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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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base_model: dslim/bert-base-NER |
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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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### LLM Guard |
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[Anonymize scanner](https://llm-guard.com/input_scanners/anonymize/) |
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## Community |
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Join our Slack to give us feedback, connect with the maintainers and fellow users, ask questions, |
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or engage in discussions about LLM security! |
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<a href="https://join.slack.com/t/laiyerai/shared_invite/zt-28jv3ci39-sVxXrLs3rQdaN3mIl9IT~w"><img src="https://github.com/laiyer-ai/llm-guard/blob/main/docs/assets/join-our-slack-community.png?raw=true" width="200"></a> |
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