---
language:
- en
inference: false
pipeline_tag: token-classification
tags:
- ner
- bert
license: mit
datasets:
- conll2003
base_model: dslim/bert-large-NER
model-index:
- name: dslim/bert-large-NER
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9031688753722759
      verified: true
    - name: Precision
      type: precision
      value: 0.920025068328604
      verified: true
    - name: Recall
      type: recall
      value: 0.9193688678588825
      verified: true
    - name: F1
      type: f1
      value: 0.9196968510445761
      verified: true
    - name: loss
      type: loss
      value: 0.5085050463676453
      verified: true
---

# ONNX version of dslim/bert-large-NER

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

**bert-large-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-large-cased* model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) 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-large-NER-onnx")
model = ORTModelForTokenClassification.from_pretrained("laiyer/bert-large-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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