Update README.md

README.md

@@ -4,4 +4,52 @@ library_name: "transformers.js"
 
 https://huggingface.co/thenlper/gte-small 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 to compute embeddings like this:
+
+```js
+import { pipeline } from '@xenova/transformers';
+
+// Create a feature-extraction pipeline
+const extractor = await pipeline('feature-extraction', 'Xenova/gte-small');
+
+// Compute sentence embeddings
+const sentences = ['That is a happy person', 'That is a very happy person'];
+const output = await extractor(sentences, { pooling: 'mean', normalize: true });
+console.log(output);
+// Tensor {
+//   dims: [ 2, 384 ],
+//   type: 'float32',
+//   data: Float32Array(768) [ -0.053555335849523544, 0.00843878649175167, ... ],
+//   size: 768
+// }
+
+// Compute cosine similarity
+import { cos_sim } from '@xenova/transformers';
+console.log(cos_sim(output[0].data, output[1].data))
+// 0.9798319649182318
+```
+
+You can convert this Tensor to a nested JavaScript array using `.tolist()`:
+```js
+console.log(output.tolist());
+// [
+//   [ -0.053555335849523544, 0.00843878649175167, 0.06234041228890419, ... ],
+//   [ -0.049980051815509796, 0.03879701718688011, 0.07510733604431152, ... ]
+// ]
+```
+
+By default, an 8-bit quantized version of the model is used, but you can choose to use the full-precision (fp32) version by specifying `{ quantized: false }` in the `pipeline` function:
+```js
+const extractor = await pipeline('feature-extraction', 'Xenova/gte-small', { quantized: false });
+```
+
+---
+
 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`).