Update README.md

README.md

@@ -22,6 +22,57 @@ datasets:
 # multi-qa-MiniLM-distill-onnx-L6-cos-v1
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
 
+## Usage (ONNX runtime)
+Using optimum
+
+```
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+from transformers import AutoTokenizer
+
+from transformers import Pipeline
+import torch.nn.functional as F
+import torch 
+
+# copied from the model card
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+class SentenceEmbeddingPipeline(Pipeline):
+    def _sanitize_parameters(self, **kwargs):
+        # we don't have any hyperameters to sanitize
+        preprocess_kwargs = {}
+        return preprocess_kwargs, {}, {}
+      
+    def preprocess(self, inputs):
+        encoded_inputs = self.tokenizer(inputs, padding=True, truncation=True, return_tensors='pt')
+        return encoded_inputs
+
+    def _forward(self, model_inputs):
+        outputs = self.model(**model_inputs)
+        return {"outputs": outputs, "attention_mask": model_inputs["attention_mask"]}
+
+    def postprocess(self, model_outputs):
+        # Perform pooling
+        sentence_embeddings = mean_pooling(model_outputs["outputs"], model_outputs['attention_mask'])
+        # Normalize embeddings
+        sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+        return sentence_embeddings
+
+# load optimized model
+onnx_path = "./models/cos-v1-best/"
+model = ORTModelForFeatureExtraction.from_pretrained(onnx_path, file_name="model_quantized.onnx")
+
+# create optimized pipeline
+tokenizer = AutoTokenizer.from_pretrained(onnx_path, use_fast=True)
+optimized_emb = SentenceEmbeddingPipeline(model=model, tokenizer=tokenizer)
+pred1 = optimized_emb("Hello world!")
+pred2 = optimized_emb("I hate everything.")
+
+print(pred1[0].dot(pred2[0]))
+```
 
 ## Usage (Sentence-Transformers)
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: