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--- |
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language: |
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- en |
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inference: false |
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pipeline_tag: token-classification |
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tags: |
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- ner |
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- bert |
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license: mit |
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datasets: |
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- conll2003 |
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--- |
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# ONNX version of dslim/bert-base-NER |
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**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. |
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`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). |
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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. |
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## Usage |
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Loading the model requires the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library installed. |
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```python |
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from optimum.onnxruntime import ORTModelForTokenClassification |
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from transformers import AutoTokenizer, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("laiyer/bert-base-NER-onnx") |
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model = ORTModelForTokenClassification.from_pretrained("laiyer/bert-base-NER-onnx") |
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ner = pipeline( |
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task="ner", |
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model=model, |
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tokenizer=tokenizer, |
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) |
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ner_output = ner("My name is John Doe.") |
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print(ner_output) |
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``` |
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