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287 lines
12 KiB
Markdown
287 lines
12 KiB
Markdown
---
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title: What's New in v3.0
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teaser: New features, backwards incompatibilities and migration guide
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menu:
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- ['Summary', 'summary']
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- ['New Features', 'features']
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- ['Backwards Incompatibilities', 'incompat']
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- ['Migrating from v2.x', 'migrating']
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- ['Migrating plugins', 'plugins']
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---
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## Summary {#summary}
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## New Features {#features}
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## Backwards Incompatibilities {#incompat}
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### Removed or renamed objects, methods, attributes and arguments {#incompat-removed}
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| Removed | Replacement |
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| -------------------------------------------------------- | ----------------------------------------- |
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| `GoldParse` | [`Example`](/api/example) |
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| `GoldCorpus` | [`Corpus`](/api/corpus) |
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| `spacy debug-data` | [`spacy debug data`](/api/cli#debug-data) |
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| `spacy link`, `util.set_data_path`, `util.get_data_path` | not needed, model symlinks are deprecated |
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### Removed deprecated methods, attributes and arguments {#incompat-removed-deprecated}
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The following deprecated methods, attributes and arguments were removed in v3.0.
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Most of them have been **deprecated for a while** and many would previously
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raise errors. Many of them were also mostly internals. If you've been working
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with more recent versions of spaCy v2.x, it's **unlikely** that your code relied
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on them.
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| Removed | Replacement |
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| ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `Doc.tokens_from_list` | [`Doc.__init__`](/api/doc#init) |
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| `Doc.merge`, `Span.merge` | [`Doc.retokenize`](/api/doc#retokenize) |
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| `Token.string`, `Span.string`, `Span.upper`, `Span.lower` | [`Span.text`](/api/span#attributes), [`Token.text`](/api/token#attributes) |
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| `Language.tagger`, `Language.parser`, `Language.entity` | [`Language.get_pipe`](/api/language#get_pipe) |
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| keyword-arguments like `vocab=False` on `to_disk`, `from_disk`, `to_bytes`, `from_bytes` | `exclude=["vocab"]` |
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| `n_threads` argument on [`Tokenizer`](/api/tokenizer), [`Matcher`](/api/matcher), [`PhraseMatcher`](/api/phrasematcher) | `n_process` |
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| `SentenceSegmenter` hook, `SimilarityHook` | [user hooks](/usage/processing-pipelines#custom-components-user-hooks), [`Sentencizer`](/api/sentencizer), [`SentenceRecognizer`](/api/sentenceregognizer) |
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## Migrating from v2.x {#migrating}
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### Downloading and loading models {#migrating-downloading-models}
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Model symlinks and shortcuts like `en` are now officially deprecated. There are
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[many different models](/models) with different capabilities and not just one
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"English model". In order to download and load a model, you should always use
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its full name – for instance, `en_core_web_sm`.
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```diff
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- python -m spacy download en
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+ python -m spacy download en_core_web_sm
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```
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```diff
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- nlp = spacy.load("en")
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+ nlp = spacy.load("en_core_web_sm")
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```
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### Custom pipeline components and factories {#migrating-pipeline-components}
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Custom pipeline components now have to be registered explicitly using the
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[`@Language.component`](/api/language#component) or
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[`@Language.factory`](/api/language#factory) decorator. For simple functions
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that take a `Doc` and return it, all you have to do is add the
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`@Language.component` decorator to it and assign it a name:
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```diff
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### Stateless function components
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+ from spacy.language import Language
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+ @Language.component("my_component")
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def my_component(doc):
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return doc
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```
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For class components that are initialized with settings and/or the shared `nlp`
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object, you can use the `@Language.factory` decorator. Also make sure that that
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the method used to initialize the factory has **two named arguments**: `nlp`
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(the current `nlp` object) and `name` (the string name of the component
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instance).
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```diff
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### Stateful class components
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+ from spacy.language import Language
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+ @Language.factory("my_component")
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class MyComponent:
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- def __init__(self, nlp):
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+ def __init__(self, nlp, name):
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self.nlp = nlp
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def __call__(self, doc):
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return doc
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```
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Instead of decorating your class, you could also add a factory function that
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takes the arguments `nlp` and `name` and returns an instance of your component:
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```diff
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### Stateful class components with factory function
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+ from spacy.language import Language
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+ @Language.factory("my_component")
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+ def create_my_component(nlp, name):
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+ return MyComponent(nlp)
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class MyComponent:
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def __init__(self, nlp):
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self.nlp = nlp
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def __call__(self, doc):
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return doc
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```
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The `@Language.component` and `@Language.factory` decorators now take care of
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adding an entry to the component factories, so spaCy knows how to load a
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component back in from its string name. You won't have to write to
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`Language.factories` manually anymore.
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```diff
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- Language.factories["my_component"] = lambda nlp, **cfg: MyComponent(nlp)
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```
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#### Adding components to the pipeline {#migrating-add-pipe}
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The [`nlp.add_pipe`](/api/language#add_pipe) method now takes the **string
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name** of the component factory instead of a callable component. This allows
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spaCy to track and serialize components that have been added and their settings.
