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---
title: What's New in v2.0
teaser: New features, backwards incompatibilities and migration guide
menu:
- ['Summary', 'summary']
- ['New Features', 'features']
- ['Backwards Incompatibilities', 'incompat']
- ['Migrating from v1.x', 'migrating']
---
We're very excited to finally introduce spaCy v2.0! On this page, you'll find a
summary of the new features, information on the backwards incompatibilities,
including a handy overview of what's been renamed or deprecated. To help you
make the most of v2.0, we also **re-wrote almost all of the usage guides and API
docs**, and added more [real-world examples](/usage/examples). If you're new to
spaCy, or just want to brush up on some NLP basics and the details of the
library, check out the [spaCy 101 guide](/usage/spacy-101) that explains the
most important concepts with examples and illustrations.
## Summary {#summary}
<Grid cols={2}>
<div>
This release features entirely new **deep learning-powered models** for spaCy's
tagger, parser and entity recognizer. The new models are **10× smaller**, **20%
more accurate** and **even cheaper to run** than the previous generation.
We've also made several usability improvements that are particularly helpful for
**production deployments**. spaCy v2 now fully supports the Pickle protocol,
making it easy to use spaCy with [Apache Spark](https://spark.apache.org/). The
string-to-integer mapping is **no longer stateful**, making it easy to reconcile
annotations made in different processes. Models are smaller and use less memory,
and the APIs for serialization are now much more consistent. Custom pipeline
components let you modify the `Doc` at any stage in the pipeline. You can now
also add your own custom attributes, properties and methods to the `Doc`,
`Token` and `Span`.
</div>
<Infobox title="Table of Contents" id="toc">
- [Summary](#summary)
- [New features](#features)
- [Neural network models](#features-models)
- [Improved processing pipelines](#features-pipelines)
- [Text classification](#features-text-classification)
- [Hash values as IDs](#features-hash-ids)
- [Improved word vectors support](#features-vectors)
- [Saving, loading and serialization](#features-serializer)
- [displaCy visualizer](#features-displacy)
- [Language data and lazy loading](#features-language)
- [Revised matcher API and phrase matcher](#features-matcher)
- [Backwards incompatibilities](#incompat)
- [Migrating from spaCy v1.x](#migrating)
</Infobox>
</Grid>
The main usability improvements you'll notice in spaCy v2.0 are around
**defining, training and loading your own models** and components. The new
neural network models make it much easier to train a model from scratch, or
update an existing model with a few examples. In v1.x, the statistical models
depended on the state of the `Vocab`. If you taught the model a new word, you
would have to save and load a lot of data — otherwise the model wouldn't
correctly recall the features of your new example. That's no longer the case.
Due to some clever use of hashing, the statistical models **never change size**,
even as they learn new vocabulary items. The whole pipeline is also now fully
differentiable. Even if you don't have explicitly annotated data, you can update
spaCy using all the **latest deep learning tricks** like adversarial training,
noise contrastive estimation or reinforcement learning.
## New features {#features}
This section contains an overview of the most important **new features and
improvements**. The [API docs](/api) include additional deprecation notes.
### Convolutional neural network models {#features-models}
> #### Example
>
> ```bash
> python -m spacy download en_core_web_sm
> python -m spacy download de_core_news_sm
> python -m spacy download xx_ent_wiki_sm
> ```
spaCy v2.0 features new neural models for tagging, parsing and entity
recognition. The models have been designed and implemented from scratch
specifically for spaCy, to give you an unmatched balance of speed, size and
accuracy. The new models are **10× smaller**, **20% more accurate**, and **even
cheaper to run** than the previous generation.
spaCy v2.0's new neural network models bring significant improvements in
accuracy, especially for English Named Entity Recognition. The new
[`en_core_web_lg`](/models/en#en_core_web_lg) model makes about **25% fewer
mistakes** than the corresponding v1.x model and is within **1% of the current
state-of-the-art**
([Strubell et al., 2017](https://arxiv.org/pdf/1702.02098.pdf)). The v2.0 models
are also cheaper to run at scale, as they require **under 1 GB of memory** per
process.
