Update README.md

README.md

@@ -1,7 +1,47 @@
 ---
 library_name: transformers.js
+pipeline_tag: feature-extraction
 ---
 
 https://huggingface.co/colbert-ir/colbertv2.0 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/colbertv2.0');
+
+// Compute sentence embeddings
+const sentences = ['Hello world', 'This is a sentence'];
+const output = await extractor(sentences, { pooling: 'mean', normalize: true });
+console.log(output);
+// Tensor {
+//   dims: [ 2, 768 ],
+//   type: 'float32',
+//   data: Float32Array(768) [ -0.008133978582918644, 0.00663341861218214, ... ],
+//   size: 768
+// }
+```
+
+You can convert this Tensor to a nested JavaScript array using `.tolist()`:
+```js
+console.log(output.tolist());
+// [
+//   [ -0.008133978582918644, 0.00663341861218214, 0.06555338203907013, ... ],
+//   [ -0.02630571834743023, 0.011146597564220428, 0.008737687021493912, ... ]
+// ]
+```
+
+---
+
 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`).