💫 Industrial-strength Natural Language Processing (NLP) in Python
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Add SpanRuler component (#9880)
* Add SpanRuler component

Add a `SpanRuler` component similar to `EntityRuler` that saves a list
of matched spans to `Doc.spans[spans_key]`. The matches from the token
and phrase matchers are deduplicated and sorted before assignment but
are not otherwise filtered.

* Update spacy/pipeline/span_ruler.py

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Fix cast

* Add self.key property

* Use number of patterns as length

* Remove patterns kwarg from init

* Update spacy/tests/pipeline/test_span_ruler.py

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Add options for spans filter and setting to ents

* Add `spans_filter` option as a registered function'
* Make `spans_key` optional and if `None`, set to `doc.ents` instead of
`doc.spans[spans_key]`.

* Update and generalize tests

* Add test for setting doc.ents, fix key property type

* Fix typing

* Allow independent doc.spans and doc.ents

* If `spans_key` is set, set `doc.spans` with `spans_filter`.
* If `annotate_ents` is set, set `doc.ents` with `ents_fitler`.
  * Use `util.filter_spans` by default as `ents_filter`.
  * Use a custom warning if the filter does not work for `doc.ents`.

* Enable use of SpanC.id in Span

* Support id in SpanRuler as Span.id

* Update types

* `id` can only be provided as string (already by `PatternType`
definition)

* Update all uses of Span.id/ent_id in Doc

* Rename Span id kwarg to span_id

* Update types and docs

* Add ents filter to mimic EntityRuler overwrite_ents

* Refactor `ents_filter` to take `entities, spans` args for more
  filtering options
* Give registered filters more descriptive names
* Allow registered `filter_spans` filter
  (`spacy.first_longest_spans_filter.v1`) to take any number of
  `Iterable[Span]` objects as args so it can be used for spans filter
  or ents filter

* Implement future entity ruler as span ruler

Implement a compatible `entity_ruler` as `future_entity_ruler` using
`SpanRuler` as the underlying component:
* Add `sort_key` and `sort_reverse` to allow the sorting behavior to be
  customized. (Necessary for the same sorting/filtering as in
  `EntityRuler`.)
* Implement `overwrite_overlapping_ents_filter` and
  `preserve_existing_ents_filter` to support
  `EntityRuler.overwrite_ents` settings.
* Add `remove_by_id` to support `EntityRuler.remove` functionality.
* Refactor `entity_ruler` tests to parametrize all tests to test both
  `entity_ruler` and `future_entity_ruler`
* Implement `SpanRuler.token_patterns` and `SpanRuler.phrase_patterns`
  properties.

Additional changes:

* Move all config settings to top-level attributes to avoid duplicating
  settings in the config vs. `span_ruler/cfg`. (Also avoids a lot of
  casting.)

* Format

* Fix filter make method name

* Refactor to use same error for removing by label or ID

* Also provide existing spans to spans filter

* Support ids property

* Remove token_patterns and phrase_patterns

* Update docstrings

* Add span ruler docs

* Fix types

* Apply suggestions from code review

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Move sorting into filters

* Check for all tokens in seen tokens in entity ruler filters

* Remove registered sort key

* Set Token.ent_id in a backwards-compatible way in Doc.set_ents

* Remove sort options from API docs

* Update docstrings

* Rename entity ruler filters

* Fix and parameterize scoring

* Add id to Span API docs

* Fix typo in API docs

* Include explicit labeled=True for scorer

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
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spaCy: Industrial-strength NLP

spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products.

spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.

💫 Version 3.2 out now! Check out the release notes here.

Azure Pipelines Current Release Version pypi Version conda Version Python wheels Code style: black
PyPi downloads Conda downloads spaCy on Twitter

📖 Documentation

Documentation
spaCy 101 New to spaCy? Here's everything you need to know!
📚 Usage Guides How to use spaCy and its features.
🚀 New in v3.0 New features, backwards incompatibilities and migration guide.
🪐 Project Templates End-to-end workflows you can clone, modify and run.
🎛 API Reference The detailed reference for spaCy's API.
📦 Models Download trained pipelines for spaCy.
🌌 Universe Plugins, extensions, demos and books from the spaCy ecosystem.
👩‍🏫 Online Course Learn spaCy in this free and interactive online course.
📺 Videos Our YouTube channel with video tutorials, talks and more.
🛠 Changelog Changes and version history.
💝 Contribute How to contribute to the spaCy project and code base.
spaCy Tailored Pipelines Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! Learn more →

💬 Where to ask questions

The spaCy project is maintained by the spaCy team. Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's shared publicly, so that more people can benefit from it.

