Token Classification
Transformers
PyTorch
Ukrainian
xlm-roberta
named-entity-recognition
sequence-tagger-model
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("EvanD/xlm-roberta-base-ukrainian-ner-ukrner")
model = AutoModelForTokenClassification.from_pretrained("EvanD/xlm-roberta-base-ukrainian-ner-ukrner", device_map="auto")Quick Links
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)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EvanD/xlm-roberta-base-ukrainian-ner-ukrner")