Update README.md

README.md

@@ -30,4 +30,42 @@ datasets:
   - embedding-data/WikiAnswers
 tags:
 - feature-extraction
----
+---
+
+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/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+You can then use the model to compute embeddings like this:
+
+```js
+import { pipeline } from '@huggingface/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, ... ]
+// ]
+```