Update README.md

README.md

@@ -4,4 +4,25 @@ library_name: "transformers.js"
 
 https://huggingface.co/openai/clip-vit-base-patch32 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:** Perform zero-shot image classification with the `pipeline` API.
+```js
+const classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32');
+const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
+const output = await classifier(url, ['tiger', 'horse', 'dog']);
+// [
+//   { score: 0.9993917942047119, label: 'tiger' },
+//   { score: 0.0003519294841680676, label: 'horse' },
+//   { score: 0.0002562698791734874, label: 'dog' }
+// ]
+```
+
+---
+
 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`).