Update README.md

README.md

@@ -27,7 +27,34 @@ model-index:
 This a ONNX quantized model and is fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset using [tasinho/text-classification-goemotions](https://huggingface.co/tasinhoque/text-classification-goemotions) as teacher model.
 The original model can be found [here](https://huggingface.co/minuva/MiniLMv2-goemotions-v2)
 
-# Usage
+# Optimum
+
+## Installation
+
+Install from source: 
+```bash
+python -m pip install optimum[onnxruntime]@git+https://github.com/huggingface/optimum.git
+```
+
+
+## Run the Model
+```py
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoTokenizer, pipeline
+
+model = ORTModelForSequenceClassification.from_pretrained('minuva/MiniLMv2-goemotions-v2-onnx', provider="CPUExecutionProvider")
+tokenizer = AutoTokenizer.from_pretrained('minuva/MiniLMv2-goemotions-v2-onnx', use_fast=True, model_max_length=256, truncation=True, padding='max_length')
+
+pipe = pipeline(task='text-classification', model=model, tokenizer=tokenizer, )
+texts = ["that's wrong", "can you please answer me?"]
+pipe(texts)
+# [{'label': 'anger', 'score': 0.9727636575698853},
+# {'label': 'love', 'score': 0.9874765276908875}]
+```
+# ONNX Runtime only
+
+A lighter solution for deployment
+
 
 ## Installation
 ```bash
@@ -114,6 +141,7 @@ for result in results:
     res.append(max_score)
 
 res
+
 # [('anger', 0.9745745062828064), ('love', 0.9884329438209534)]
 ```
 # Training hyperparameters