Update README.md

README.md

@@ -11,4 +11,26 @@ tags:
 
 https://huggingface.co/naver-clova-ix/donut-base-finetuned-docvqa 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
+```
+
+**Example:** Answer questions about a document with `Xenova/donut-base-finetuned-docvqa`.
+```js
+import { pipeline } from '@huggingface/transformers';
+
+// Create a document question answering pipeline
+const qa_pipeline = await pipeline('document-question-answering', 'Xenova/donut-base-finetuned-docvqa');
+
+// Generate an answer for a given image and question
+const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/invoice.png';
+const question = 'What is the invoice number?';
+const output = await qa_pipeline(image, question);
+// [{ answer: 'us-001' }]
+```
+
 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`).