<Infobox>
**Usage:** [Models directory](/models)
</Infobox>
### Improved processing pipelines {#features-pipelines}
> #### Example
>
> ```python
> # Set custom attributes
> Doc.set_extension("my_attr", default=False)
> Token.set_extension("my_attr", getter=my_token_getter)
> assert doc._.my_attr, token._.my_attr
>
> # Add components to the pipeline
> my_component = lambda doc: doc
> nlp.add_pipe(my_component)
> ```
It's now much easier to **customize the pipeline** with your own components:
functions that receive a `Doc` object, modify and return it. Extensions let you
write any **attributes, properties and methods** to the `Doc`, `Token` and
`Span`. You can add data, implement new features, integrate other libraries with
spaCy or plug in your own machine learning models.
![The processing pipeline](../images/pipeline.svg)
<Infobox>
**API:** [`Language`](/api/language),
[`Doc.set_extension`](/api/doc#set_extension),
[`Span.set_extension`](/api/span#set_extension),
[`Token.set_extension`](/api/token#set_extension) **Usage:**
[Processing pipelines](/usage/processing-pipelines) **Code:**
[Pipeline examples](/usage/examples#section-pipeline)
</Infobox>
### Text classification {#features-text-classification}
> #### Example
>
> ```python
> textcat = nlp.create_pipe("textcat")
> nlp.add_pipe(textcat, last=True)
> nlp.begin_training()
> for itn in range(100):
> for doc, gold in train_data:
> nlp.update([doc], [gold])
> doc = nlp("This is a text.")
> print(doc.cats)
> ```
spaCy v2.0 lets you add text categorization models to spaCy pipelines. The model
supports classification with multiple, non-mutually exclusive labels – so
multiple labels can apply at once. You can change the model architecture rather
easily, but by default, the `TextCategorizer` class uses a convolutional neural
network to assign position-sensitive vectors to each word in the document.
<Infobox>
**API:** [`TextCategorizer`](/api/textcategorizer),
[`Doc.cats`](/api/doc#attributes), `GoldParse.cats` **Usage:**
[Training a text classification model](/usage/training#textcat)
</Infobox>
### Hash values instead of integer IDs {#features-hash-ids}
> #### Example
>
> ```python
> doc = nlp("I love coffee")
> assert doc.vocab.strings["coffee"] == 3197928453018144401
> assert doc.vocab.strings[3197928453018144401] == "coffee"
>
> beer_hash = doc.vocab.strings.add("beer")
> assert doc.vocab.strings["beer"] == beer_hash
> assert doc.vocab.strings[beer_hash] == "beer"
> ```
The [`StringStore`](/api/stringstore) now resolves all strings to hash values
instead of integer IDs. This means that the string-to-int mapping **no longer
depends on the vocabulary state**, making a lot of workflows much simpler,
especially during training. Unlike integer IDs in spaCy v1.x, hash values will
**always match** – even across models. Strings can now be added explicitly using
the new [`Stringstore.add`](/api/stringstore#add) method. A token's hash is
available via `token.orth`.
<Infobox>
**API:** [`StringStore`](/api/stringstore) **Usage:**
[Vocab, hashes and lexemes 101](/usage/spacy-101#vocab)
</Infobox>
### Improved word vectors support {#features-vectors}
> #### Example
>
> ```python
> for word, vector in vector_data:
> nlp.vocab.set_vector(word, vector)
> nlp.vocab.vectors.from_glove("/path/to/vectors")
> # Keep 10000 unique vectors and remap the rest
> nlp.vocab.prune_vectors(10000)
> nlp.to_disk("/model")
> ```
The new [`Vectors`](/api/vectors) class helps the `Vocab` manage the vectors
assigned to strings, and lets you assign vectors individually, or
[load in GloVe vectors](/usage/linguistic-features#adding-vectors) from a
directory. To help you strike a good balance between coverage and memory usage,
the `Vectors` class lets you map **multiple keys** to the **same row** of the
table. If you're using the [`spacy init-model`](/api/cli#init-model) command to
create a vocabulary, pruning the vectors will be taken care of automatically if
you set the `--prune-vectors` flag. Otherwise, you can use the new
[`Vocab.prune_vectors`](/api/vocab#prune_vectors).
