Create README.md

README.md

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