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)
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