README.md

---
base_model: hf-tiny-model-private/tiny-random-RoFormerForSequenceClassification
library_name: transformers.js
---

https://huggingface.co/hf-tiny-model-private/tiny-random-RoFormerForSequenceClassification 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:** Text Classification.

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

const classifier = await pipeline('text-classification', 'Xenova/tiny-random-RoFormerForSequenceClassification');
const output = await classifier('I love transformers!');
```

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`).