Add/update the quantized ONNX model files and README.md for Transformers.js v3

README.md

@@ -7,15 +7,15 @@ https://huggingface.co/nvidia/mit-b5 with ONNX weights to be compatible with Tra
 
 ## 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:
+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 @xenova/transformers
+npm i @huggingface/transformers
 ```
 
 **Example:** Perform image classification with `Xenova/mit-b5`.
 
 ```js
-import { pipeline } from '@xenova/transformers';
+import { pipeline } from '@huggingface/transformers';
 
 // Create image classification pipeline
 const classifier = await pipeline('image-classification', 'Xenova/mit-b5');
@@ -23,11 +23,10 @@ const classifier = await pipeline('image-classification', 'Xenova/mit-b5');
 // Classify an image
 const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
 const output = await classifier(url);
-console.log(output)
+console.log(output);
 // [{ label: 'tiger, Panthera tigris', score: 0.5656083822250366 }]
 ```
 
 ---
 
-
 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`).