Update README.md

README.md

@@ -4,4 +4,36 @@ library_name: "transformers.js"
 
 https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english 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 classify text like this:
+
+```js
+import { pipeline } from "@xenova/transformers";
+
+// Create a sentiment analysis pipeline
+const classifier = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english');
+
+// Classify input text
+const output = await classifier('I love transformers!');
+console.log(output);
+// [{ label: 'POSITIVE', score: 0.999788761138916 }]
+
+// Classify input text (and return all classes)
+const output2 = await classifier('I love transformers!', { topk: null });
+console.log(output2);
+// [
+//   { label: 'POSITIVE', score: 0.999788761138916 },
+//   { label: 'NEGATIVE', score: 0.00021126774663571268 }
+// ]
+```
+
+---
+
 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`).