chcaa/da_dacy_large_ner_fine_grained

DaCy_large_ner_fine_grained

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.

Feature	Description
Name	da_dacy_large_ner_fine_grained
Version	0.1.0
spaCy	>=3.5.0,<3.6.0
Default Pipeline	transformer, ner
Components	transformer, ner
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	DANSK - Danish Annotations for NLP Specific TasKs (chcaa) chcaa/dfm-encoder-large-v1 (CHCAA)
License	apache-2.0
Author	Centre for Humanities Computing Aarhus

Label Scheme

View label scheme (18 labels for 1 components)

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

Accuracy

Type	Score
ENTS_F	82.32
ENTS_P	81.30
ENTS_R	83.37
TRANSFORMER_LOSS	41138.73
NER_LOSS	103772.53

Training

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