README.md

---
base_model: microsoft/dit-base-finetuned-rvlcdip
library_name: transformers.js
tags:
- dit
---

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](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
```bash
npm i @xenova/transformers
```

**Example:** Perform document image classification with `Xenova/dit-base-finetuned-rvlcdip`
```js
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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).