---
language:
- en
inference: false
pipeline_tag: token-classification
tags:
- ner
license: mit
datasets:
- conll2003
base_model: dbmdz/bert-large-cased-finetuned-conll03-english

---

# ONNX version of dbmdz/bert-large-cased-finetuned-conll03-english

**This model is a conversion of [dbmdz/bert-large-cased-finetuned-conll03-english](https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english) to ONNX** format using the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library.

`dbmdz/bert-large-cased-finetuned-conll03-english` is designed for named-entity recognition (NER), capable of finding person, organization, and other entities in the text.

## Usage

Loading the model requires the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library installed.

```python
from optimum.onnxruntime import ORTModelForTokenClassification
from transformers import AutoTokenizer, pipeline


tokenizer = AutoTokenizer.from_pretrained("laiyer/bert-large-cased-finetuned-conll03-english-onnx")
model = ORTModelForTokenClassification.from_pretrained("laiyer/bert-large-cased-finetuned-conll03-english-onnx")
ner = pipeline(
    task="ner",
    model=model,
    tokenizer=tokenizer,
)

ner_output = ner("My name is John Doe.")
print(ner_output)
```

### LLM Guard

[Anonymize scanner](https://llm-guard.com/input_scanners/anonymize/)

## Community

Join our Slack to give us feedback, connect with the maintainers and fellow users, ask questions, 
or engage in discussions about LLM security!

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