DaCy: An efficient 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 as the first version to pypi! 📦
    • 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 and code for reproducing it here! 🌟
  • A new beautiful hand-drawn logo 🤩
  • A test for biases and robustness in Danish NLP pipelines 🧐
  • DaCy is now officially supported by the Centre for Humanities Computing at Aarhus University
• 0.4.1 (03/06/21)
  • DaCy now has a stunningly looking documentation site 🌟. It even has a dark mode!
• 0.3.1 (01/06/21)
  • DaCy's tests now cover 99% of its codebase 🎉
  • DaCy's test suite is now 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

To get started using DaCy simply install it using pip by running the following line in your terminal:

pip install dacy

pip install dacy[all]

git clone https://github.com/centre-for-humanities-computing/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")
# or equivalently
nlp = dacy.load("medium")

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 includes 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.
🍒 Augmentation	A guide on how to augment text using the DaCy and SpaCy augmenters.
⛑ Fairness and Robustness	A guide on how to use augmenters to measure model robustness and biases using 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. Questions related to SpaCy are kindly referred to the SpaCy GitHub or forum. Otherwise, please use the discussion Forums.

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 and 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 cite:

@inproceedings{dacy2021,
    title={DaCy: A Unified Framework for Danish NLP},
    author={Enevoldsen, Kenneth and Hansen, Lasse and Nielbo, Kristoffer},
    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.