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---
pipeline_tag: token-classification
tags:
- named-entity-recognition
- sequence-tagger-model
widget:
- text: A nevem Amadeus Wolfgang és Berlinben élek
inference:
parameters:
aggregation_strategy: simple
grouped_entities: true
language:
- hu
---
xlm-roberta model trained on [hungarian ner](https://flairnlp.github.io/docs/tutorial-training/how-to-load-prepared-dataset) 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 |
```python
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)
``` |