dacy 2.2.7

A Danish pipeline trained in SpaCy that has achieved State-of-the-Art performance on all dependency parsing, NER and POS-tagging for Danish

DaCy: An efficient NLP Pipeline for Danish

DaCy is a Danish natural language preprocessing framework made with SpaCy. Its largest pipeline has achieved State-of-the-Art performance on Named entity recognition, part-of-speech tagging and dependency parsing for Danish. Feel free to try out the demo. This repository contains material for using DaCy, reproducing the results and guides on usage of the package. Furthermore, it also contains behavioural tests for biases and robustness of Danish NLP pipelines.

🔧 Installation

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

pip install dacy

👩‍💻 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_trf-0.1.0
# da_dacy_medium_trf-0.1.0
# da_dacy_large_trf-0.1.0

To download and load a model simply execute:

nlp = dacy.load("da_dacy_medium_tfrf-0.1.0")
# 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_trf-0.1.0", your_save_path)
nlp = dacy.load_model("da_dacy_medium_trf-0.1.0", your_save_path)

📖 Documentation

DaCy includes detailed documentation as well as a series of Jupyter notebook tutorials. 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.

Documentation
📚 Getting started	Guides and instructions on how to use DaCy and its features.
🦾 Performance	A detailed description of the performance of DaCy and comparison with similar Danish models
😎 Demo	A simple Streamlit demo to try out the augmenters.
📰 News and changelog	New additions, changes and version history.
🎛 API References	The detailed reference for DaCy's API. Including function documentation
🙋 FAQ	Frequently asked questions

Training and reproduction

the folder training contains a SpaCy project which will allow for reproduction of the results. This folder also includes the evaluation metrics on DaNE and scripts for downloading the required data. For more information, please see the training readme.

Want to learn more about how DaCy initially came to be, check out this blog post.

💬 Where to ask questions

To ask 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.

Type
🚨 Bug Reports	GitHub Issue Tracker
🎁 Feature Requests & Ideas	GitHub Issue Tracker
👩‍💻 Usage Questions	GitHub Discussions
🗯 General Discussion	GitHub Discussions

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. Multiple parties including Certainly.io and Malte Hojmark-Bertelsen for making their models publicly available. Alexandra Institute for developing and maintaining DaNLP which has made it easy to get access to Danish resources and even supplied some of the tagged data themselves.