README.md

---
base_model: OFA-Sys/chinese-clip-vit-large-patch14-336px
library_name: transformers.js
---

https://huggingface.co/OFA-Sys/chinese-clip-vit-large-patch14-336px 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:** Zero shot image classification.

```js
import { pipeline } from '@huggingface/transformers';

const classifier = await pipeline('zero-shot-image-classification', 'Xenova/chinese-clip-vit-large-patch14-336px');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url, ['tiger', 'horse', 'dog']);
```

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`).