Rudar mmBERT Slovenian NER Model
Slovenian named-entity recognition (NER) model — token classification over the four CoNLL entity classes. Part of the Rudar family of Slovenian information-extraction models.
- Language: Slovenian (
sl) - Task: Named-entity recognition (token classification)
- Base model:
jhu-clsp/mmBERT-base - Labels (BIO):
PER,LOC,ORG,MISC - Benchmark: SloBench Slovenian NER (Task 11)
Usage
from transformers import pipeline
ner = pipeline(
"token-classification",
model="martinkorelic/rudar-mmbert-slv-ner",
aggregation_strategy="average", # recommended — gives clean entity spans
)
ner("Predsednik vlade Robert Golob je v Ljubljani obiskal sedež podjetja Petrol .")
# [{'entity_group': 'PER', 'word': 'Robert Golob'},
# {'entity_group': 'LOC', 'word': 'Ljubljani'},
# {'entity_group': 'ORG', 'word': 'Petrol'}]
Use
aggregation_strategy="average"(or"max") —"simple"may fragment subword spans.
Labels
| Class | Description |
|---|---|
PER | People |
LOC | Locations |
ORG | Organizations |
MISC | Miscellaneous named entities |
Emitted as BIO tags (B-PER, I-PER, B-LOC, …).
Training
Fine-tuned from jhu-clsp/mmBERT-base in two stages (a broad stage followed by a
refinement stage) on Slovenian text — on the order of ~100k samples per stage.
Intended use & limitations
- Slovenian-language NER over the four CoNLL classes; suited to news / general-domain text.
- Expects cased, reasonably well-formed input.
- Not intended for languages other than Slovenian.