EvanD/xlm-roberta-base-hungarian-ner-huner

xlm-roberta model trained on hungarian ner dataset from flair

Test metric	Results
test_f1_mac_hu_ner	0.9962009787559509
test_loss_hu_ner	0.019755737856030464
test_prec_mac_hu_ner	0.9692726135253906
test_rec_mac_hu_ner	0.9708725810050964

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

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

nlp = pipeline("ner", model=ner_model, tokenizer=tokenizer, aggregation_strategy="simple")
example = "A nevem Amadeus Wolfgang és Berlinben élek"

ner_results = nlp(example)
print(ner_results)