Create README.md

README.md

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+---
+language:
+- en
+inference: false
+pipeline_tag: token-classification
+tags:
+- ner
+- bert
+license: mit
+datasets:
+- conll2003
+---
+
+# 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)
+```