---
pipeline_tag: token-classification
tags:
  - named-entity-recognition
  - sequence-tagger-model
widget:
- text: "George Washington ging naar Washington"
inference:
  parameters:
    aggregation_strategy: "simple"
language:
- nl
---

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"
# }
```