913d74f509
* [wip] Update * [wip] Update * Add initial port * [wip] Update * Fix all imports * Add spancat_exclusive to pipeline * [WIP] Update * [ci skip] Add breakpoint for debugging * Use spacy.SpanCategorizer.v1 as default archi * Update spacy/pipeline/spancat_exclusive.py Co-authored-by: kadarakos <kadar.akos@gmail.com> * [ci skip] Small updates * Use Softmax v2 directly from thinc * Cache the label map * Fix mypy errors However, I ignored line 370 because it opened up a bunch of type errors that might be trickier to solve and might lead to a more complicated codebase. * avoid multiplication with 1.0 Co-authored-by: kadarakos <kadar.akos@gmail.com> * Update spacy/pipeline/spancat_exclusive.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update component versions to v2 * Add scorer to docstring * Add _n_labels property to SpanCategorizer Instead of using len(self.labels) in initialize() I am using a private property self._n_labels. This achieves implementation parity and allows me to delete the whole initialize() method for spancat_exclusive (since it's now the same with spancat). * Inherit from SpanCat instead of TrainablePipe This commit changes the inheritance structure of Exclusive_Spancat, now it's inheriting from SpanCategorizer than TrainablePipe. This allows me to remove duplicate methods that are already present in the parent function. * Revert documentation link to spancat * Fix init call for exclusive spancat * Update spacy/pipeline/spancat_exclusive.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Import Suggester from spancat * Include zero_init.v1 for spancat * Implement _allow_extra_label to use _n_labels To ensure that spancat / spancat_exclusive cannot be resized after initialization, I inherited the _allow_extra_label() method from spacy/pipeline/trainable_pipe.pyx and used self._n_labels instead of len(self.labels) for checking. I think that changing it locally is a better solution rather than forcing each class that inherits TrainablePipe to use the self._n_labels attribute. Also note that I turned-off black formatting in this block of code because it reads better without the overhang. * Extend existing tests to spancat_exclusive In this commit, I extended the existing tests for spancat to include spancat_exclusive. I parametrized the test functions with 'name' (similar var name with textcat and textcat_multilabel) for each applicable test. TODO: Add overfitting tests for spancat_exclusive * Update documentation for spancat * Turn on formatting for allow_extra_label * Remove initializers in default config * Use DEFAULT_EXCL_SPANCAT_MODEL I also renamed spancat_exclusive_default_config into spancat_excl_default_config because black does some not pretty formatting changes. * Update documentation Update grammar and usage Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Clarify docstring for Exclusive_SpanCategorizer * Remove mypy ignore and typecast labels to list * Fix documentation API * Use a single variable for tests * Update defaults for number of rows Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Put back initializers in spancat config Whenever I remove model.scorer.init_w and model.scorer.init_b, I encounter an error in the test: SystemError: <method '__getitem__' of 'dict' objects> returned a result with an error set. My Thinc version is 8.1.5, but I can't seem to check what's causing the error. * Update spancat_exclusive docstring * Remove init_W and init_B parameters This commit is expected to fail until the new Thinc release. * Require thinc>=8.1.6 for serializable Softmax defaults * Handle zero suggestions to make tests pass I'm not sure if this is the most elegant solution. But what should happen is that the _make_span_group function MUST return an empty SpanGroup if there are no suggestions. The error happens when the 'scores' variable is empty. We cannot get the 'predicted' and other downstream vars. * Better approach for handling zero suggestions * Update website/docs/api/spancategorizer.md Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spancategorizer headers * Apply suggestions from code review Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Add default value in negative_weight in docs * Add default value in allow_overlap in docs * Update how spancat_exclusive is constructed In this commit, I added the following: - Put the default values of negative_weight and allow_overlap in the default_config dictionary. - Rename make_spancat -> make_exclusive_spancat * Run prettier on spancategorizer.mdx * Change exactly one -> at most one * Add suggester documentation in Exclusive_SpanCategorizer * Add suggester to spancat docstrings * merge multilabel and singlelabel spancat * rename spancat_exclusive to singlelable * wire up different make_spangroups for single and multilabel * black * black * add docstrings * more docstring and fix negative_label * don't rely on default arguments * black * remove spancat exclusive * replace single_label with add_negative_label and adjust inference * mypy * logical bug in configuration check * add spans.attrs[scores] * single label make_spangroup test * bugfix * black * tests for make_span_group with negative labels * refactor make_span_group * black * Update spacy/tests/pipeline/test_spancat.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * remove duplicate declaration * Update spacy/pipeline/spancat.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * raise error instead of just print * make label mapper private * update docs * run prettier * Update website/docs/api/spancategorizer.mdx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update website/docs/api/spancategorizer.mdx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/pipeline/spancat.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/pipeline/spancat.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/pipeline/spancat.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/pipeline/spancat.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * don't keep recomputing self._label_map for each span * typo in docs * Intervals to private and document 'name' param * Update spacy/pipeline/spancat.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/pipeline/spancat.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * add Tag to new features * replace tags * revert * revert * revert * revert * Update website/docs/api/spancategorizer.mdx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update website/docs/api/spancategorizer.mdx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * prettier * Fix merge * Update website/docs/api/spancategorizer.mdx * remove references to 'single_label' * remove old paragraph * Add spancat_singlelabel to config template * Format * Extend init config tests --------- Co-authored-by: kadarakos <kadar.akos@gmail.com> Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> |
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extra | ||
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spacy | ||
website | ||
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azure-pipelines.yml | ||
build-constraints.txt | ||
CITATION.cff | ||
CONTRIBUTING.md | ||
LICENSE | ||
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MANIFEST.in | ||
netlify.toml | ||
pyproject.toml | ||
README.md | ||
requirements.txt | ||
setup.cfg | ||
setup.py |
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 70+ 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.
💥 We'd love to hear more about your experience with spaCy! Fill out our survey here.
💫 Version 3.5 out now! Check out the release notes here.
📖 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. |
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 → | |
Bespoke advice for problem solving, strategy and analysis for applied NLP projects. Services include data strategy, code reviews, pipeline design and annotation coaching. Curious? Fill in 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 70+ 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