Update README.md

README.md

@@ -4,4 +4,31 @@ library_name: transformers.js
 
 https://huggingface.co/YituTech/conv-bert-medium-small with ONNX weights to be compatible with 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:** Feature extraction w/ `Xenova/conv-bert-medium-small`.
+
+```javascript
+import { pipeline } from '@xenova/transformers';
+
+// Create feature extraction pipeline
+const classifier = await pipeline('feature-extraction', 'Xenova/conv-bert-medium-small', { quantized: false });
+
+// Perform feature extraction
+const output = await classifier('This is a test sentence.');
+console.log(output)
+// Tensor {
+//   dims: [ 1, 8, 384 ],
+//   type: 'float32',
+//   data: Float32Array(3072) [ 0.5316754579544067, 0.5101212859153748, ... ],
+//   size: 3072
+// }
+```
+
+---
+
+
 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`).