EvanD/xlm-roberta-base-danish-ner-daner

xlm-roberta model trained on DaNe, performing 97.1 f1-Macro on test set.

Test metric	Results
test_f1_mac_dane_ner	0.9713183641433716
test_loss_dane_ner	0.11384682357311249
test_prec_mac_dane_ner	0.8712055087089539
test_rec_mac_dane_ner	0.8684446811676025

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("EvanD/xlm-roberta-base-danish-ner-daner")
ner_model = AutoModelForTokenClassification.from_pretrained("EvanD/xlm-roberta-base-danish-ner-daner")

nlp = pipeline("ner", model=ner_model, tokenizer=tokenizer, aggregation_strategy="simple")
example = "Mit navn er Amadeus Wolfgang, og jeg bor i Berlin"

ner_results = nlp(example)
print(ner_results)