Transformers.js v3 (#1)

- Transformers.js v3 (c075c60233c2eb231a65b1477b4b545534e11f32)

README.md

@@ -8,7 +8,7 @@ https://huggingface.co/nielsr/slimsam-50-uniform with ONNX weights to be compati
 
 ## 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
 ```
@@ -16,7 +16,7 @@ npm i @xenova/transformers
 **Example:** Perform mask generation with `Xenova/slimsam-50-uniform`.
 
 ```js
-import { SamModel, AutoProcessor, RawImage } from '@xenova/transformers';
+import { SamModel, AutoProcessor, RawImage } from '@huggingface/transformers';
 
 // Load model and processor
 const model = await SamModel.from_pretrained('Xenova/slimsam-50-uniform');
@@ -25,10 +25,10 @@ const processor = await AutoProcessor.from_pretrained('Xenova/slimsam-50-uniform
 // Prepare image and input points
 const img_url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/corgi.jpg';
 const raw_image = await RawImage.read(img_url);
-const input_points = [[[340, 250]]] // 2D localization of a window
+const input_points = [[[340, 250]]];
 
 // Process inputs and perform mask generation
-const inputs = await processor(raw_image, input_points);
+const inputs = await processor(raw_image, { input_points });
 const outputs = await model(inputs);
 
 // Post-process masks