Update README.md

README.md

@@ -8,4 +8,27 @@ license: apache-2.0
 
 https://huggingface.co/timm/mobilenetv4_conv_small.e2400_r224_in1k with ONNX weights to be compatible with Transformers.js.
 
+## Usage (Transformers.js)
+
+If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+**Example:** Perform image classification with `onnx-community/mobilenetv4s-webnn`
+
+```js
+import { pipeline } from '@huggingface/transformers';
+
+// Create an image classification pipeline
+const classifier = await pipeline('image-classification', 'onnx-community/mobilenetv4s-webnn');
+
+// Classify an image
+const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
+const output = await classifier(url);
+// [{ label: 'tiger, Panthera tigris', score: 0.903573540929381 }]
+```
+
+---
+
 Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).