---
title: What's New in v2.1
teaser: New features, backwards incompatibilities and migration guide
menu:
  - ['New Features', 'features']
  - ['Backwards Incompatibilities', 'incompat']
---

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

spaCy v2.1 has focussed primarily on stability and performance, solidifying the
design changes introduced in [v2.0](/usage/v2). As well as smaller models,
faster runtime, and many bug-fixes, v2.1 also introduces experimental support
for some exciting new NLP innovations. For the full changelog, see the
[release notes on GitHub](https://github.com/explosion/spaCy/releases/tag/v2.1.0).

### BERT/ULMFit/Elmo-style pre-training

> #### Example
>
> ```bash
> $ python -m spacy pretrain ./raw_text.jsonl
> en_vectors_web_lg ./pretrained-model
> ```

spaCy v2.1 introduces a new CLI command, `spacy pretrain`, that can make your
models much more accurate. It's especially useful when you have **limited
training data**. The `spacy pretrain` command lets you use transfer learning to
initialize your models with information from raw text, using a language model
objective similar to the one used in Google's BERT system. We've taken
particular care to ensure that pretraining works well even with spaCy's small
default architecture sizes, so you don't have to compromise on efficiency to use
it.

<Infobox>

**API:** [`spacy pretrain`](/api/cli#pretrain) **Usage: **
[Improving accuracy with transfer learning](/usage/training#transfer-learning)

</Infobox>

### Extended match pattern API

> #### Example
>
> ```python
> # Matches "love cats" or "likes flowers"
> pattern1 = [{"LEMMA": {"IN": ["like", "love"]}}, {"POS": "NOUN"}]
> # Matches tokens of length >= 10
> pattern2 = [{"LENGTH": {">=": 10}}]
> # Matches custom attribute with regex
> pattern3 = [{"_": {"country": {"REGEX": "^([Uu](\\.?|nited) ?[Ss](\\.?|tates)"}}}]
> ```

Instead of mapping to a single value, token patterns can now also map to a
**dictionary of properties**. For example, to specify that the value of a lemma
should be part of a list of values, or to set a minimum character length. It now
also supports a `REGEX` property, as well as set membership via `IN` and
`NOT_IN`, custom extension attributes via `_` and rich comparison for numeric
values.

<Infobox>

**API:** [`Matcher`](/api/matcher) **Usage: **
[Extended pattern syntax and attributes](/usage/rule-based-matching#adding-patterns-attributes-extended),
[Regular expressions](/usage/rule-based-matching#regex)

</Infobox>

### Easy rule-based entity recognition

> #### Example
>
> ```python
> from spacy.pipeline import EntityRuler
> ruler = EntityRuler(nlp)
> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
> nlp.add_pipe(ruler, before="ner")
> ```

The `EntityRuler` is an exciting new component that lets you add named entities
based on pattern dictionaries, and makes it easy to combine rule-based and
statistical named entity recognition for even more powerful models. Entity rules
can be phrase patterns for exact string matches, or token patterns for full
flexibility.

<Infobox>

**API:** [`EntityRuler`](/api/entityruler) **Usage: **
[Rule-based entity recognition](/usage/rule-based-matching#entityruler)

</Infobox>

### Phrase matching with other attributes

> #### Example
>
> ```python
> matcher = PhraseMatcher(nlp.vocab, attr="POS")
> matcher.add("PATTERN", None, nlp(u"I love cats"))
> doc = nlp(u"You like dogs")
> matches = matcher(doc)
> ```

By default, the `PhraseMatcher` will match on the verbatim token text, e.g.
`Token.text`. By setting the `attr` argument on initialization, you can change
**which token attribute the matcher should use** when comparing the phrase
pattern to the matched `Doc`. For example, `LOWER` for case-insensitive matches
or `POS` for finding sequences of the same part-of-speech tags.

<Infobox>

**API:** [`PhraseMatcher`](/api/phrasematcher) **Usage: **
[Matching on other token attributes](/usage/rule-based-matching#phrasematcher-attrs)

</Infobox>

### Components and languages via entry points

> #### Example
>
> ```python
> from setuptools import setup
> setup(
>     name="custom_extension_package",
>     entry_points={
>         "spacy_factories": ["your_component = component:ComponentFactory"]
>         "spacy_languages": ["xyz = language:XYZLanguage"]
>    }
> )
> ```

Using entry points, model packages and extension packages can now define their
own `"spacy_factories"` and `"spacy_languages"`, which will be added to the
built-in factories and languages. If a package in the same environment exposes
spaCy entry points, all of this happens automatically and no further user action
is required.

<Infobox>

**Usage:** [Using entry points](/usage/saving-loading#entry-points)

</Infobox>

### Retokenizer for merging and splitting

> #### Example
>
> ```python
> doc = nlp(u"I like David Bowie")
> with doc.retokenize() as retokenizer:
>     attrs = {"LEMMA": u"David Bowie"}
>     retokenizer.merge(doc[2:4], attrs=attrs)
> ```

The new `Doc.retokenize` context manager allows merging spans of multiple tokens
into one single token, and splitting single tokens into multiple tokens.
Modifications to the `Doc`'s tokenization are stored, and then made all at once
when the context manager exits. This is much more efficient, and less
error-prone. `Doc.merge` and `Span.merge` still work, but they're considered
deprecated.

