| 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`). |