-cased
size_factor: 3
accuracy:
name: neuralmind/bert-base-portuguese-cased
size_factor: 3

2
spacy/schemas.py
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@ -257,7 +257,7 @@ class TokenPattern(BaseModel):
class TokenPatternSchema(BaseModel):
pattern: List[TokenPattern] = Field(..., minItems=1)
pattern: List[TokenPattern] = Field(..., min_items=1)
class Config:
extra = "forbid"

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spacy/tests/regression/test_issue6258.py Normal file
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@ -0,0 +1,14 @@
import pydantic
import pytest
from pydantic import ValidationError
from spacy.schemas import TokenPattern, TokenPatternSchema
def test_issue6258():
"""Test that the non-empty constraint pattern field is respected"""
# These one is valid
TokenPatternSchema(pattern=[TokenPattern()])
# But an empty pattern list should fail to validate
# based on the schema's constraint
with pytest.raises(ValidationError):
TokenPatternSchema(pattern=[])

4
website/docs/usage/101/_vectors-similarity.md
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@ -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

7
website/docs/usage/linguistic-features.md
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@ -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)

2
website/docs/usage/v2-1.md
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@ -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

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website/gatsby-node.js
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@ -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 },
},
})
})
})
)
})

13
website/src/templates/docs.js
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@ -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)

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website/src/templates/models.js
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@ -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 }) =>