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* Rename to spans_key for consistency * Implement spans length in debug data * Implement how span bounds and spans are obtained In this commit, I implemented how span boundaries (the tokens) around a given span and spans are obtained. I've put them in the compile_gold() function so that it's accessible later on. I will do the actual computation of the span and boundary distinctiveness in the main function above. * Compute for p_spans and p_bounds * Add computation for SD and BD * Fix mypy issues * Add weighted average computation * Fix compile_gold conditional logic * Add test for frequency distribution computation * Add tests for kl-divergence computation * Fix weighted average computation * Make tables more compact by rounding them * Add more descriptive checks for spans * Modularize span computation methods In this commit, I added the _get_span_characteristics and _print_span_characteristics functions so that they can be reusable anywhere. * Remove unnecessary arguments and make fxs more compact * Update a few parameter arguments * Add tests for print_span and get_span methods * Update API to talk about span characteristics in brief * Add better reporting of spans_length * Add test for span length reporting * Update formatting of span length report Removed '' to indicate that it's not a string, then sort the n-grams by their length, not by their frequency. * Apply suggestions from code review Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Show all frequency distribution when -V In this commit, I displayed the full frequency distribution of the span lengths when --verbose is passed. To make things simpler, I rewrote some of the formatter functions so that I can call them whenever. Another notable change is that instead of showing percentages as Integers, I showed them as floats (max 2-decimal places). I did this because it looks weird when it displays (0%). * Update logic on how total is computed The way the 90% thresholding is computed now is that we keep adding the percentages until we reach >= 90%. I also updated the wording and used the term "At least" to denote that >= 90% of your spans have these distributions. * Fix display when showing the threshold percentage * Apply suggestions from code review Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Add better phrasing for span information * Update spacy/cli/debug_data.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Add minor edits for whitespaces etc. Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> |
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spacy | ||
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azure-pipelines.yml | ||
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CITATION.cff | ||
CONTRIBUTING.md | ||
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netlify.toml | ||
pyproject.toml | ||
README.md | ||
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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 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.
📖 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 → |
💬 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