---
title: What's New in v3.1
teaser: New features and how to upgrade
menu:
  - ['New Features', 'features']
  - ['Upgrading Notes', 'upgrading']
---

## New Features {#features hidden="true"}

<!-- TODO: intro -->

### Using predicted annotations during training {#predicted-annotations-training}

<!-- TODO: write -->

<Project id="pipelines/tagger_parser_predicted_annotations">

This project shows how to use the `token.dep` attribute predicted by the parser
as a feature for a subsequent tagger component in the pipeline.

</Project>

### SpanCategorizer for predicting arbitrary and overlapping spans {#spancategorizer tag="experimental"}

A common task in applied NLP is extracting spans of texts from documents,
including longer phrases or nested expressions. Named entity recognition isn't
the right tool for this problem, since an entity recognizer typically predicts
single token-based tags that are very sensitive to boundaries. This is effective
for proper nouns and self-contained expressions, but less useful for other types
of phrases or overlapping spans. The new
[`SpanCategorizer`](/api/spancategorizer) component and
[SpanCategorizer](/api/architectures#spancategorizer) architecture let you label
arbitrary and potentially overlapping spans of texts. A span categorizer
consists of two parts: a [suggester function](/api/spancategorizer#suggesters)
that proposes candidate spans, which may or may not overlap, and a labeler model
that predicts zero or more labels for each candidate. The predicted spans are
available via the [`Doc.spans`](/api/doc#spans) container.

<!-- TODO: example, getting started (init config?), maybe project template -->

<Infobox title="Tip: Create data with Prodigy's new span annotation UI">

<!-- TODO: screenshot -->

The upcoming version of our annotation tool [Prodigy](https://prodi.gy)
(currently available as a [pre-release](https://support.prodi.gy/t/3861) for all
users) features a [new workflow and UI](https://support.prodi.gy/t/3861) for
annotating overlapping and nested spans. You can use it to create training data
for spaCy's `SpanCategorizer` component.

</Infobox>

### Update the entity recognizer with partial incorrect annotations {#negative-samples}

> #### config.cfg (excerpt)
>
> ```ini
> [components.ner]
> factory = "ner"
> incorrect_spans_key = "incorrect_spans"
> moves = null
> update_with_oracle_cut_size = 100
> ```

The [`EntityRecognizer`](/api/entityrecognizer) can now be updated with known
incorrect annotations, which lets you take advantage of partial and sparse data.
For example, you'll be able to use the information that certain spans of text
are definitely **not** `PERSON` entities, without having to provide the
complete-gold standard annotations for the given example. The incorrect span
annotations can be added via the [`Doc.spans`](/api/doc#spans) in the training
data under the key defined as
[`incorrect_spans_key`](/api/entityrecognizer#init) in the component config.

<!-- TODO: more details and/or example project? -->

### New pipeline packages for Catalan and Danish {#pipeline-packages}

<!-- TODO: intro and update with final numbers -->

| Package                                           | Language | Tagger | Parser |  NER |
| ------------------------------------------------- | -------- | -----: | -----: | ---: |
| [`ca_core_news_sm`](/models/ca#ca_core_news_sm)   | Catalan  |        |        |      |
| [`ca_core_news_md`](/models/ca#ca_core_news_md)   | Catalan  |        |        |      |
| [`ca_core_news_lg`](/models/ca#ca_core_news_lg)   | Catalan  |        |        |      |
| [`ca_core_news_trf`](/models/ca#ca_core_news_trf) | Catalan  |        |        |      |
| [`da_core_news_trf`](/models/da#da_core_news_trf) | Danish   |        |        |      |

### Resizable text classification architectures {#resizable-textcat}

<!-- TODO: write -->

### CLI command to assemble pipeline from config {#assemble}

The [`spacy assemble`](/api/cli#assemble) command lets you assemble a pipeline
from a config file without additional training. It can be especially useful for
creating a blank pipeline with a custom tokenizer, rule-based components or word
vectors.

```cli
$ python -m spacy assemble config.cfg ./output
```

### Support for streaming large or infinite corpora {#streaming-corpora}

<!-- TODO: write -->

### New lemmatizers for Catalan and Italian {#pos-lemmatizers}

<!-- TODO: write -->

## Notes about upgrading from v3.0 {#upgrading}

<!-- TODO: this could just be a bullet-point list mentioning stuff like the spacy_version, vectors initialization etc. -->