Type Platforms
🚨 Bug Reports GitHub Issue Tracker
🎁 Feature Requests & Ideas GitHub Discussions
👩‍💻 Usage Questions GitHub Discussions · Stack Overflow
🗯 General Discussion GitHub Discussions

Features

  • Support for 60+ languages
  • Trained pipelines for different languages and tasks
  • Multi-task learning with pretrained transformers like BERT
  • Support for pretrained word vectors and embeddings
  • State-of-the-art speed
  • Production-ready training system
  • Linguistically-motivated tokenization
  • Components for named entity recognition, part-of-speech-tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more
  • Easily extensible with custom components and attributes
  • Support for custom models in PyTorch, TensorFlow and other frameworks
  • Built in visualizers for syntax and NER
  • Easy model packaging, deployment and workflow management
  • Robust, rigorously evaluated accuracy

📖 For more details, see the facts, figures and benchmarks.

Install spaCy

For detailed installation instructions, see the documentation.

  • Operating system: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual Studio)
  • Python version: Python 3.6+ (only 64 bit)
  • Package managers: pip · conda (via conda-forge)

pip

Using pip, spaCy releases are available as source packages and binary wheels. Before you install spaCy and its dependencies, make sure that your pip, setuptools and wheel are up to date.

pip install -U pip setuptools wheel
pip install spacy

To install additional data tables for lemmatization and normalization you can run pip install spacy[lookups] or install spacy-lookups-data separately. The lookups package is needed to create blank models with lemmatization data, and to lemmatize in languages that don't yet come with pretrained models and aren't powered by third-party libraries.

When using pip it is generally recommended to install packages in a virtual environment to avoid modifying system state:

python -m venv .env
source .env/bin/activate
pip install -U pip setuptools wheel
pip install spacy

conda

You can also install spaCy from conda via the conda-forge channel. For the feedstock including the build recipe and configuration, check out this repository.

conda install -c conda-forge spacy

Updating spaCy

Some updates to spaCy may require downloading new statistical models. If you're running spaCy v2.0 or higher, you can use the validate command to check if your installed models are compatible and if not, print details on how to update them:

pip install -U spacy
python -m spacy validate

If you've trained your own models, keep in mind that your training and runtime inputs must match. After updating spaCy, we recommend retraining your models with the new version.

📖 For details on upgrading from spaCy 2.x to spaCy 3.x, see the migration guide.

📦 Download model packages

Trained pipelines for spaCy can be installed as Python packages. This means that they're a component of your application, just like any other module. Models can be installed using spaCy's download command, or manually by pointing pip to a path or URL.

Documentation
Available Pipelines Detailed pipeline descriptions, accuracy figures and benchmarks.
Models Documentation Detailed usage and installation instructions.
Training How to train your own pipelines on your data.
# Download best-matching version of specific model for your spaCy installation
python -m spacy download en_core_web_sm

# pip install .tar.gz archive or .whl from path or URL
pip install /Users/you/en_core_web_sm-3.0.0.tar.gz
pip install /Users/you/en_core_web_sm-3.0.0-py3-none-any.whl
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz

Loading and using models

To load a model, use spacy.load() with the model name or a path to the model data directory.

import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("This is a sentence.")

You can also import a model directly via its full name and then call its load() method with no arguments.

import spacy
import en_core_web_sm

nlp = en_core_web_sm.load()
doc = nlp("This is a sentence.")

📖 For more info and examples, check out the models documentation.

⚒ Compile from source

The other way to install spaCy is to clone its GitHub repository and build it from source. That is the common way if you want to make changes to the code base. You'll need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, virtualenv and git installed. The compiler part is the trickiest. How to do that depends on your system.

Platform
Ubuntu Install system-level dependencies via apt-get: sudo apt-get install build-essential python-dev git .
Mac Install a recent version of XCode, including the so-called "Command Line Tools". macOS and OS X ship with Python and git preinstalled.
Windows Install a version of the Visual C++ Build Tools or Visual Studio Express that matches the version that was used to compile your Python interpreter.

For more details and instructions, see the documentation on compiling spaCy from source and the quickstart widget to get the right commands for your platform and Python version.

git clone https://github.com/explosion/spaCy
cd spaCy

python -m venv .env
source .env/bin/activate

# make sure you are using the latest pip
python -m pip install -U pip setuptools wheel

pip install -r requirements.txt
pip install --no-build-isolation --editable .

To install with extras:

pip install --no-build-isolation --editable .[lookups,cuda102]

🚦 Run tests

spaCy comes with an extensive test suite. In order to run the tests, you'll usually want to clone the repository and build spaCy from source. This will also install the required development dependencies and test utilities defined in the requirements.txt.

Alternatively, you can run pytest on the tests from within the installed spacy package. Don't forget to also install the test utilities via spaCy's requirements.txt:

pip install -r requirements.txt
python -m pytest --pyargs spacy