Update README.md

README.md

@@ -4,4 +4,41 @@ library_name: "transformers.js"
 
 https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 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
+```
+
+You can then use the model for computing embeddings like this:
+
+```js
+import { pipeline } from '@xenova/transformers';
+
+// Create a feature-extraction pipeline
+const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+
+// Compute sentence embeddings
+const sentences = ['This is an example sentence', 'Each sentence is converted'];
+const output = await extractor(sentences, { pooling: 'mean', normalize: true });
+console.log(output);
+// Tensor {
+//   dims: [ 2, 384 ],
+//   type: 'float32',
+//   data: Float32Array(768) [ 0.04592696577310562, 0.07328180968761444, ... ],
+//   size: 768
+// }
+```
+
+You can convert this Tensor to a nested JavaScript array using `.tolist()`:
+```js
+console.log(output.tolist());
+// [
+//   [ 0.04592696577310562, 0.07328180968761444, 0.05400655046105385, ... ],
+//   [ 0.08188057690858841, 0.10760223120450974, -0.013241755776107311, ... ]
+// ]
+```
+
+
 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`).