Update README.md

README.md

@@ -4,4 +4,38 @@ library_name: transformers.js
 
 https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-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
+```
+
+**Example:** Information Retrieval w/ `Xenova/ms-marco-MiniLM-L-4-v2`.
+```js
+import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers';
+
+const model = await AutoModelForSequenceClassification.from_pretrained('Xenova/ms-marco-MiniLM-L-4-v2');
+const tokenizer = await AutoTokenizer.from_pretrained('Xenova/ms-marco-MiniLM-L-4-v2');
+
+const features = tokenizer(
+    ['How many people live in Berlin?', 'How many people live in Berlin?'],
+    {
+        text_pair: [
+            'Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.',
+            'New York City is famous for the Metropolitan Museum of Art.',
+        ],
+        padding: true,
+        truncation: true,
+    }
+)
+
+const scores = await model(features)
+console.log(scores);
+// quantized:   [ 9.241240501403809, -11.621903419494629 ]
+// unquantized: [ 9.238697052001953, -11.619404792785645 ]
+```
+
+---
+
 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`).