CoolFace
Modelpublic

chcaa/da_dacy_small_ner_fine_grained

sourceHugging Faceapache-2.0updated 4y agoView on Hugging Face
0likes309downloads
Model Card

<a href="https://github.com/centre-for-humanities-computing/Dacy"><img src="https://centre-for-humanities-computing.github.io/DaCy/_static/icon.png" width="175" height="175" align="right" /></a>

DaCysmallnerfinegrained

DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analyzing Danish pipelines. At the time of publishing this model, also included in DaCy encorporates the only models for fine-grained NER using DANSK dataset - a dataset containing 18 annotation types in the same format as Ontonotes. Moreover, DaCy's largest pipeline has achieved State-of-the-Art performance on Named entity recognition, part-of-speech tagging and dependency parsing for Danish on the DaNE dataset. Check out the DaCy repository for material on how to use DaCy and reproduce the results. DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines.

For information about the use of this model as well as guides to its use, please refer to DaCys documentation.

FeatureDescription
Nameda_dacy_small_ner_fine_grained
Version0.1.0
spaCy>=3.5.0,<3.6.0
Default Pipelinetransformer, ner
Componentstransformer, ner
Vectors0 keys, 0 unique vectors (0 dimensions)
SourcesDANSK - Danish Annotations for NLP Specific TasKs (chcaa)<br />jonfd/electra-small-nordic (Jón Daðason)
Licenseapache-2.0
AuthorCentre for Humanities Computing Aarhus

Label Scheme

<details>

<summary>View label scheme (18 labels for 1 components)</summary>

ComponentLabels
`ner`CARDINAL, DATE, EVENT, FACILITY, GPE, LANGUAGE, LAW, LOCATION, MONEY, NORP, ORDINAL, ORGANIZATION, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK OF ART

</details>

Accuracy

TypeScore
ENTS_F77.56
ENTS_P78.12
ENTS_R77.02
TRANSFORMER_LOSS90762.74
NER_LOSS99794.90

For progression in loss and performance on the dev set during training, please refer to the Weights and Biases run, HERE