EvanD/xlm-roberta-base-ukrainian-ner-ukrner

xlm-roberta model trained on ukrainian ner dataset from flair

Test metric	Results
test_f1_mac_ukr_ner	0.9900672435760498
test_loss_ukr_ner	0.054602641612291336
test_prec_mac_ukr_ner	0.9386032819747925
test_rec_mac_ukr_ner	0.9383019208908081

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

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

nlp = pipeline("ner", model=ner_model, tokenizer=tokenizer, aggregation_strategy="simple")
example = "Мене звуть Амадей Вольфганг, я живу в Берліні"

ner_results = nlp(example)
print(ner_results)