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README.rst
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README.rst
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.. image:: https://travis-ci.org/spacy-io/spaCy.svg?branch=master
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:target: https://travis-ci.org/spacy-io/spaCy
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==============================
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spaCy: Industrial-strength NLP
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spaCy: Industrial-strength NLP
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==============================
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******************************
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spaCy is a library for advanced natural language processing in Python and Cython.
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.. image:: http://i.imgur.com/jYM3iOy.png
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:target: https://spacy.io
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Documentation and details: https://spacy.io/
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.. image:: https://travis-ci.org/spacy-io/spaCy.svg?branch=master
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:target: https://travis-ci.org/spacy-io/spaCy
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spaCy is built on the very latest research, but it isn't researchware. It was
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spaCy is a library for advanced natural language processing in Python and
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designed from day 1 to be used in real products. It's commercial open-source
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Cython. `See here <https://spacy.io>`_ for documentation and details. spaCy is built on
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software, released under the MIT license.
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the very latest research, but it isn't researchware. It was designed from day 1
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to be used in real products. It's commercial open-source software, released under
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the MIT license.
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Features
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Features
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--------
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========
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* Labelled dependency parsing (91.8% accuracy on OntoNotes 5)
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* Labelled dependency parsing (91.8% accuracy on OntoNotes 5)
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* Named entity recognition (82.6% accuracy on OntoNotes 5)
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* Named entity recognition (82.6% accuracy on OntoNotes 5)
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* Part-of-speech tagging (97.1% accuracy on OntoNotes 5)
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* Part-of-speech tagging (97.1% accuracy on OntoNotes 5)
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* Easy to use word vectors
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* Easy to use word vectors
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* All strings mapped to integer IDs
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* All strings mapped to integer IDs
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* Export to numpy data arrays
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* Export to numpy data arrays
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* Alignment maintained to original string, ensuring easy mark up calculation
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* Alignment maintained to original string, ensuring easy mark up calculation
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* Range of easy-to-use orthographic features.
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* Range of easy-to-use orthographic features.
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* No pre-processing required. spaCy takes raw text as input, warts and newlines and all.
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* No pre-processing required. spaCy takes raw text as input, warts and newlines and all.
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Top Peformance
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Top Peformance
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--------------
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==============
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* Fastest in the world: <50ms per document. No faster system has ever been
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* Fastest in the world: <50ms per document. No faster system has ever been
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announced.
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announced.
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* Accuracy within 1% of the current state of the art on all tasks performed
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* Accuracy within 1% of the current state of the art on all tasks performed
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(parsing, named entity recognition, part-of-speech tagging). The only more
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(parsing, named entity recognition, part-of-speech tagging). The only more
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accurate systems are an order of magnitude slower or more.
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accurate systems are an order of magnitude slower or more.
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Supports
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Supports
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--------
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========
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* CPython 2.6, 2.7, 3.3, 3.4, 3.5 (only 64 bit)
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* CPython 2.6, 2.7, 3.3, 3.4, 3.5 (only 64 bit)
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* OSX
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* OSX
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* Linux
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* Linux
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* Windows (Cygwin, MinGW, Visual Studio)
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* Windows (Cygwin, MinGW, Visual Studio)
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Install spaCy
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=============
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2016-05-0 0.101.0: Fixed German model
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spaCy is compatible with 64-bit CPython 2.6+/3.3+ and runs on Unix/Linux, OS X
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-------------------------------------
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and Windows. Source and binary packages are available via
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`pip <https://pypi.python.org/pypi/spacy>`_ and `conda <https://anaconda.org/spacy/spacy>`_.
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If there are no binary packages for your platform available please make sure that
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you have a working build enviroment set up. See notes on Ubuntu, OS X and Windows
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for details.
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**conda**
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.. code:: bash
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conda config --add channels spacy # only needed once
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conda install spacy
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**pip**
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When using pip it is generally recommended to install packages in a virtualenv to
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avoid modifying system state:
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.. code:: bash
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# make sure you are using a recent pip/virtualenv version
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python -m pip install -U pip virtualenv
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virtualenv .env
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source .env/bin/activate
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pip install spacy
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Python packaging is awkward at the best of times, and it's particularly tricky with
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C extensions, built via Cython, requiring large data files. So, please report issues
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as you encounter them.
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Install model
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=============
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After installation you need to download a language model. Currently only models for
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English and German, named ``en`` and ``de``, are available.
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.. code:: bash
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python -m spacy.en.download
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python -m spacy.de.download
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sputnik --name spacy en_glove_cc_300_1m_vectors # For better word vectors
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Then check whether the model was successfully installed:
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.. code:: bash
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python -c "import spacy; spacy.load('en'); print('OK')"
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The download command fetches and installs about 500 MB of data which it installs
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within the ``spacy`` package directory.
