83ac227bd3
The new spacy pretrain command implemented BERT/ULMFit/etc-like transfer learning, using our Language Modelling with Approximate Outputs version of BERT's cloze task. Pretraining is convenient, but in some ways it's a bit of a strange solution. All we're doing is initialising the weights. At the same time, we're putting a lot of work into our optimisation so that it's less sensitive to initial conditions, and more likely to find good optima. I discuss this a bit in the pseudo-rehearsal blog post: https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting Support semi-supervised learning in spacy train One obvious way to improve these pretraining methods is to do multi-task learning, instead of just transfer learning. This has been shown to work very well: https://arxiv.org/pdf/1809.08370.pdf . This patch makes it easy to do this sort of thing. Add a new argument to spacy train, --raw-text. This takes a jsonl file with unlabelled data that can be used in arbitrary ways to do semi-supervised learning. Add a new method to the Language class and to pipeline components, .rehearse(). This is like .update(), but doesn't expect GoldParse objects. It takes a batch of Doc objects, and performs an update on some semi-supervised objective. Move the BERT-LMAO objective out from spacy/cli/pretrain.py into spacy/_ml.py, so we can create a new pipeline component, ClozeMultitask. This can be specified as a parser or NER multitask in the spacy train command. Example usage: python -m spacy train en ./tmp ~/data/en-core-web/train/nw.json ~/data/en-core-web/dev/nw.json --pipeline parser --raw-textt ~/data/unlabelled/reddit-100k.jsonl --vectors en_vectors_web_lg --parser-multitasks cloze Implement rehearsal methods for pipeline components The new --raw-text argument and nlp.rehearse() method also gives us a good place to implement the the idea in the pseudo-rehearsal blog post in the parser. This works as follows: Add a new nlp.resume_training() method. This allocates copies of pre-trained models in the pipeline, setting things up for the rehearsal updates. It also returns an optimizer object. This also greatly reduces confusion around the nlp.begin_training() method, which randomises the weights, making it not suitable for adding new labels or otherwise fine-tuning a pre-trained model. Implement rehearsal updates on the Parser class, making it available for the dependency parser and NER. During rehearsal, the initial model is used to supervise the model being trained. The current model is asked to match the predictions of the initial model on some data. This minimises catastrophic forgetting, by keeping the model's predictions close to the original. See the blog post for details. Implement rehearsal updates for tagger Implement rehearsal updates for text categoriz |
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| spacy | ||
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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 pre-trained statistical models and word vectors, and currently supports tokenization for 30+ languages. It features the fastest syntactic parser in the world, 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 2.1 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 v2.0 | 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. 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.
- Bug Reports: GitHub Issue Tracker
- Usage Questions: Stack Overflow · Gitter Chat · Reddit User Group
- General Discussion: Gitter Chat · Reddit User Group
Features
- Fastest syntactic parser in the world
- Named entity recognition
- Non-destructive tokenization
- Support for 30+ languages
- Pre-trained statistical models and word vectors
- 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
- State-of-the-art speed
- 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 2.7, 3.4+ (only 64 bit)
- Package managers: pip · conda (via
conda-forge)
pip
Using pip, spaCy releases are available as source packages and binary wheels
(as of v2.0.13).
pip install spacy
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 config --add channels conda-forge
conda install 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. |
# out-of-the-box: download best-matching default model
python -m spacy download en
# download best-matching version of specific model for your spaCy installation
python -m spacy download en_core_web_lg
# pip install .tar.gz archive from path or URL
pip install /Users/you/en_core_web_sm-2.0.0.tar.gz
Loading and using models
To load a model, use spacy.load() with the model's shortcut link:
import spacy
nlp = spacy.load('en')
doc = nlp(u'This is a sentence.')
If you've installed a model via pip, you can also import it directly and
then call its load() method:
import spacy
import en_core_web_sm
nlp = en_core_web_sm.load()
doc = nlp(u'This is a sentence.')
📖 For more info and examples, check out the models documentation.
Support for older versions
If you're using an older version (v1.6.0 or below), you can still download
and install the old models from within spaCy using python -m spacy.en.download all
or python -m spacy.de.download all. The .tar.gz archives are also
attached to the v1.6.0 release.
To download and install the models manually, unpack the archive, drop the
contained directory into spacy/data and load the model via spacy.load('en')
or spacy.load('de').
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. For official distributions these are VS 2008 (Python 2.7), VS 2010 (Python 3.4) and VS 2015 (Python 3.5).
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.