Token Classification
spaCy
Danish
dacy
danish
morphological analysis
dependency parsing
named entity recognition
Eval Results (legacy)
Instructions to use chcaa/da_dacy_edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use chcaa/da_dacy_edge with spaCy:
!pip install https://huggingface.co/chcaa/da_dacy_edge/resolve/main/da_dacy_edge-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("da_dacy_edge") # Importing as module. import da_dacy_edge nlp = da_dacy_edge.load() - Notebooks
- Google Colab
- Kaggle
adding citation of dacy paper
Browse files
README.md
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# DaCy edge
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DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines.
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This model is the **edge Dacy pipeline**, trained for Danish linguistic
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annotation and downstream NLP tasks with efficient CPU inference.
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To read more check out the [DaCy repository](https://github.com/centre-for-humanities-computing/DaCy) for material on how to use DaCy and reproduce the results.
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### Training
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This model was trained using [spaCy](https://spacy.io)
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# DaCy edge
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DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines (Enevoldsen et al., 2021).
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This model is the **edge Dacy pipeline**, trained for Danish linguistic
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annotation and downstream NLP tasks with efficient CPU inference.
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To read more check out the [DaCy repository](https://github.com/centre-for-humanities-computing/DaCy) for material on how to use DaCy and reproduce the results.
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### Training
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This model was trained using [spaCy](https://spacy.io)
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### References
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Enevoldsen, K., Hansen, L., & Nielbo, K. L. (2021). DaCy: A Unified Framework for Danish NLP. https://ceur-ws.org/Vol-2989/short_paper24.pdf
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