From b180019eeea2b32cb9e44043222d3e09a64cd999 Mon Sep 17 00:00:00 2001
From: Ella Charlaix <ella@huggingface.co>
Date: Mon, 21 Oct 2024 14:57:46 +0200
Subject: [PATCH] add sentence-transformers and timm example to documentation

---
 .../onnxruntime/usage_guides/models.mdx       | 56 +++++++++++++++++++
 1 file changed, 56 insertions(+)

diff --git a/docs/source/onnxruntime/usage_guides/models.mdx b/docs/source/onnxruntime/usage_guides/models.mdx
index 1292e755c0..f4c9b70246 100644
--- a/docs/source/onnxruntime/usage_guides/models.mdx
+++ b/docs/source/onnxruntime/usage_guides/models.mdx
@@ -43,6 +43,62 @@ Once your model was [exported to the ONNX format](https://huggingface.co/docs/op
   image = pipeline(prompt).images[0]
 ```
 
+
+### Sentence Transformers models
+
+Once your model was [exported to the ONNX format](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model), you can load it by replacing the `AutoModel` class with the corresponding `ORTModelForFeatureExtraction`.
+
+```diff
+  from transformers import AutoTokenizer
+- from transformers import AutoModel
++ from optimum.onnxruntime import ORTModelForFeatureExtraction
+
+  model_id = "sentence-transformers/all-MiniLM-L6-v2"
+  tokenizer = AutoTokenizer.from_pretrained(model_id)
+- model = AutoModel.from_pretrained(model_id)
++ model = ORTModelForFeatureExtraction.from_pretrained(model_id, export=True)
+  inputs = tokenizer("This is an example sentence", return_tensors="pt")
+  outputs = model(**inputs)
+```
+
+You can also load your ONNX model directly using the `SentenceTransformer` class, just make sure to have `sentence-transformers>=3.2.1` installed. If the model wasn't already converted to ONNX, it will be converted automatically on-the-fly.
+
+```diff
+  from sentence_transformers import SentenceTransformer
+
+  model_id = "sentence-transformers/all-MiniLM-L6-v2"
+- model = SentenceTransformer(model_id)
++ model = SentenceTransformer(model_id, backend="onnx")
+
+  sentences = ["This is an example sentence", "Each sentence is converted"]
+  embeddings = model.encode(sentences)
+```
+
+
+### Timm models
+
+Once your model was [exported to the ONNX format](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model), you can load it by replacing the `create_model` with the corresponding `ORTModelForImageClassification` class.
+
+
+```diff
+  import requests
+  from PIL import Image
+- from timm import create_model
+  from timm.data import resolve_data_config, create_transform
++ from optimum.onnxruntime import ORTModelForImageClassification
+
+  model_id = "timm/mobilenetv3_large_100.ra_in1k"
+- model = create_model(model_id, pretrained=True)
++ model = ORTModelForImageClassification.from_pretrained(model_id, export=True)
+  transform = create_transform(**resolve_data_config(model.config.pretrained_cfg, model=model))
+  url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"
+  image = Image.open(requests.get(url, stream=True).raw)
+  inputs = transform(image).unsqueeze(0)
+  outputs = model(inputs)
+```
+
+
+
 ## Converting your model to ONNX on-the-fly
 
 In case your model wasn't already [converted to ONNX](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model), [`~optimum.onnxruntime.ORTModel`] includes a method to convert your model to ONNX on-the-fly.