
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)
```
{"language": ["hu"], "tags": ["named-entity-recognition", "sequence-tagger-model"], "pipeline_tag": "token-classification", "widget": [{"text": "A nevem Amadeus Wolfgang \u00e9s Berlinben \u00e9lek"}], "inference": {"parameters": {"aggregation_strategy": "simple", "grouped_entities": true}}}
transformers pytorch xlm-roberta token-classification named-entity-recognition sequence-tagger-model hu autotrain_compatible endpoints_compatible region:us