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Add "New in v3.1" guide
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Ines Montani
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@ -82,7 +82,7 @@ shortcut for this and instantiate the component using its string name and
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| `moves` | A list of transition names. Inferred from the data if set to `None`, which is the default. ~~Optional[List[str]]~~ |
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| _keyword-only_ | |
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| `update_with_oracle_cut_size` | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. Defaults to `100`. ~~int~~ |
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| `incorrect_spans_key` | Identifies spans that are known to be incorrect entity annotations. The incorrect entity annotations can be stored in the span group, under this key. Defaults to `None`. ~~Optional[str]~~ |
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| `incorrect_spans_key` | Identifies spans that are known to be incorrect entity annotations. The incorrect entity annotations can be stored in the span group in [`Doc.spans`](/api/doc#spans), under this key. Defaults to `None`. ~~Optional[str]~~ |
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## EntityRecognizer.\_\_call\_\_ {#call tag="method"}
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114
website/docs/usage/v3-1.md
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114
website/docs/usage/v3-1.md
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@ -0,0 +1,114 @@
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---
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title: What's New in v3.1
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teaser: New features and how to upgrade
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menu:
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- ['New Features', 'features']
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- ['Upgrading Notes', 'upgrading']
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---
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## New Features {#features hidden="true"}
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<!-- TODO: intro -->
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### Using predicted annotations during training {#predicted-annotations-training}
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<!-- TODO: write -->
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<Project id="pipelines/tagger_parser_predicted_annotations">
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This project shows how to use the `token.dep` attribute predicted by the parser
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as a feature for a subsequent tagger component in the pipeline.
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</Project>
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### SpanCategorizer for predicting arbitrary and overlapping spans {#spancategorizer tag="experimental"}
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A common task in applied NLP is extracting spans of texts from documents,
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including longer phrases or nested expressions. Named entity recognition isn't
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the right tool for this problem, since an entity recognizer typically predicts
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single token-based tags that are very sensitive to boundaries. This is effective
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for proper nouns and self-contained expressions, but less useful for other types
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of phrases or overlapping spans. The new
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[`SpanCategorizer`](/api/spancategorizer) component and
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[SpanCategorizer](/api/architectures#spancategorizer) architecture let you label
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arbitrary and potentially overlapping spans of texts. A span categorizer
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consists of two parts: a [suggester function](/api/spancategorizer#suggesters)
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that proposes candidate spans, which may or may not overlap, and a labeler model
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that predicts zero or more labels for each candidate. The predicted spans are
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available via the [`Doc.spans`](/api/doc#spans) container.
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<!-- TODO: example, getting started (init config?), maybe project template -->
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<Infobox title="Tip: Create data with Prodigy's new span annotation UI">
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<!-- TODO: screenshot -->
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The upcoming version of our annotation tool [Prodigy](https://prodi.gy)
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(currently available as a [pre-release](https://support.prodi.gy/t/3861) for all
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users) features a [new workflow and UI](https://support.prodi.gy/t/3861) for
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annotating overlapping and nested spans. You can use it to create training data
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for spaCy's `SpanCategorizer` component.
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</Infobox>
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### Update the entity recognizer with partial incorrect annotations {#negative-samples}
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> #### config.cfg (excerpt)
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>
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> ```ini
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> [components.ner]
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> factory = "ner"
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> incorrect_spans_key = "incorrect_spans"
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> moves = null
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> update_with_oracle_cut_size = 100
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> ```
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The [`EntityRecognizer`](/api/entityrecognizer) can now be updated with known
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incorrect annotations, which lets you take advantage of partial and sparse data.
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For example, you'll be able to use the information that certain spans of text
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are definitely **not** `PERSON` entities, without having to provide the
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complete-gold standard annotations for the given example. The incorrect span
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annotations can be added via the [`Doc.spans`](/api/doc#spans) in the training
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data under the key defined as
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[`incorrect_spans_key`](/api/entityrecognizer#init) in the component config.
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<!-- TODO: more details and/or example project? -->
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### New pipeline packages for Catalan and Danish {#pipeline-packages}
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<!-- TODO: intro and update with final numbers -->
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| Package | Language | Tagger | Parser | NER |
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| ------------------------------------------------- | -------- | -----: | -----: | ---: |
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| [`ca_core_news_sm`](/models/ca#ca_core_news_sm) | Catalan | | | |
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| [`ca_core_news_md`](/models/ca#ca_core_news_md) | Catalan | | | |
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| [`ca_core_news_lg`](/models/ca#ca_core_news_lg) | Catalan | | | |
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| [`ca_core_news_trf`](/models/ca#ca_core_news_trf) | Catalan | | | |
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| [`da_core_news_trf`](/models/da#da_core_news_trf) | Danish | | | |
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### Resizable text classification architectures {#resizable-textcat}
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<!-- TODO: write -->
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### CLI command to assemble pipeline from config {#assemble}
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The [`spacy assemble`](/api/cli#assemble) command lets you assemble a pipeline
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from a config file without additional training. It can be especially useful for
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creating a blank pipeline with a custom tokenizer, rule-based components or word
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vectors.
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```cli
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$ python -m spacy assemble config.cfg ./output
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```
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### Support for streaming large or infinite corpora {#streaming-corpora}
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<!-- TODO: write -->
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### New lemmatizers for Catalan and Italian {#pos-lemmatizers}
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<!-- TODO: write -->
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## Notes about upgrading from v3.0 {#upgrading}
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<!-- TODO: this could just be a bullet-point list mentioning stuff like the spacy_version, vectors initialization etc. -->
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@ -9,7 +9,8 @@
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{ "text": "Models & Languages", "url": "/usage/models" },
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{ "text": "Facts & Figures", "url": "/usage/facts-figures" },
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{ "text": "spaCy 101", "url": "/usage/spacy-101" },
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{ "text": "New in v3.0", "url": "/usage/v3" }
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{ "text": "New in v3.0", "url": "/usage/v3" },
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{ "text": "New in v3.1", "url": "/usage/v3-1" }
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]
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},
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{
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@ -135,9 +136,7 @@
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},
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{
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"label": "Legacy",
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"items": [
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{ "text": "Legacy functions", "url": "/api/legacy" }
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]
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"items": [{ "text": "Legacy functions", "url": "/api/legacy" }]
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}
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]
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}
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@ -119,8 +119,8 @@ const AlertSpace = ({ nightly, legacy }) => {
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}
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const navAlert = (
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<Link to="/usage/v3" hidden>
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<strong>💥 Out now:</strong> spaCy v3.0
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<Link to="/usage/v3-1" hidden>
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<strong>💥 Out now:</strong> spaCy v3.1
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</Link>
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)
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