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Upgrading spaCy
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===============
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To upgrade spaCy to the latest release:
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**conda**
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.. code:: bash
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conda update spacy
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**pip**
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.. code:: bash
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pip install -U spacy
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Sometimes new releases require a new language model. Then you will have to upgrade to
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a new model, too. You can also force re-downloading and installing a new language model:
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.. code:: bash
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python -m spacy.en.download --force
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Compile from source
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===================
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The other way to install spaCy is to clone its GitHub repository and build it from
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source. That is the common way if you want to make changes to the code base.
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You'll need to make sure that you have a development enviroment consisting of a
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Python distribution including header files, a compiler, pip, virtualenv and git
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installed. The compiler part is the trickiest. How to do that depends on your
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system. See notes on Ubuntu, OS X and Windows for details.
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.. code:: bash
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# make sure you are using recent pip/virtualenv versions
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python -m pip install -U pip virtualenv
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# find git install instructions at https://git-scm.com/downloads
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git clone https://github.com/spacy-io/spaCy.git
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cd spaCy
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virtualenv .env && source .env/bin/activate
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pip install -r requirements.txt
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pip install -e .
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Compared to regular install via pip and conda `requirements.txt <requirements.txt>`_
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additionally installs developer dependencies such as cython.
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**Ubuntu**
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Install system-level dependencies via ``apt-get``:
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.. code:: bash
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sudo apt-get install build-essential python-dev git
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**OS X**
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Install a recent version of XCode, including the so-called "Command Line Tools".
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OS X ships with Python and git preinstalled.
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**Windows**
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Install a version of Visual Studio Express or higher that matches the version
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that was used to compile your Python interpreter. For official distributions
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these are VS 2008 (Python 2.7), VS 2010 (Python 3.4) and VS 2015 (Python 3.5).
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**Workaround for obsolete system Python**
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If you're stuck using a system with an old version of Python, and you don't
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have root access, we've prepared a bootstrap script to help you compile a local
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Python install. Run:
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.. code:: bash
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curl https://raw.githubusercontent.com/spacy-io/gist/master/bootstrap_python_env.sh | bash && source .env/bin/activate
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**Run tests**
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spaCy comes with an extensive test suite. First, find out where spaCy is
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installed:
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.. code:: bash
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python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))"
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Then run ``pytest`` on that directory. The flags ``--vectors``, ``--slow``
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and ``--model`` are optional and enable additional tests:
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.. code:: bash
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# make sure you are using recent pytest version
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python -m pip install -U pytest
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python -m pytest <spacy-directory> --vectors --model --slow
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API Documentation and Usage Examples
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====================================
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For the detailed documentation, check out the `spaCy website <https://spacy.io/docs/>`_.
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* `Usage Examples <https://spacy.io/docs/#examples>`_
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* `API <https://spacy.io/docs/#api>`_
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* `Annotation Specification <https://spacy.io/docs/#annotation>`_
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* `Tutorials <https://spacy.io/docs/#tutorials>`_
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Changelog
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=========
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2016-05-10 `v0.101.0 <../../releases/tag/0.101.0>`_: *Fixed German model*
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-------------------------------------------------------------------------
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* Fixed bug that prevented German parses from being deprojectivised.
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* Fixed bug that prevented German parses from being deprojectivised.
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* Bug fixes to sentence boundary detection.
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* Bug fixes to sentence boundary detection.
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* Add rich comparison methods to the Lexeme class.
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* Add rich comparison methods to the Lexeme class.
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* Add missing ``Doc.has_vector`` and ``Span.has_vector`` properties.
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* Add missing ``Span.sent`` property.
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* Add missing Doc.has_vector and Span.has_vector properties.
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2016-05-05 `v0.100.7 <../../releases/tag/0.100.7>`_: *German!*
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--------------------------------------------------------------
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* Add missing Span.sent property.
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spaCy finally supports another language, in addition to English. We're lucky
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to have Wolfgang Seeker on the team, and the new German model is just the
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beginning. Now that there are multiple languages, you should consider loading
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2016-05-05 v0.100.7: German!
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spaCy via the ``load()`` function. This function also makes it easier to load extra
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----------------------------
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word vector data for English:
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spaCy finally supports another language, in addition to English. We're lucky to have Wolfgang Seeker on the team, and the new German model is just the beginning.
