Update README.md

README.md

@@ -41,7 +41,39 @@ This model is the ONNX version of [https://huggingface.co/SamLowe/roberta-base-g
 
 #### Using Optimum Library ONNX Classes
 
-To follow.
+Optimum library has equivalents (starting `ORT`) for the main Transformers classes, so these models can be used with the familiar constructs. The only extra property needed is `file_name` on the model creation, which in the below example specifies the quantized (INT8) model. 
+
+```python
+sentences = ["ONNX is seriously fast for small batches. Impressive"]
+
+from transformers import AutoTokenizer, pipeline
+from optimum.onnxruntime import ORTModelForSequenceClassification
+
+model_id = "SamLowe/roberta-base-go_emotions-onnx"
+file_name = "onnx/model_quantized.onnx"
+
+model = ORTModelForSequenceClassification.from_pretrained(model_id, file_name=file_name)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+onnx_classifier = pipeline(
+    task="text-classification",
+    model=model,
+    tokenizer=tokenizer,
+    top_k=None,
+    function_to_apply="sigmoid",  # optional as is the default for the task
+)
+
+model_outputs = onnx_classifier(sentences)
+# gives a list of outputs, each a list of dicts (one per label)
+
+print(model_outputs)
+# E.g.
+# [[{'label': 'admiration', 'score': 0.9203393459320068},
+#   {'label': 'approval', 'score': 0.0560273639857769},
+#   {'label': 'neutral', 'score': 0.04265536740422249},
+#   {'label': 'gratitude', 'score': 0.015126707963645458},
+# ...
+```
 
 #### Using ONNXRuntime