Update README.md

add quantized onnx model

README.md

@@ -10,12 +10,12 @@ widget:
 DistilCamemBERT-NER
 ===================
 
-We present DistilCamemBERT-NER which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine tuned for the NER (Named Entity Recognition) task for the French language. The work is inspired by [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) based on the [CamemBERT](https://huggingface.co/camembert-base) model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which **divides the inference time by 2** with the same consumption power thanks to [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base).
+We present DistilCamemBERT-NER, which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine-tuned for the NER (Named Entity Recognition) task for the French language. The work is inspired by [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) based on the [CamemBERT](https://huggingface.co/camembert-base) model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which **divides the inference time by two** with the same consumption power thanks to [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base).
 
 Dataset
 -------
 
-The dataset used is [wikiner_fr](https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr) which represents ~170k sentences labelized in 5 categories :
+The dataset used is [wikiner_fr](https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr), which represents ~170k sentences labeled in 5 categories :
 * PER: personality ;
 * LOC: location ;
 * ORG: organization ;
@@ -125,6 +125,22 @@ result
   'end': 409}]
 ```
 
+### Optimum + ONNX
+```python
+from optimum.onnxruntime import ORTModelForTokenClassification
+from transformers import AutoTokenizer, pipeline
+
+HUB_MODEL = "cmarkea/distilcamembert-base-nli"
+tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL)
+model = ORTModelForTokenClassification.from_pretrained(HUB_MODEL)
+onnx_qa = pipeline("token-classification", model=model, tokenizer=tokenizer)
+
+# Quantized onnx model
+quantized_model = ORTModelForTokenClassification.from_pretrained(
+    HUB_MODEL, file_name="model_quantized.onnx"
+)
+```
+
 Citation
 --------
 ```bibtex