https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip 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 @xenova/transformers
Example: Perform document image classification with Xenova/dit-base-finetuned-rvlcdip
import { pipeline } from '@xenova/transformers';
// Create an image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/dit-base-finetuned-rvlcdip');
// Classify an image
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/coca_cola_advertisement.png';
const output = await classifier(url);
// [{ label: 'advertisement', score: 0.9035086035728455 }]
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).
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