DaCy: A SpaCy NLP Pipeline for Danish

DaCy is a Danish preprocessing pipeline trained in SpaCy. It has achieved State-of-the-Art performance on Named entity recognition, part-of-speech tagging and dependency parsing for Danish. This repository contains material for using the DaCy, reproducing the results and guides on usage of the package. Furthermore, it also a behavioural test for biases and robustness of Danish NLP pipelines.

📰 News

• 1.0.0 (09/07/21)
  • DaCy version 1.0.0 releases.
    • Including a series of augmenters with a few specifically designed for Danish
    • Code for behavioural tests of NLP pipelines
    • A new tutorial for both 📖
  • The first paper on DaCy check it out as a preprint here and code for reproducing it! 🌟
  • A new beautiful hand-drawn logo 🤩
  • A behavioural test for biases and robustness in Danish NLP pipelines 🧐
• 0.4.1 (03/06/21)
  • DaCy now have a stunningly looking documentation site 🌟. It even got a dark mode!
• 0.3.1 (01/06/21)
  • DaCy's tests now cover 99% of its codebase 🎉
  • DaCy's test suite is now being applied for all major operating systems instead of just linux 👩‍💻
• 0.2.2 (25/05/21)
  • The new Danish Model Senda was added to DaCy
• 0.2.1 (30/03/21)
  • DaCy now includes a small model for efficient processing based on the Danish Ælæctra 🏃

🔧 Installation

it currently only possible to download DaCy directly from GitHub, however this can be done quite easily using:

pip install git+https://github.com/KennethEnevoldsen/DaCy

git clone https://github.com/KennethEnevoldsen/DaCy.git
cd DaCy
pip install .

👩‍💻 Usage

To use the model you first have to download either the small, medium, or large model. To see a list of all available models:

import dacy
for model in dacy.models():
    print(model)
# da_dacy_small_tft-0.0.0
# da_dacy_medium_tft-0.0.0
# da_dacy_large_tft-0.0.0

To download and load a model simply execute:

nlp = dacy.load("da_dacy_medium_tft-0.0.0")

Which will download the model to the .dacy directory in your home directory.

To download the model to a specific directory:

dacy.download_model("da_dacy_medium_tft-0.0.0", your_save_path)
nlp = dacy.load_model("da_dacy_medium_tft-0.0.0", your_save_path)

For more on how to use DaCy please check out our [documentation)

👩‍🏫 Tutorials and documentation

DaCy also includes a detailed documentation as well as a series of Jupyter notebook tutorial. If you do not have Jupyter Notebook installed, instructions for installing and running it can be found here. All the tutorials are located in the tutorials folder.

	Content	Google Colab
🌟 Getting Started	An introduction on how to use DaCy
📖 Documentation	The Documentation of DaCy
😡😂 Sentiment	A simple introduction to the new sentiment features in DaCy.
😎 wrapping a fine-tuned Tranformer	A guide on how to wrap an already fine-tuned transformer to and add it to your SpaCy pipeline using DaCy helper functions.

🦾 Performance and Training

The following table shows the performance on the DaNE test set when compared to other models. Highest scores are highlighted with bold and second highest is underlined.

Stanza uses the spacy-stanza implementation. The speed on the DaNLP model is as reported by the framework, which does not utilize batch input. However, given the model size, it can be expected to reach speeds comparable to DaCy medium. Empty cells indicate that the framework does not include the specific model.

Robustness and Biases

DaCy compares the performance of Danish language processing pipeline under a large variaty of augmentations to test the robustness and biases hereof. To find out more please check the website.

🤔 Issues and Usage Q&A

To ask questions, report issues or request features, please use the GitHub Issue Tracker. Question related to SpaCy is kindly referred to the SpaCy GitHub or forum.

FAQ

where_is_my_dacy()

pip install -r requirements.txt
pip install pytest

python -m pytest

python -m pytest dacy/tests/test_readability.py

pip install pytest-cov

python -m pytest--cov=.

Does DaCy run on X?

DaCy is intended to run on all major OS, this includes Windows (latest version), MacOS (Catalina) and the latest version of Linux (Ubuntu). Below you can see if DaCy passes its test suite for the system of interest. The first one indicated Linux. Please note these are only the systems DaCy is being actively tested on, if you run on a similar system (e.g. an earlier version of Linux) DaCy will likely run there as well.

Operating System	Status
Ubuntu (Latest)
MacOS (Catalina)
Windows (Latest)

# install sphinx, themes and extensions
pip install sphinx furo sphinx-copybutton sphinxext-opengraph

# generate html from documentations

make -C docs html

Acknowledgements

DaCy is a result of great open-source software and contributors. It wouldn't have been possible without the work by the SpaCy team which developed an integrated the software. Huggingface for developing Transformers and making model sharing convenient. BotXO for training and sharing the Danish BERT model and Malte Hojmark-Bertelsen for making it easily available and developing Ælæctra. A huge compliment also goes out to DaNLP which have made it easy to get access to Danish resources and even supplied some of the tagged data themselves.

References

If you use this library in your research, please kindly cite:

@inproceedings{dacy2021,
    title={DaCy: A Unified Framework for Danish NLP},
    author={Enevoldsen, Kenneth},
    year={2021}
}

To read more on this paper or to see the code for reproducing the results please check out the associated readme.

License

DaCy is released under the Apache License, Version 2.0. See the LICENSE file for more details.

Contact

For feature request issues and bugs please use the GitHub Issue Tracker. Otherwise, please use the Discussion Forums.