News and Updates#

2.7.0 (15/05/23)

  • Updated the DaCy models to version 0.2.0, including a small, medium and large

    • Added beta support for Coreference Resolution! 🤩

    • Added beta support for Named Entity Linking!

    • Updated dependency parsing and part-of-speech tagging to use the latest version of the DDT treebank 🌳

    • Added a trainable lemmatizer, notably improving the lemmatization

    • All model are trained using the intersection between the CDT and the DDT treebanks (so actually trained on less data than before) 🤯

      • This includes the annotations from DaNED, DaCoref and DaNE

  • Large model:

    • obtained state-of-the-art performance on:

      • Dependency parsing

      • Part-of-speech tagging

      • Morphological tagging

      • lemmatization (from 84.91 to 95.89!)

    • Improved performance on:

      • Reduced performance for NER down to 87.38 but we recommend either using the nlp.add_pipe("dacy/ner") to add the SotA ScandiNER model to your pipeline or use one of the new fine-grained NER models.

    • Added support

      • Coreference Resolution, performance isn’t great yet, but it’s a start!

      • Named entity linking, with a precision of 0.86 but recall is still low due to a lacking knowledge base

  • Medium model:

    • Consistent improvements across all tasks:

      • Notable performance gain for NER from an F1 of 81.79 to 85.82

      • Notable performance gain for lemmatization from an ACC 84.91 to 94

    • Added support

      • Coreference Resolution

      • Named entity linking

  • Small model:

    • Consistent improvements across all tasks

    • Added support:

      • Coreference Resolution

      • Named entity linking

  • Fixes a variety of issues:

  • Removed support for DaCy model version 0.1.0, if you need to use these models you will have to use DaCy <= 2.0.0

  • What is next?

    • A coreference resolution only model

    • Better named entity linking by

      • Improving the annotations of DaNED which currently annotate PERSONS using the QID reference to the name among other things

      • Improving the knowledge base which currently is the main source of low recall

    • Examine model generalization using DANSK and whether we can improve the generalization

    • and more!

2.6.0 (10/04/23)

  • Added support three new models (small, medium, large) for fine-grained NER, which lets you do NER on up to 18 different entities! 🤩

    • You can add these models to your pipeline using nlp.add_pipe("dacy/ner-fine-grained", config={"size": "small"})

2.5.0 (10/04/23)

  • Removed support for 0.0.0 models. To use these models you will now have to use DaCy=<2.4.3

2.3.0 (05/01/23)

  • New tutorial added for using DaCy with textdescriptives. You can find it here

2.2.10 (05/01/23)

  • Added support for spaCy 3.4.0

    • This required the wrapped component model to change name from e.g. dacy.ner to dacy/ner. As the . is no longer allowed by spaCy.

  • Added support for the state-of-the-art NER model by Dan Nielsen

    • You can add this model to your pipeline using nlp.add_pipe("dacy/ner")

2.0.0 (27/06/22): The Spandaur Update

  • Added models for hate-speech detection and classification

  • A large part of DaCy is now moved to seperate packages to allow for more versatility:

    • Now uses spacy-wrap for including existing models in DaCy.

    • Removed augmenters, they are now available through the external package augmenty

    • Removed the rule-based sentiment pipeline instead we recommend using asent

  • Removed support for multiple installs, thus pip install dacy[all] or dacy[large] is no longer required. This should simplify installation processes and avoid errors

  • Documentation

    • New tutorial on using the sentiment models, including emotions detection, subjectivity detection and polarity classifcation.

    • New tutorial on using the hate speech classification and detection.

    • Multiple updated on function and package documentation

  • Multiple bugfixes

1.2.0 (04/11/21)

  • Removed DaNLP dependency, now DaNLP models is downloaded directly from Huggingface’s model hub which is faster and more stable 🌟.

  • Removed the readability module, we instead recommend you use the more extensive textdescriptives package developed by [HLasse]( and I for extracting readability and other text metrics.

  • Added support for the configuring the default the model location with the environmental variable ‘DACY_CACHE_DIR’ thanks to a PR by dhpullack 🙏.

1.1.0 (23/07/21)

  • DaCy in now available on the Huggingface model hub 🤗 . Including detailed performance descriptions of biases and robustness.

  • It also got a brand new online demo - try it out!

  • And more, including documentation update and prettier prints.

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

    • And new tutorials for both 📖

  • A new beautiful hand-drawn logo 🤩

  • A behavioural test for biases and robustness in Danish NLP pipelines 🧐

  • DaCy is now officially supported by the Centre for Humanities Computing at Aarhus University

  • The first paper on DaCy; check it out as a preprint and code for reproducing it here! 🌟

0.4.1 (03/06/21)

  • DaCy now has a stunningly looking documentation site 🌟

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 🏃

0.1.1 (24/03/21)

  • DaCy includes a wrapped version of major Danish sentiment analysis software including the models by DaNLP, as well as code for wrapping any sequence classification model into its pipeline 🤩

  • Tutorials is added to introduce the above functionality

0.0.1 (25/02/21)

  • DaCy launches with a medium-sized and a large language model obtaining state-of-the-art on Named entity recognition, part-of-speech tagging and dependency parsing for Danish 🇩🇰