Update README.md

README.md

@@ -9,29 +9,33 @@ tags:
 - transformers
 ---
 
-# sentence-transformers/distiluse-base-multilingual-cased-v2
+# ONNX convert distiluse-base-multilingual-cased-v2
+## Conversion of [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2)
+This is a [sentence-transformers](https://www.SBERT.net) ONNX model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model outputs `last_hidden_state` similar like original sentence-transformer implementation.
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
 
-
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
+## Usage (HuggingFace Optimum)
+Using this model becomes easy when you have [optimum](https://github.com/huggingface/optimum) installed:
 ```
-pip install -U sentence-transformers
+python -m pip install optimum
 ```
-
 Then you can use the model like this:
+```python
+from optimum.onnxruntime.modeling_ort import ORTModelForCustomTasks
 
+model = ORTModelForCustomTasks.from_pretrained("lorenpe2/distiluse-base-multilingual-cased-v2")
+tokenizer = AutoTokenizer.from_pretrained("lorenpe2/distiluse-base-multilingual-cased-v2")
+inputs = tokenizer("I love burritos!", return_tensors="pt")
+pred = model(**inputs)
+```
+You will also be able to leverage the pipeline API in transformers:
 ```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+from transformers import pipeline
 
-model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v2')
-embeddings = model.encode(sentences)
-print(embeddings)
+onnx_extractor = pipeline("feature-extraction", model=model, tokenizer=tokenizer)
+text = "I love burritos!"
+pred = onnx_extractor(text)
 ```