Update README.md

README.md

@@ -4,4 +4,28 @@ library_name: transformers.js
 
 https://huggingface.co/facebook/dinov2-small-imagenet1k-1-layer 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/@xenova/transformers) using:
+```bash
+npm i @xenova/transformers
+```
+
+**Example:** Image classification w/ `Xenova/dinov2-small-imagenet1k-1-layer`.
+
+```javascript
+import { pipeline} from '@xenova/transformers';
+
+// Create image classification pipeline
+const classifier = await pipeline('image-classification', 'Xenova/dinov2-small-imagenet1k-1-layer');
+
+// Classify an image
+const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
+const output = await classifier(url);
+console.log(output)
+// [{ label: 'tabby, tabby cat', score: 0.7468826770782471 }]
+```
+
+---
+
 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`).