metadata
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 JavaScript library from NPM using:
npm i @huggingface/transformers
Example: Answer questions about a document with Xenova/donut-base-finetuned-docvqa.
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 and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).