DaCy: An efficient and unified framework for danish NLP
DaCy is a Danish natural language preprocessing framework made with SpaCy. Its largest pipelines has achieved State-of-the-Art performance on Named entity recognition, part-of-speech tagging, dependency parsing and Danish, but it also integrates pipelines for emotion detection, sentiment analysis, coreference resolution and more.
🔧 Installation
You can install dacy via pip from PyPI:
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-0.2.0
# da_dacy_medium_trf-0.2.0
# da_dacy_large_trf-0.2.0
To download and load a model simply execute:
nlp = dacy.load("da_dacy_medium_trf-0.2.0")
# or equivalently (always loads the latest version)
nlp = dacy.load("medium")
To see more examples, see the documentation.
📖 Documentation
| 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 |
| 📰 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 range of folders with a SpaCy project for each model version. This allows for the reproduction of the results.
Want to learn more about how DaCy initially came to be, check out this blog post.
💬 Where to ask questions
To 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 | |
|---|---|
| 📚 FAQ | FAQ |
| 🚨 Bug Reports | GitHub Issue Tracker |
| 🎁 Feature Requests & Ideas | GitHub Issue Tracker |
| 👩💻 Usage Questions | GitHub Discussions |
| 🗯 General Discussion | GitHub Discussions |
3 maintainers
Metadata
3 maintainers
Release files for dacy 2.8.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dacy-2.8.1.tar.gz | 5.7 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dacy-2.8.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.8 MB
Release files / dacy-2.8.1.tar.gz
| Download URL | dacy-2.8.1.tar.gz |
|---|---|
| Size | 5.7 MB |
| Tags | Source |
|
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Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / dacy-2.8.1-py3-none-any.whl
| Download URL | dacy-2.8.1-py3-none-any.whl |
|---|---|
| Size | 50.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 2, 2026.
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