<Infobox>
**API:** [`Vectors`](/api/vectors), [`Vocab`](/api/vocab) **Usage:**
[Word vectors and semantic similarity](/usage/vectors-similarity)
</Infobox>
### Saving, loading and serialization {#features-serializer}
> #### Example
>
> ```python
> nlp = spacy.load("en") # shortcut link
> nlp = spacy.load("en_core_web_sm") # package
> nlp = spacy.load("/path/to/en") # unicode path
> nlp = spacy.load(Path("/path/to/en")) # pathlib Path
>
> nlp.to_disk("/path/to/nlp")
> nlp = English().from_disk("/path/to/nlp")
> ```
spaCy's serialization API has been made consistent across classes and objects.
All container classes, i.e. `Language`, `Doc`, `Vocab` and `StringStore` now
have a `to_bytes()`, `from_bytes()`, `to_disk()` and `from_disk()` method that
supports the Pickle protocol.
The improved `spacy.load` makes loading models easier and more transparent. You
can load a model by supplying its shortcut link, the name of an installed
[model package](/models) or a path. The `Language` class to initialize will be
determined based on the model's settings. For a blank language, you can import
the class directly, e.g. `from spacy.lang.en import English` or use
[`spacy.blank()`](/api/top-level#spacy.blank).
<Infobox>
**API:** [`spacy.load`](/api/top-level#spacy.load),
[`Language.to_disk`](/api/language#to_disk) **Usage:**
[Models](/usage/models#usage),
[Saving and loading](/usage/saving-loading#models)
</Infobox>
### displaCy visualizer with Jupyter support {#features-displacy}
> #### Example
>
> ```python
> from spacy import displacy
> doc = nlp("This is a sentence about Facebook.")
> displacy.serve(doc, style="dep") # run the web server
> html = displacy.render(doc, style="ent") # generate HTML
> ```
Our popular dependency and named entity visualizers are now an official part of
the spaCy library. displaCy can run a simple web server, or generate raw HTML
markup or SVG files to be exported. You can pass in one or more docs, and
customize the style. displaCy also auto-detects whether you're running
[Jupyter](https://jupyter.org) and will render the visualizations in your
notebook.
<Infobox>
**API:** [`displacy`](/api/top-level#displacy) **Usage:**
[Visualizing spaCy](/usage/visualizers)
</Infobox>
### Improved language data and lazy loading {#features-language}
Language-specific data now lives in its own submodule, `spacy.lang`. Languages
are lazy-loaded, i.e. only loaded when you import a `Language` class, or load a
model that initializes one. This allows languages to contain more custom data,
e.g. lemmatizer lookup tables, or complex regular expressions. The language data
has also been tidied up and simplified. spaCy now also supports simple
lookup-based lemmatization – and **many new languages**!
<Infobox>
**API:** [`Language`](/api/language) **Code:**
[`spacy/lang`](https://github.com/explosion/spaCy/tree/master/spacy/lang)
**Usage:** [Adding languages](/usage/adding-languages)
</Infobox>
### Revised matcher API and phrase matcher {#features-matcher}
> #### Example
>
> ```python
> from spacy.matcher import Matcher, PhraseMatcher
>
> matcher = Matcher(nlp.vocab)
> matcher.add('HEARTS', None, [{"ORTH": "❤️", "OP": '+'}])
>
> phrasematcher = PhraseMatcher(nlp.vocab)
> phrasematcher.add("OBAMA", None, nlp("Barack Obama"))
> ```
Patterns can now be added to the matcher by calling
[`matcher.add()`](/api/matcher#add) with a match ID, an optional callback
function to be invoked on each match, and one or more patterns. This allows you
to write powerful, pattern-specific logic using only one matcher. For example,
you might only want to merge some entity types, and set custom flags for other
matched patterns. The new [`PhraseMatcher`](/api/phrasematcher) lets you
efficiently match very large terminology lists using `Doc` objects as match
patterns.
