| | --- |
| | base_model: vikp/texify2 |
| | library_name: transformers.js |
| | pipeline_tag: image-to-text |
| | --- |
| | |
| | https://huggingface.co/vikp/texify2 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:** Image-to-text w/ `Xenova/texify2`. |
| |
|
| | ```js |
| | import { pipeline } from '@huggingface/transformers'; |
| | |
| | // Create an image-to-text pipeline |
| | const texify = await pipeline('image-to-text', 'Xenova/texify2'); |
| | |
| | // Generate LaTeX from image |
| | const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/latex.png'; |
| | const latex = await texify(image, { max_new_tokens: 384 }); |
| | console.log(latex); |
| | // [{ generated_text: "The potential $V_i$ of cell $\\mathcal{C}_i$ centred at position $\\mathbf{r}_i$ is related to the surface charge densities $\\sigma_j$ of cells $\\mathcal{C}_j$ $j\\in[1,N]$ through the superposition principle as: $$V_i\\,=\\,\\sum_{j=0}^{N}\\,\\frac{\\sigma_j}{4\\pi\\varepsilon_0}\\,\\int_{\\mathcal{C}_j}\\frac{1}{\\|\\mathbf{r}_i-\\mathbf{r}'\\|}\\mathrm{d}^2\\mathbf{r}'\\,=\\,\\sum_{j=0}^{N}\\,Q_{ij}\\,\\sigma_j,$$ where the integral over the surface of cell $\\mathcal{C}_j$ only depends on $\\mathcal{C}_j$ shape and on the relative position of the target point $\\mathbf{r}_i$ with respect to $\\mathcal{C}_j$ location, as $\\sigma_j$ is assumed constant over the whole surface of cell $\\mathcal{C}_j$." }] |
| | ``` |
| |
|
| | | 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`). |
