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<!--- Provide a general summary of your changes in the title. --> ## Description The new website is implemented using [Gatsby](https://www.gatsbyjs.org) with [Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This allows authoring content in **straightforward Markdown** without the usual limitations. Standard elements can be overwritten with powerful [React](http://reactjs.org/) components and wherever Markdown syntax isn't enough, JSX components can be used. Hopefully, this update will also make it much easier to contribute to the docs. Once this PR is merged, I'll implement auto-deployment via [Netlify](https://netlify.com) on a specific branch (to avoid building the website on every PR). There's a bunch of other cool stuff that the new setup will allow us to do – including writing front-end tests, service workers, offline support, implementing a search and so on. This PR also includes various new docs pages and content. Resolves #3270. Resolves #3222. Resolves #2947. Resolves #2837. ### Types of change enhancement ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
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| title | teaser | tag | source |
|---|---|---|---|
| GoldParse | A collection for training annotations | class | spacy/gold.pyx |
GoldParse.__init__
Create a GoldParse.
| Name | Type | Description |
|---|---|---|
doc |
Doc |
The document the annotations refer to. |
words |
iterable | A sequence of unicode word strings. |
tags |
iterable | A sequence of strings, representing tag annotations. |
heads |
iterable | A sequence of integers, representing syntactic head offsets. |
deps |
iterable | A sequence of strings, representing the syntactic relation types. |
entities |
iterable | A sequence of named entity annotations, either as BILUO tag strings, or as (start_char, end_char, label) tuples, representing the entity positions. |
| RETURNS | GoldParse |
The newly constructed object. |
GoldParse.__len__
Get the number of gold-standard tokens.
| Name | Type | Description |
|---|---|---|
| RETURNS | int | The number of gold-standard tokens. |
GoldParse.is_projective
Whether the provided syntactic annotations form a projective dependency tree.
| Name | Type | Description |
|---|---|---|
| RETURNS | bool | Whether annotations form projective tree. |
Attributes
| Name | Type | Description |
|---|---|---|
tags |
list | The part-of-speech tag annotations. |
heads |
list | The syntactic head annotations. |
labels |
list | The syntactic relation-type annotations. |
ents |
list | The named entity annotations. |
cand_to_gold |
list | The alignment from candidate tokenization to gold tokenization. |
gold_to_cand |
list | The alignment from gold tokenization to candidate tokenization. |
cats 2 |
list | Entries in the list should be either a label, or a (start, end, label) triple. The tuple form is used for categories applied to spans of the document. |
Utilities
gold.biluo_tags_from_offsets
Encode labelled spans into per-token tags, using the BILUO scheme (Begin/In/Last/Unit/Out).
Returns a list of unicode strings, describing the tags. Each tag string will be
of the form of either "", "O" or "{action}-{label}", where action is one
of "B", "I", "L", "U". The string "-" is used where the entity offsets
don't align with the tokenization in the Doc object. The training algorithm
will view these as missing values. O denotes a non-entity token. B denotes
the beginning of a multi-token entity, I the inside of an entity of three or
more tokens, and L the end of an entity of two or more tokens. U denotes a
single-token entity.
Example
from spacy.gold import biluo_tags_from_offsets doc = nlp(u"I like London.") entities = [(7, 13, "LOC")] tags = biluo_tags_from_offsets(doc, entities) assert tags == ["O", "O", "U-LOC", "O"]
| Name | Type | Description |
|---|---|---|
doc |
Doc |
The document that the entity offsets refer to. The output tags will refer to the token boundaries within the document. |
entities |
iterable | A sequence of (start, end, label) triples. start and end should be character-offset integers denoting the slice into the original string. |
| RETURNS | list | Unicode strings, describing the BILUO tags. |
gold.offsets_from_biluo_tags
Encode per-token tags following the BILUO scheme into entity offsets.
Example
from spacy.gold import offsets_from_biluo_tags doc = nlp(u"I like London.") tags = ["O", "O", "U-LOC", "O"] entities = offsets_from_biluo_tags(doc, tags) assert entities == [(7, 13, "LOC")]
| Name | Type | Description |
|---|---|---|
doc |
Doc |
The document that the BILUO tags refer to. |
entities |
iterable | A sequence of BILUO tags with each tag describing one token. Each tag string will be of the form of either "", "O" or "{action}-{label}", where action is one of "B", "I", "L", "U". |
| RETURNS | list | A sequence of (start, end, label) triples. start and end will be character-offset integers denoting the slice into the original string. |
gold.spans_from_biluo_tags
Encode per-token tags following the BILUO scheme into
Span objects. This can be used to create entity spans from
token-based tags, e.g. to overwrite the doc.ents.
Example
from spacy.gold import offsets_from_biluo_tags doc = nlp(u"I like London.") tags = ["O", "O", "U-LOC", "O"] doc.ents = spans_from_biluo_tags(doc, tags)
| Name | Type | Description |
|---|---|---|
doc |
Doc |
The document that the BILUO tags refer to. |
entities |
iterable | A sequence of BILUO tags with each tag describing one token. Each tag string will be of the form of either "", "O" or "{action}-{label}", where action is one of "B", "I", "L", "U". |
| RETURNS | list | A sequence of Span objects with added entity labels. |