<Infobox>
**API:** [`Matcher`](/api/matcher), [`PhraseMatcher`](/api/phrasematcher)
**Usage:** [Rule-based matching](/usage/rule-based-matching)
</Infobox>
## Backwards incompatibilities {#incompat}
The following modules, classes and methods have changed between v1.x and v2.0.
| Old | New |
| ------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------- |
| `spacy.download.en`, `spacy.download.de` | [`cli.download`](/api/cli#download) |
| `spacy.en` etc. | `spacy.lang.en` etc. |
| `spacy.en.word_sets` | `spacy.lang.en.stop_words` |
| `spacy.orth` | `spacy.lang.xx.lex_attrs` |
| `spacy.syntax.iterators` | `spacy.lang.xx.syntax_iterators` |
| `spacy.tagger.Tagger` | `spacy.pipeline.Tagger` |
| `spacy.cli.model` | [`spacy.cli.vocab`](/api/cli#vocab) |
| `Language.save_to_directory` | [`Language.to_disk`](/api/language#to_disk) |
| `Language.end_training` | [`Language.begin_training`](/api/language#begin_training) |
| `Language.create_make_doc` | [`Language.tokenizer`](/api/language#attributes) |
| `Vocab.resize_vectors` | [`Vectors.resize`](/api/vectors#resize) |
| `Vocab.load` `Vocab.load_lexemes` | [`Vocab.from_disk`](/api/vocab#from_disk) [`Vocab.from_bytes`](/api/vocab#from_bytes) |
| `Vocab.dump` | [`Vocab.to_disk`](/api/vocab#to_disk) [`Vocab.to_bytes`](/api/vocab#to_bytes) |
| `Vocab.load_vectors` `Vocab.load_vectors_from_bin_loc` | [`Vectors.from_disk`](/api/vectors#from_disk) [`Vectors.from_bytes`](/api/vectors#from_bytes) [`Vectors.from_glove`](/api/vectors#from_glove) |
| `Vocab.dump_vectors` | [`Vectors.to_disk`](/api/vectors#to_disk) [`Vectors.to_bytes`](/api/vectors#to_bytes) |
| `StringStore.load` | [`StringStore.from_disk`](/api/stringstore#from_disk) [`StringStore.from_bytes`](/api/stringstore#from_bytes) |
| `StringStore.dump` | [`StringStore.to_disk`](/api/stringstore#to_disk) [`StringStore.to_bytes`](/api/stringstore#to_bytes) |
| `Tokenizer.load` | [`Tokenizer.from_disk`](/api/tokenizer#from_disk) [`Tokenizer.from_bytes`](/api/tokenizer#from_bytes) |
| `Tagger.load` | [`Tagger.from_disk`](/api/tagger#from_disk) [`Tagger.from_bytes`](/api/tagger#from_bytes) |
| `Tagger.tag_names` | `Tagger.labels` |
| `DependencyParser.load` | [`DependencyParser.from_disk`](/api/dependencyparser#from_disk) [`DependencyParser.from_bytes`](/api/dependencyparser#from_bytes) |
| `EntityRecognizer.load` | [`EntityRecognizer.from_disk`](/api/entityrecognizer#from_disk) [`EntityRecognizer.from_bytes`](/api/entityrecognizer#from_bytes) |
| `Matcher.load` | - |
| `Matcher.add_pattern` `Matcher.add_entity` | [`Matcher.add`](/api/matcher#add) [`PhraseMatcher.add`](/api/phrasematcher#add) |
| `Matcher.get_entity` | [`Matcher.get`](/api/matcher#get) |
| `Matcher.has_entity` | [`Matcher.has_key`](/api/matcher#has_key) |
| `Doc.read_bytes` | [`Doc.to_bytes`](/api/doc#to_bytes) [`Doc.from_bytes`](/api/doc#from_bytes) [`Doc.to_disk`](/api/doc#to_disk) [`Doc.from_disk`](/api/doc#from_disk) |
| `Token.is_ancestor_of` | [`Token.is_ancestor`](/api/token#is_ancestor) |
### Deprecated {#deprecated}
The following methods are deprecated. They can still be used, but should be
replaced.
| Old | New |
| ---------------------------- | ----------------------------------------------- |
| `Tokenizer.tokens_from_list` | [`Doc`](/api/doc) |
| `Span.sent_start` | [`Span.is_sent_start`](/api/span#is_sent_start) |
## Migrating from spaCy 1.x {#migrating}
Because we'e made so many architectural changes to the library, we've tried to
**keep breaking changes to a minimum**. A lot of projects follow the philosophy
that if you're going to break anything, you may as well break everything. We
think migration is easier if there's a logic to what has changed. We've
therefore followed a policy of avoiding breaking changes to the `Doc`, `Span`
and `Token` objects. This way, you can focus on only migrating the code that
does training, loading and serialization — in other words, code that works with
the `nlp` object directly. Code that uses the annotations should continue to
work.
