
Same model as [flair/ner-dutch-large](https://huggingface.co/flair/ner-dutch-large) but transformed back to pure huggingface pytorch for performance purposes


```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("EvanD/dutch-ner-xlm-conll2003")
ner_model = AutoModelForTokenClassification.from_pretrained("EvanD/dutch-ner-xlm-conll2003")

nlp = pipeline("ner", model=ner_model, tokenizer=tokenizer, aggregation_strategy="simple")
example = "George Washington ging naar Washington"

ner_results = nlp(example)
print(ner_results)

# {
#     "start_pos": 0,
#     "end_pos": 17,
#     "text": "George Washington",
#     "score": 0.9999986886978149,
#     "label": "PER"
# }
# {
#     "start_pos": 28,
#     "end_pos": 38,
#     "text": "Washington",
#     "score": 0.9999939203262329,
#     "label": "LOC"
# }
```

{"language": ["nl"], "tags": ["named-entity-recognition", "sequence-tagger-model"], "pipeline_tag": "token-classification", "widget": [{"text": "George Washington ging naar Washington"}], "inference": {"parameters": {"aggregation_strategy": "simple", "grouped_entities": true}}}
transformers pytorch xlm-roberta token-classification named-entity-recognition sequence-tagger-model nl autotrain_compatible endpoints_compatible region:us