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)