README.md

---
base_model: naver-clova-ix/donut-base-finetuned-docvqa
library_name: transformers.js
pipeline_tag: document-question-answering
tags:
- donut
- image-to-text
- vision
- donut-swin
---

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`).