8c29268749
* Update errors * Remove beam for now (maybe) Remove beam_utils Update setup.py Remove beam * Remove GoldParse WIP on removing goldparse Get ArcEager compiling after GoldParse excise Update setup.py Get spacy.syntax compiling after removing GoldParse Rename NewExample -> Example and clean up Clean html files Start updating tests Update Morphologizer * fix error numbers * fix merge conflict * informative error when calling to_array with wrong field * fix error catching * fixing language and scoring tests * start testing get_aligned * additional tests for new get_aligned function * Draft create_gold_state for arc_eager oracle * Fix import * Fix import * Remove TokenAnnotation code from nonproj * fixing NER one-to-many alignment * Fix many-to-one IOB codes * fix test for misaligned * attempt to fix cases with weird spaces * fix spaces * test_gold_biluo_different_tokenization works * allow None as BILUO annotation * fixed some tests + WIP roundtrip unit test * add spaces to json output format * minibatch utiltiy can deal with strings, docs or examples * fix augment (needs further testing) * various fixes in scripts - needs to be further tested * fix test_cli * cleanup * correct silly typo * add support for MORPH in to/from_array, fix morphologizer overfitting test * fix tagger * fix entity linker * ensure test keeps working with non-linked entities * pipe() takes docs, not examples * small bug fix * textcat bugfix * throw informative error when running the components with the wrong type of objects * fix parser tests to work with example (most still failing) * fix BiluoPushDown parsing entities * small fixes * bugfix tok2vec * fix renames and simple_ner labels * various small fixes * prevent writing dummy values like deps because that could interfer with sent_start values * fix the fix * implement split_sent with aligned SENT_START attribute * test for split sentences with various alignment issues, works * Return ArcEagerGoldParse from ArcEager * Update parser and NER gold stuff * Draft new GoldCorpus class * add links to to_dict * clean up * fix test checking for variants * Fix oracles * Start updating converters * Move converters under spacy.gold * Move things around * Fix naming * Fix name * Update converter to produce DocBin * Update converters * Allow DocBin to take list of Doc objects. * Make spacy convert output docbin * Fix import * Fix docbin * Fix compile in ArcEager * Fix import * Serialize all attrs by default * Update converter * Remove jsonl converter * Add json2docs converter * Draft Corpus class for DocBin * Work on train script * Update Corpus * Update DocBin * Allocate Doc before starting to add words * Make doc.from_array several times faster * Update train.py * Fix Corpus * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests * Skip tests that cause crashes * Skip test causing segfault * Remove GoldCorpus * Update imports * Update after removing GoldCorpus * Fix module name of corpus * Fix mimport * Work on parser oracle * Update arc_eager oracle * Restore ArcEager.get_cost function * Update transition system * Update test_arc_eager_oracle * Remove beam test * Update test * Unskip * Unskip tests * add links to to_dict * clean up * fix test checking for variants * Allow DocBin to take list of Doc objects. * Fix compile in ArcEager * Serialize all attrs by default Move converters under spacy.gold Move things around Fix naming Fix name Update converter to produce DocBin Update converters Make spacy convert output docbin Fix import Fix docbin Fix import Update converter Remove jsonl converter Add json2docs converter * Allocate Doc before starting to add words * Make doc.from_array several times faster * Start updating converters * Work on train script * Draft Corpus class for DocBin Update Corpus Fix Corpus * Update DocBin Add missing strings when serializing * Update train.py * Fix parser model * Start debugging arc_eager oracle * Update header * Fix parser declaration * Xfail some tests Skip tests that cause crashes Skip test causing segfault * Remove GoldCorpus Update imports Update after removing GoldCorpus Fix module name of corpus Fix mimport * Work on parser oracle Update arc_eager oracle Restore ArcEager.get_cost function Update transition system * Update tests Remove beam test Update test Unskip Unskip tests * Add get_aligned_parse method in Example Fix Example.get_aligned_parse * Add kwargs to Corpus.dev_dataset to match train_dataset * Update nonproj * Use get_aligned_parse in ArcEager * Add another arc-eager oracle test * Remove Example.doc property Remove Example.doc Remove Example.doc Remove Example.doc Remove Example.doc * Update ArcEager oracle Fix Break oracle * Debugging * Fix Corpus * Fix eg.doc * Format * small fixes * limit arg for Corpus * fix test_roundtrip_docs_to_docbin * fix test_make_orth_variants * fix add_label test * Update tests * avoid writing temp dir in json2docs, fixing 4402 test * Update test * Add missing costs to NER oracle * Update test * Work on Example.get_aligned_ner method * Clean up debugging * Xfail tests * Remove prints * Remove print * Xfail some tests * Replace unseen labels for parser * Update test * Update test * Xfail test * Fix Corpus * fix imports * fix docs_to_json * various small fixes * cleanup * Support gold_preproc in Corpus * Support gold_preproc * Pass gold_preproc setting into corpus * Remove debugging * Fix gold_preproc * Fix json2docs converter * Fix convert command * Fix flake8 * Fix import * fix output_dir (converted to Path by typer) * fix var * bugfix: update states after creating golds to avoid out of bounds indexing * Improve efficiency of ArEager oracle * pull merge_sent into iob2docs to avoid Doc creation for each line * fix asserts * bugfix excl Span.end in iob2docs * Support max_length in Corpus * Fix arc_eager oracle * Filter out uannotated sentences in NER * Remove debugging in parser * Simplify NER alignment * Fix conversion of NER data * Fix NER init_gold_batch * Tweak efficiency of precomputable affine * Update onto-json default * Update gold test for NER * Fix parser test * Update test * Add NER data test * Fix convert for single file * Fix test * Hack scorer