<Infobox>

**API:** [`Doc.retokenize`](/api/doc#retokenize),
[`Retokenizer.merge`](/api/doc#retokenizer.merge),
[`Retokenizer.split`](/api/doc#retokenizer.split)<br />**Usage:
**[Merging and splitting](/usage/linguistic-features#retokenization)

</Infobox>

### Improved documentation

Although it looks pretty much the same, we've rebuilt the entire documentation
using [Gatsby](https://www.gatsbyjs.org/) and [MDX](https://mdxjs.com/). It's
now an even faster progressive web app and allows us to write all content
entirely **in Markdown**, without having to compromise on easy-to-use custom UI
components. We're hoping that the Markdown source will make it even easier to
contribute to the documentation. For more details, check out the
[styleguide](/styleguide) and
[source](https://github.com/explosion/spaCy/tree/master/website). While
converting the pages to Markdown, we've also fixed a bunch of typos, improved
the existing pages and added some new content:

- **Usage Guide:** [Rule-based Matching](/usage/rule-based-matching)<br/>How to
  use the `Matcher`, `PhraseMatcher` and the new `EntityRuler`, and write
  powerful components to combine statistical models and rules.
- **Usage Guide:** [Saving and Loading](/usage/saving-loading)<br/>Everything
  you need to know about serialization, and how to save and load pipeline
  components, package your spaCy models as Python modules and use entry points.
- **Usage Guide: **
  [Merging and Splitting](/usage/linguistic-features#retokenization)<br />How to
  retokenize a `Doc` using the new `retokenize` context manager and merge spans
  into single tokens and split single tokens into multiple.
- **Universe:** [Videos](/universe/category/videos) and
  [Podcasts](/universe/category/podcasts)
- **API:** [`EntityRuler`](/api/entityruler)
- **API:** [`SentenceSegmenter`](/api/sentencesegmenter)
- **API:** [Pipeline functions](/api/pipeline-functions)

## Backwards incompatibilities {#incompat}

<Infobox title="Important note on models" variant="warning">

If you've been training **your own models**, you'll need to **retrain** them
with the new version. Also don't forget to upgrade all models to the latest
versions. Models for v2.0.x aren't compatible with models for v2.1.x. To check
if all of your models are up to date, you can run the
[`spacy validate`](/api/cli#validate) command.

</Infobox>

- While the [`Matcher`](/api/matcher) API is fully backwards compatible, its
  algorithm has changed to fix a number of bugs and performance issues. This
  means that the `Matcher` in v2.1.x may produce different results compared to
  the `Matcher` in v2.0.x.

- For better compatibility with the Universal Dependencies data, the lemmatizer
  now preserves capitalization, e.g. for proper nouns. See
  [this issue](https://github.com/explosion/spaCy/issues/3256) for details.

- The built-in rule-based sentence boundary detector is now only called
  `"sentencizer"` – the name `"sbd"` is deprecated.

  ```diff
  - sentence_splitter = nlp.create_pipe("sbd")
  + sentence_splitter = nlp.create_pipe("sentencizer")
  ```

- The `Doc.print_tree` method is now deprecated. If you need a custom nested
  JSON representation of a `Doc` object, you might want to write your own helper
  function. For a simple and consistent JSON representation of the `Doc` object
  and its annotations, you can now use the [`Doc.to_json`](/api/doc#to_json)
  method. Going forward, this method will output the same format as the JSON
  training data expected by [`spacy train`](/api/cli#train).

- The [`spacy train`](/api/cli#train) command now lets you specify a
  comma-separated list of pipeline component names, instead of separate flags
  like `--no-parser` to disable components. This is more flexible and also
  handles custom components out-of-the-box.

  ```diff
  - $ spacy train en /output train_data.json dev_data.json --no-parser
  + $ spacy train en /output train_data.json dev_data.json --pipeline tagger,ner
  ```

- The [`spacy init-model`](/api/cli#init-model) command now uses a `--jsonl-loc`
  argument to pass in a a newline-delimited JSON (JSONL) file containing one
  lexical entry per line instead of a separate `--freqs-loc` and
  `--clusters-loc`.

  ```diff
  - $ spacy init-model en ./model --freqs-loc ./freqs.txt --clusters-loc ./clusters.txt
  + $ spacy init-model en ./model --jsonl-loc ./vocab.jsonl
  ```

- Also note that some of the model licenses have changed:
  [`it_core_news_sm`](/models/it#it_core_news_sm) is now correctly licensed
  under CC BY-NC-SA 3.0, and all [English](/models/en) and [German](/models/de)
  models are now published under the MIT license.