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Now that there are multiple languages, you should consider loading spaCy via the load() function. This function also makes it easier to load extra word vector data for English:
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.. code:: python
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.. code:: python
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@ -79,8 +237,9 @@ Now that there are multiple languages, you should consider loading spaCy via the
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en_nlp = spacy.load('en', vectors='en_glove_cc_300_1m_vectors')
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en_nlp = spacy.load('en', vectors='en_glove_cc_300_1m_vectors')
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de_nlp = spacy.load('de')
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de_nlp = spacy.load('de')
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To support use of the load function, there are also two new helper functions: spacy.get_lang_class and spacy.set_lang_class.
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To support use of the load function, there are also two new helper functions:
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Once the German model is loaded, you can use it just like the English model:
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``spacy.get_lang_class`` and ``spacy.set_lang_class``. Once the German model is
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loaded, you can use it just like the English model:
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.. code:: python
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.. code:: python
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@ -92,20 +251,130 @@ Once the German model is loaded, you can use it just like the English model:
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# (u'ist', 1, 2)
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# (u'ist', 1, 2)
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# (u'sind', 1, 3)
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# (u'sind', 1, 3)
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The German model provides tokenization, POS tagging, sentence boundary detection, syntactic dependency parsing, recognition of organisation, location and person entities, and word vector representations trained on a mix of open subtitles and Wikipedia data. It doesn't yet provide lemmatisation or morphological analysis, and it doesn't yet recognise numeric entities such as numbers and dates.
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The German model provides tokenization, POS tagging, sentence boundary detection,
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syntactic dependency parsing, recognition of organisation, location and person
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entities, and word vector representations trained on a mix of open subtitles and
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Wikipedia data. It doesn't yet provide lemmatisation or morphological analysis,
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and it doesn't yet recognise numeric entities such as numbers and dates.
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Bugfixes
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**Bugfixes**
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--------
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* spaCy < 0.100.7 had a bug in the semantics of the Token.__str__ and Token.__unicode__ built-ins: they included a trailing space.
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* spaCy < 0.100.7 had a bug in the semantics of the ``Token.__str__`` and ``Token.__unicode__`` built-ins: they included a trailing space.
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* Improve handling of "infixed" hyphens. Previously the tokenizer struggled with multiple hyphens, such as "well-to-do".
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* Improve handling of "infixed" hyphens. Previously the tokenizer struggled with multiple hyphens, such as "well-to-do".
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* Improve handling of periods after mixed-case tokens
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* Improve handling of periods after mixed-case tokens
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* Improve lemmatization for English special-case tokens
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* Improve lemmatization for English special-case tokens
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* Fix bug that allowed spaces to be treated as heads in the syntactic parse
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* Fix bug that allowed spaces to be treated as heads in the syntactic parse
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* Fix bug that led to inconsistent sentence boundaries before and after serialisation.
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* Fix bug that led to inconsistent sentence boundaries before and after serialisation.
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* Fix bug from deserialising untagged documents.
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* Fix bug from deserialising untagged documents.
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2016-03-08 `v0.100.6 <../../releases/tag/0.100.6>`_: *Add support for GloVe vectors*
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------------------------------------------------------------------------------------
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This release offers improved support for replacing the word vectors used by spaCy.
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To install Stanford's GloVe vectors, trained on the Common Crawl, just run:
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.. code:: bash
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sputnik --name spacy install en_glove_cc_300_1m_vectors
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To reduce memory usage and loading time, we've trimmed the vocabulary down to 1m entries.
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This release also integrates all the code necessary for German parsing. A German model
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will be released shortly. To assist in multi-lingual processing, we've added a ``load()``
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function. To load the English model with the GloVe vectors:
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.. code:: python
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spacy.load('en', vectors='en_glove_cc_300_1m_vectors')
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2016-02-07 `v0.100.5 <../../releases/tag/0.100.5>`_
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---------------------------------------------------
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Fix incorrect use of header file, caused from problem with thinc
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2016-02-07 `v0.100.4 <../../releases/tag/0.100.4>`_: *Fix OSX problem introduced in 0.100.3*
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--------------------------------------------------------------------------------------------
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Small correction to right_edge calculation
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2016-02-06 `v0.100.3 <../../releases/tag/0.100.3>`_
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---------------------------------------------------
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Support multi-threading, via the ``.pipe`` method. spaCy now releases the GIL around the
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parser and entity recognizer, so systems that support OpenMP should be able to do
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shared memory parallelism at close to full efficiency.
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We've also greatly reduced loading time, and fixed a number of bugs.