to avoid evaluating non-nered data * Fix handling of NER data in Example * Output unlabelled spans from O biluo tags in iob_utils * Fix unset variable * Return kept examples from init_gold_batch * Return examples from init_gold_batch * Dont return Example from init_gold_batch * Set spaces on gold doc after conversion * Add test * Fix spaces reading * Improve NER alignment * Improve handling of missing values in NER * Restore the 'cutting' in parser training * Add assertion * Print epochs * Restore random cuts in parser/ner training * Implement Doc.copy * Implement Example.copy * Copy examples at the start of Language.update * Don't unset example docs * Tweak parser model slightly * attempt to fix _guess_spaces * _add_entities_to_doc first, so that links don't get overwritten * fixing get_aligned_ner for one-to-many * fix indexing into x_text * small fix biluo_tags_from_offsets * Add onto-ner config * Simplify NER alignment * Fix NER scoring for partially annotated documents * fix indexing into x_text * fix test_cli failing tests by ignoring spans in doc.ents with empty label * Fix limit * Improve NER alignment * Fix count_train * Remove print statement * fix tests, we're not having nothing but None * fix clumsy fingers * Fix tests * Fix doc.ents * Remove empty docs in Corpus and improve limit * Update config Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> |
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.buildkite | ||
.github | ||
bin | ||
examples | ||
include | ||
spacy | ||
website | ||
.gitignore | ||
azure-pipelines.yml | ||
CITATION | ||
CONTRIBUTING.md | ||
fabfile.py | ||
LICENSE | ||
Makefile | ||
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 statistical models and word vectors, and currently supports tokenization for 60+ languages. It features state-of-the-art speed, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration. It's commercial open-source software, released under the MIT license.
💫 Version 2.3 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 v2.3 | New features, backwards incompatibilities and migration guide. |
API Reference | The detailed reference for spaCy's API. |
Models | Download statistical language models for spaCy. |
Universe | Libraries, extensions, demos, books and courses. |
Changelog | Changes and version history. |
Contribute | How to contribute to the spaCy project and code base. |
💬 Where to ask questions
The spaCy project is maintained by @honnibal and @ines, along with core contributors @svlandeg and @adrianeboyd. 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 | GitHub Issue Tracker |
👩💻 Usage Questions | Stack Overflow · Gitter Chat · Reddit User Group |
🗯 General Discussion | Gitter Chat · Reddit User Group |
Features
- Non-destructive tokenization
- Named entity recognition
- Support for 50+ languages
- pretrained statistical models and word vectors
- State-of-the-art speed
- Easy deep learning integration
- Part-of-speech tagging
- Labelled dependency parsing
- Syntax-driven sentence segmentation
- Built in visualizers for syntax and NER
- Convenient string-to-hash mapping
- Export to numpy data arrays
- Efficient binary serialization
- Easy model packaging and deployment
- 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
)
⚠️ Important note for Python 3.8: We can't yet ship pre-compiled binary wheels for spaCy that work on Python 3.8, as we're still waiting for our CI providers and other tooling to support it. This means that in order to run spaCy on Python 3.8, you'll need a compiler installed and compile the library and its Cython dependencies locally. If this is causing problems for you, the easiest solution is to use Python 3.7 in the meantime.
pip
Using pip, spaCy releases are available as source packages and binary wheels (as
of v2.0.13
).
pip install spacy
To install additional data tables for lemmatization and normalization in
spaCy v2.2+ 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 for v2.2+ plus normalization data for v2.3+, 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 spacy
conda
Thanks to our great community, we've finally re-added conda support. You can now
install spaCy via conda-forge
:
conda install -c conda-forge spacy
For the feedstock including the build recipe and configuration, check out this repository. Improvements and pull requests to the recipe and setup are always appreciated.
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 1.x to spaCy 2.x, see the migration guide.
Download models
As of v1.7.0, models 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 Models | Detailed model descriptions, accuracy figures and benchmarks. |
Models Documentation | Detailed usage instructions. |
# download best-matching version of specific model for your spaCy installation
python -m spacy download en_core_web_sm
# pip install .tar.gz archive from path or URL
pip install /Users/you/en_core_web_sm-2.2.0.tar.gz
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz
Loading and using models
To load a model, use spacy.load()
with the model name, a shortcut link 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. See notes on Ubuntu, OS X and Windows for details.
# make sure you are using the latest pip
python -m pip install -U pip
git clone https://github.com/explosion/spaCy
cd spaCy
python -m venv .env
source .env/bin/activate
export PYTHONPATH=`pwd`
pip install -r requirements.txt
python setup.py build_ext --inplace
Compared to regular install via pip, requirements.txt additionally installs developer dependencies such as Cython. 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.
Ubuntu
Install system-level dependencies via apt-get
:
sudo apt-get install build-essential python-dev git
macOS / OS X
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.
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 find out where spaCy is installed and run pytest
on
that directory. Don't forget to also install the test utilities via spaCy's
requirements.txt
:
python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))"
pip install -r path/to/requirements.txt
python -m pytest <spacy-directory>
See the documentation for more details and examples.