<Infobox title="Important note" variant="warning">
If you've trained your own models, keep in mind that your train and runtime
inputs must match. This means you'll have to **retrain your models** with spaCy
v2.0.
</Infobox>
### Document processing {#migrating-document-processing}
The [`Language.pipe`](/api/language#pipe) method allows spaCy to batch
documents, which brings a **significant performance advantage** in v2.0. The new
neural networks introduce some overhead per batch, so if you're processing a
number of documents in a row, you should use `nlp.pipe` and process the texts as
a stream.
```diff
- docs = (nlp(text) for text in texts)
+ docs = nlp.pipe(texts)
```
To make usage easier, there's now a boolean `as_tuples` keyword argument, that
lets you pass in an iterator of `(text, context)` pairs, so you can get back an
iterator of `(doc, context)` tuples.
### Saving, loading and serialization {#migrating-saving-loading}
Double-check all calls to `spacy.load()` and make sure they don't use the `path`
keyword argument. If you're only loading in binary data and not a model package
that can construct its own `Language` class and pipeline, you should now use the
[`Language.from_disk`](/api/language#from_disk) method.
```diff
- nlp = spacy.load("en", path="/model")
+ nlp = spacy.load("/model")
+ nlp = spacy.blank("en").from_disk("/model/data")
```
Review all other code that writes state to disk or bytes. All containers, now
share the same, consistent API for saving and loading. Replace saving with
`to_disk()` or `to_bytes()`, and loading with `from_disk()` and `from_bytes()`.
```diff
- nlp.save_to_directory("/model")
- nlp.vocab.dump("/vocab")
+ nlp.to_disk("/model")
+ nlp.vocab.to_disk("/vocab")
```
If you've trained models with input from v1.x, you'll need to **retrain them**
with spaCy v2.0. All previous models will not be compatible with the new
version.
### Processing pipelines and language data {#migrating-languages}
If you're importing language data or `Language` classes, make sure to change
your import statements to import from `spacy.lang`. If you've added your own
custom language, it needs to be moved to `spacy/lang/xx` and adjusted
accordingly.
```diff
- from spacy.en import English
+ from spacy.lang.en import English
```
If you've been using custom pipeline components, check out the new guide on
[processing pipelines](/usage/processing-pipelines). Pipeline components are now
`(name, func)` tuples. Appending them to the pipeline still works – but the
[`add_pipe`](/api/language#add_pipe) method now makes this much more convenient.
Methods for removing, renaming, replacing and retrieving components have been
added as well. Components can now be disabled by passing a list of their names
to the `disable` keyword argument on load, or by using
[`disable_pipes`](/api/language#disable_pipes) as a method or context manager:
```diff
- nlp = spacy.load("en_core_web_sm", tagger=False, entity=False)
- doc = nlp("I don't want parsed", parse=False)
+ nlp = spacy.load("en_core_web_sm", disable=["tagger", "ner"])
+ with nlp.disable_pipes("parser"):
+ doc = nlp("I don't want parsed")
```
To add spaCy's built-in pipeline components to your pipeline, you can still
import and instantiate them directly – but it's more convenient to use the new
[`create_pipe`](/api/language#create_pipe) method with the component name, i.e.
`'tagger'`, `'parser'`, `'ner'` or `'textcat'`.
```diff
- from spacy.pipeline import Tagger
- tagger = Tagger(nlp.vocab)
- nlp.pipeline.insert(0, tagger)
+ tagger = nlp.create_pipe("tagger")
+ nlp.add_pipe(tagger, first=True)
```
### Training {#migrating-training}
All built-in pipeline components are now subclasses of [`Pipe`](/api/pipe),
fully trainable and serializable, and follow the same API. Instead of updating
the model and telling spaCy when to _stop_, you can now explicitly call
[`begin_training`](/api/language#begin_training), which returns an optimizer you
can pass into the [`update`](/api/language#update) function. While `update`
still accepts sequences of `Doc` and `GoldParse` objects, you can now also pass
in a list of strings and dictionaries describing the annotations. We call this
the ["simple training style"](/usage/training#training-simple-style). This is
also the recommended usage, as it removes one layer of abstraction from the
training.
