---
language:
- en
inference: false
pipeline_tag: token-classification
tags:
- ner
- bert
license: mit
datasets:
- conll2003
base_model: dslim/bert-base-NER
---

# ONNX version of dslim/bert-base-NER

**This model is a conversion of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) to ONNX** format using the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library.

`bert-base-NER` is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC).

Specifically, this model is a `bert-base-cased` model that was fine-tuned on the English version of the standard `CoNLL-2003 Named Entity Recognition` dataset.

## 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-base-NER-onnx")
model = ORTModelForTokenClassification.from_pretrained("laiyer/bert-base-NER-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

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or engage in discussions about LLM security!

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