Update to Transformers.js v3.4

README.md

@@ -6,14 +6,14 @@ library_name: transformers.js
 https://huggingface.co/google/siglip-large-patch16-384 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:
+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:** Zero-shot image classification w/ `Xenova/siglip-large-patch16-384`:
 ```js
-import { pipeline } from '@xenova/transformers';
+import { pipeline } from '@huggingface/transformers';
 
 const classifier = await pipeline('zero-shot-image-classification', 'Xenova/siglip-large-patch16-384');
 const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
@@ -30,7 +30,7 @@ console.log(output);
 **Example:** Compute text embeddings with `SiglipTextModel`.
 
 ```javascript
-import { AutoTokenizer, SiglipTextModel } from '@xenova/transformers';
+import { AutoTokenizer, SiglipTextModel } from '@huggingface/transformers';
 
 // Load tokenizer and text model
 const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-large-patch16-384');
@@ -53,7 +53,7 @@ const { pooler_output } = await text_model(text_inputs);
 **Example:** Compute vision embeddings with `SiglipVisionModel`.
 
 ```javascript
-import { AutoProcessor, SiglipVisionModel, RawImage} from '@xenova/transformers';
+import { AutoProcessor, SiglipVisionModel, RawImage } from '@huggingface/transformers';
 
 // Load processor and vision model
 const processor = await AutoProcessor.from_pretrained('Xenova/siglip-large-patch16-384');