Update README.md

README.md

@@ -1,7 +1,38 @@
 ---
 library_name: transformers.js
+pipeline_tag: image-classification
+license: other
 ---
 
 https://huggingface.co/timm/fastvit_ma36.apple_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/@xenova/transformers) using:
+```bash
+npm i @xenova/transformers
+```
+
+**Example:** Perform image classification with `Xenova/fastvit_ma36.apple_in1k`.
+```js
+import { pipeline } from '@xenova/transformers';
+
+// Create an image classification pipeline
+const classifier = await pipeline('image-classification', 'Xenova/fastvit_ma36.apple_in1k');
+
+// Classify an image
+const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
+const output = await classifier(url, { topk: 5 });
+console.log(output);
+// [
+//   { label: 'tiger, Panthera tigris', score: 0.546970784664154 },
+//   { label: 'tiger cat', score: 0.16752418875694275 },
+//   { label: 'lynx, catamount', score: 0.0018565849168226123 },
+//   { label: 'jaguar, panther, Panthera onca, Felis onca', score: 0.0017013729084283113 },
+//   { label: 'dhole, Cuon alpinus', score: 0.000908317684661597 }
+// ]
+```
+
+---
+
 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`).