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```diff
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+ @Language.component("my_component")
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def my_component(doc):
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return doc
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- nlp.add_pipe(my_component)
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+ nlp.add_pipe("my_component")
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```
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[`nlp.add_pipe`](/api/language#add_pipe) now also returns the pipeline component
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itself, so you can access its attributes. The
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[`nlp.create_pipe`](/api/language#create_pipe) method is now mostly internals
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and you typically shouldn't have to use it in your code.
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```diff
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- parser = nlp.create_pipe("parser")
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- nlp.add_pipe(parser)
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+ parser = nlp.add_pipe("parser")
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```
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### Training models {#migrating-training}
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To train your models, you should now pretty much always use the
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[`spacy train`](/api/cli#train) CLI. You shouldn't have to put together your own
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training scripts anymore, unless you _really_ want to. The training commands now
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use a [flexible config file](/usage/training#config) that describes all training
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settings and hyperparameters, as well as your pipeline, model components and
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architectures to use. The `--code` argument lets you pass in code containing
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[custom registered functions](/usage/training#custom-code) that you can
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reference in your config.
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#### Binary .spacy training data format {#migrating-training-format}
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spaCy now uses a new
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[binary training data format](/api/data-formats#binary-training), which is much
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smaller and consists of `Doc` objects, serialized via the
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[`DocBin`](/api/docbin). You can convert your existing JSON-formatted data using
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the [`spacy convert`](/api/cli#convert) command, which outputs `.spacy` files:
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```bash
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$ python -m spacy convert ./training.json ./output
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```
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#### Training config {#migrating-training-config}
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<!-- TODO: update once we have recommended "getting started with a new config" workflow -->
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```diff
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### {wrap="true"}
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- python -m spacy train en ./output ./train.json ./dev.json --pipeline tagger,parser --cnn-window 1 --bilstm-depth 0
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+ python -m spacy train ./train.spacy ./dev.spacy ./config.cfg --output ./output
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```
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<Project id="some_example_project">
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The easiest way to get started with an end-to-end training process is to clone a
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[project](/usage/projects) template. Projects let you manage multi-step
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workflows, from data preprocessing to training and packaging your model.
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</Project>
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#### Migrating training scripts to CLI command and config {#migrating-training-scripts}
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<!-- TODO: write -->
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#### Training via the Python API {#migrating-training-python}
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<!-- TODO: this should explain the GoldParse -> Example stuff -->
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#### Packaging models {#migrating-training-packaging}
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The [`spacy package`](/api/cli#package) command now automatically builds the
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installable `.tar.gz` sdist of the Python package, so you don't have to run this
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step manually anymore. You can disable the behavior by setting the `--no-sdist`
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flag.
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```diff
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python -m spacy package ./model ./packages
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- cd /output/en_model-0.0.0
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- python setup.py sdist
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```
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## Migration notes for plugin maintainers {#plugins}
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Thanks to everyone who's been contributing to the spaCy ecosystem by developing
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and maintaining one of the many awesome [plugins and extensions](/universe).
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We've tried to keep breaking changes to a minimum and make it as easy as
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possible for you to upgrade your packages for spaCy v3.
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### Custom pipeline components
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The most common use case for plugins is providing pipeline components and
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extension attributes.
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- Use the [`@Language.factory`](/api/language#factory) decorator to register
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your component and assign it a name. This allows users to refer to your
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components by name and serialize pipelines referencing them. Remove all manual
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entries to the `Language.factories`.
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- Make sure your component factories take at least two **named arguments**:
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`nlp` (the current `nlp` object) and `name` (the instance name of the added
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component so you can identify multiple instances of the same component).
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- Update all references to [`nlp.add_pipe`](/api/language#add_pipe) in your docs
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to use **string names** instead of the component functions.
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```python
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### {highlight="1-5"}
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from spacy.language import Language
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@Language.factory("my_component", default_config={"some_setting": False})
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def create_component(nlp: Language, name: str, some_setting: bool):
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return MyCoolComponent(some_setting=some_setting)
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class MyCoolComponent:
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def __init__(self, some_setting):
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self.some_setting = some_setting
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def __call__(self, doc):
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# Do something to the doc
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return doc
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```
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> #### Result in config.cfg
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>
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> ```ini
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> [components.my_component]
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> factory = "my_component"
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> some_setting = true
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> ```
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```diff
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import spacy
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from your_plugin import MyCoolComponent
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nlp = spacy.load("en_core_web_sm")
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- component = MyCoolComponent(some_setting=True)
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- nlp.add_pipe(component)
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+ nlp.add_pipe("my_component", config={"some_setting": True})
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```
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<Infobox title="Important note on registering factories" variant="warning">
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The [`@Language.factory`](/api/language#factory) decorator takes care of letting
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spaCy know that a component of that name is available. This means that your
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users can add it to the pipeline using its **string name**. However, this
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requires the decorator to be executed – so users will still have to **import
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your plugin**. Alternatively, your plugin could expose an
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[entry point](/usage/saving-loading#entry-points), which spaCy can read from.
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This means that spaCy knows how to initialize `my_component`, even if your
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package isn't imported.
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</Infobox>
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