```diff
- for itn in range(1000):
- for text, entities in train_data:
- doc = Doc(text)
- gold = GoldParse(doc, entities=entities)
- nlp.update(doc, gold)
- nlp.end_training()
- nlp.save_to_directory("/model")
+ nlp.begin_training()
+ for itn in range(1000):
+ for texts, annotations in train_data:
+ nlp.update(texts, annotations)
+ nlp.to_disk("/model")
```
### Attaching custom data to the Doc {#migrating-doc}
Previously, you had to create a new container in order to attach custom data to
a `Doc` object. This often required converting the `Doc` objects to and from
arrays. In spaCy v2.0, you can set your own attributes, properties and methods
on the `Doc`, `Token` and `Span` via
[custom extensions](/usage/processing-pipelines#custom-components-attributes).
This means that your application can – and should – only pass around `Doc`
objects and refer to them as the single source of truth.
```diff
- doc = nlp("This is a regular doc")
- doc_array = doc.to_array(["ORTH", "POS"])
- doc_with_meta = {"doc_array": doc_array, "meta": get_doc_meta(doc_array)}
+ Doc.set_extension("meta", getter=get_doc_meta)
+ doc_with_meta = nlp(u'This is a doc with meta data')
+ meta = doc._.meta
```
If you wrap your extension attributes in a
[custom pipeline component](/usage/processing-pipelines#custom-components), they
will be assigned automatically when you call `nlp` on a text. If your
application assigns custom data to spaCy's container objects, or includes other
utilities that interact with the pipeline, consider moving this logic into its
own extension module.
```diff
- doc = nlp("Doc with a standard pipeline")
- meta = get_meta(doc)
+ nlp.add_pipe(meta_component)
+ doc = nlp("Doc with a custom pipeline that assigns meta")
+ meta = doc._.meta
```
### Strings and hash values {#migrating-strings}
The change from integer IDs to hash values may not actually affect your code
very much. However, if you're adding strings to the vocab manually, you now need
to call [`StringStore.add`](/api/stringstore#add) explicitly. You can also now
be sure that the string-to-hash mapping will always match across vocabularies.
```diff
- nlp.vocab.strings["coffee"] # 3672
- other_nlp.vocab.strings["coffee"] # 40259
+ nlp.vocab.strings.add("coffee")
+ nlp.vocab.strings["coffee"] # 3197928453018144401
+ other_nlp.vocab.strings["coffee"] # 3197928453018144401
```
### Adding patterns and callbacks to the matcher {#migrating-matcher}
If you're using the matcher, you can now add patterns in one step. This should
be easy to update – simply merge the ID, callback and patterns into one call to
[`Matcher.add()`](/api/matcher#add). The matcher now also supports string keys,
which saves you an extra import. If you've been using **acceptor functions**,
you'll need to move this logic into the
[`on_match` callbacks](/usage/linguistic-features#on_match). The callback
function is invoked on every match and will give you access to the doc, the
index of the current match and all total matches. This lets you both accept or
reject the match, and define the actions to be triggered.
```diff
- matcher.add_entity("GoogleNow", on_match=merge_phrases)
- matcher.add_pattern("GoogleNow", [{ORTH: "Google"}, {ORTH: "Now"}])
+ matcher.add("GoogleNow", merge_phrases, [{"ORTH": "Google"}, {"ORTH": "Now"}])
```
If you need to match large terminology lists, you can now also use the
[`PhraseMatcher`](/api/phrasematcher), which accepts `Doc` objects as match
patterns and is more efficient than the regular, rule-based matcher.
```diff
- matcher = Matcher(nlp.vocab)
- matcher.add_entity("PRODUCT")
- for text in large_terminology_list
- matcher.add_pattern("PRODUCT", [{ORTH: text}])
+ from spacy.matcher import PhraseMatcher
+ matcher = PhraseMatcher(nlp.vocab)
+ patterns = [nlp.make_doc(text) for text in large_terminology_list]
+ matcher.add("PRODUCT", None, *patterns)
```
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