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2016-01-21 `v0.100.2 <../../releases/tag/0.100.2>`_
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---------------------------------------------------
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Fix data version lock that affected v0.100.1
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2016-01-21 `v0.100.1 <../../releases/tag/0.100.1>`_: *Fix install for OSX*
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--------------------------------------------------------------------------
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v0.100 included header files built on Linux that caused installation to fail on OSX.
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This should now be corrected. We also update the default data distribution, to
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include a small fix to the tokenizer.
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2016-01-19 `v0.100 <../../releases/tag/0.100>`_: *Revise setup.py, better model downloads, bug fixes*
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-----------------------------------------------------------------------------------------------------
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* Redo setup.py, and remove ugly headers_workaround hack. Should result in fewer install problems.
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* Update data downloading and installation functionality, by migrating to the Sputnik data-package manager. This will allow us to offer finer grained control of data installation in future.
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* Fix bug when using custom entity types in ``Matcher``. This should work by default when using the
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``English.__call__`` method of running the pipeline. If invoking ``Parser.__call__`` directly to do NER,
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you should call the ``Parser.add_label()`` method to register your entity type.
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* Fix head-finding rules in ``Span``.
|
||||||
|
* Fix problem that caused ``doc.merge()`` to sometimes hang
|
||||||
|
* Fix problems in handling of whitespace
|
||||||
|
|
||||||
|
2015-11-08 `v0.99 <../../releases/tag/0.99>`_: *Improve span merging, internal refactoring*
|
||||||
|
-------------------------------------------------------------------------------------------
|
||||||
|
|
||||||
|
* Merging multi-word tokens into one, via the ``doc.merge()`` and ``span.merge()`` methods, no longer invalidates existing ``Span`` objects. This makes it much easier to merge multiple spans, e.g. to merge all named entities, or all base noun phrases. Thanks to @andreasgrv for help on this patch.
|
||||||
|
* Lots of internal refactoring, especially around the machine learning module, thinc. The thinc API has now been improved, and the spacy._ml wrapper module is no longer necessary.
|
||||||
|
* The lemmatizer now lower-cases non-noun, noun-verb and non-adjective words.
|
||||||
|
* A new attribute, ``.rank``, is added to Token and Lexeme objects, giving the frequency rank of the word.
|
||||||
|
|
||||||
|
2015-11-03 `v0.98 <../../releases/tag/0.98>`_: *Smaller package, bug fixes*
|
||||||
|
---------------------------------------------------------------------------
|
||||||
|
|
||||||
|
* Remove binary data from PyPi package.
|
||||||
|
* Delete archive after downloading data
|
||||||
|
* Use updated cymem, preshed and thinc packages
|
||||||
|
* Fix information loss in deserialize
|
||||||
|
* Fix ``__str__`` methods for Python2
|
||||||
|
|
||||||
|
2015-10-23 `v0.97 <../../releases/tag/0.97>`_: *Load the StringStore from a json list, instead of a text file*
|
||||||
|
--------------------------------------------------------------------------------------------------------------
|
||||||
|
|
||||||
|
* Fix bugs in download.py
|
||||||
|
* Require ``--force`` to over-write the data directory in download.py
|
||||||
|
* Fix bugs in ``Matcher`` and ``doc.merge()``
|
||||||
|
|
||||||
|
2015-10-19 `v0.96 <../../releases/tag/0.96>`_: *Hotfix to .merge method*
|
||||||
|
------------------------------------------------------------------------
|
||||||
|
|
||||||
|
* Fix bug that caused text to be lost after ``.merge``
|
||||||
|
* Fix bug in Matcher when matched entities overlapped
|
||||||
|
|
||||||
|
2015-10-18 `v0.95 <../../releases/tag/0.95>`_: *Bugfixes*
|
||||||
|
---------------------------------------------------------
|
||||||
|
|
||||||
|
* Reform encoding of symbols
|
||||||
|
* Fix bugs in ``Matcher``
|
||||||
|
* Fix bugs in ``Span``
|
||||||
|
* Add tokenizer rule to fix numeric range tokenization
|
||||||
|
* Add specific string-length cap in Tokenizer
|
||||||
|
* Fix ``token.conjuncts```
|
||||||
|
|
||||||
|
2015-10-09 `v0.94 <../../releases/tag/0.94>`_
|
||||||
|
---------------------------------------------
|
||||||
|
|
||||||
|
* Fix memory error that caused crashes on 32bit platforms
|
||||||
|
* Fix parse errors caused by smart quotes and em-dashes
|
||||||
|
|
||||||
|
2015-09-22 `v0.93 <../../releases/tag/0.93>`_
|
||||||
|
---------------------------------------------
|
||||||
|
|
||||||
|
Bug fixes to word vectors
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue
Block a user