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194 lines
8.2 KiB
Markdown
194 lines
8.2 KiB
Markdown
---
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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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By default, components are updated in isolation during training, which means
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that they don't see the predictions of any earlier components in the pipeline.
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The new
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[`[training.annotating_components]`](/usage/training#annotating-components)
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config setting lets you specify pipeline component names that should set
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annotations on the predicted docs during training. This makes it easy to use the
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predictions of a previous component in the pipeline as features for a subsequent
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component, e.g. the dependency labels in the tagger:
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```ini
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### config.cfg (excerpt) {highlight="7,12"}
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[nlp]
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pipeline = ["parser", "tagger"]
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[components.tagger.model.tok2vec.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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width = ${components.tagger.model.tok2vec.encode.width}
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attrs = ["NORM","DEP"]
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rows = [5000,2500]
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include_static_vectors = false
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[training]
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annotating_components = ["parser"]
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```
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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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[](https://support.prodi.gy/t/3861)
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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 complete
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gold-standard annotations for the given example. The incorrect span annotations
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can be added via the [`Doc.spans`](/api/doc#spans) in the training data under
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the key defined as [`incorrect_spans_key`](/api/entityrecognizer#init) in the
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component config.
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```python
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train_doc = nlp.make_doc("Barack Obama was born in Hawaii.")
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# The doc.spans key can be defined in the config
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train_doc.spans["incorrect_spans"] = [
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Span(doc, 0, 2, label="ORG"),
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Span(doc, 5, 6, label="PRODUCT")
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]
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```
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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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spaCy v3.1 adds 5 new pipeline packages, including a new core family for Catalan
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and a new transformer-based pipeline for Danish using the
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[`danish-bert-botxo`](http://huggingface.co/Maltehb/danish-bert-botxo) weights.
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See the [models directory](/models) for an overview of all available trained
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pipelines and the [training guide](/usage/training) for details on how to train
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your own.
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<!-- TODO: thank contributors 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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Previously, the [`TextCategorizer`](/api/textcategorizer) architectures could
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not be resized, meaning that you couldn't add new labels to an already trained
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model. In spaCy v3.1, the [TextCatCNN](/api/architectures#TextCatCNN) and
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[TextCatBOW](/api/architectures#TextCatBOW) architectures are now resizable,
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while ensuring that the predictions for the old labels remain the same.
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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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### Pretty pipeline package READMEs {#package-readme}
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The [`spacy package`](/api/cli#package) command now auto-generates a pretty
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`README.md` based on the pipeline information defined in the `meta.json`. This
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includes a table with a general overview, as well as the label scheme and
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accuracy figures, if available. For an example, see the
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[model releases](https://github.com/explosion/spacy-models/releases).
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### Support for streaming large or infinite corpora {#streaming-corpora}
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> #### config.cfg (excerpt)
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>
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> ```ini
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> [training]
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> max_epochs = -1
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> ```
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The training process now supports streaming large or infinite corpora
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out-of-the-box, which can be controlled via the
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[`[training.max_epochs]`](/api/data-formats#training) config setting. Setting it
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to `-1` means that the train corpus should be streamed rather than loaded into
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memory with no shuffling within the training loop. For details on how to
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implement a custom corpus loader, e.g. to stream in data from a remote storage,
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see the usage guide on
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[custom data reading](/usage/training#custom-code-readers-batchers).
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When streaming a corpus, only the first 100 examples will be used for
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[initialization](/usage/training#config-lifecycle). This is no problem if you're
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training a component like the text classifier with data that specifies all
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available labels in every example. If necessary, you can use the
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[`init labels`](/api/cli#init-labels) command to pre-generate the labels for
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your components using a representative sample so the model can be initialized
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correctly before training.
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### New lemmatizers for Catalan and Italian {#pos-lemmatizers}
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The trained pipelines for [Catalan](/models/ca) and [Italian](/models/it) now
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include lemmatizers that use the predicted part-of-speech tags as part of the
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lookup lemmatization for higher lemmatization accuracy. If you're training your
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own pipelines for these languages and you want to include a lemmatizer, make
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sure you have the
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[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) package
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installed, which provides the relevant tables.
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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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