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---
base_model: vikp/texify
library_name: transformers.js
pipeline_tag: image-to-text
---
https://huggingface.co/vikp/texify 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:** Image-to-text w/ `Xenova/texify`.
```js
import { pipeline } from '@xenova/transformers';
// Create an image-to-text pipeline
const texify = await pipeline('image-to-text', 'Xenova/texify');
// Generate LaTeX from image
const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/latex2.png';
const latex = await texify(image, { max_new_tokens: 384 });
console.log(latex);
// [{ generated_text: "$$ |\\ \\frac{1}{x}=\\frac{1}{c}|=|\\ \\frac{c-x}{xc}|=\\frac{1}{|x|}\\cdot\\frac{1}{|c|}\\cdot|x-c|$$\n\nThe factor $$ \\frac{1}{|x|}$$ is not good if its near 0." }]
```
| Input image | Visualized output |
|--------|--------|
|  |  |
---
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`). |