Update Transformers.js code snippets to V3

README.md

@@ -2726,31 +2726,31 @@ print('similarities:', similarities)
 
 ### 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:
 
-```
-npm i @xenova/transformers
+```sh
+npm i @huggingface/transformers
 ```
 
 You can then use the model to compute embeddings like this:
 
 ```javascript
-import { pipeline, cos_sim } from '@xenova/transformers';
+import { pipeline, cos_sim } from "@huggingface/transformers";
 
 // Create a feature extraction pipeline
-const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-large-v1', {
-    quantized: false, // Comment out this line to use the quantized version
+const extractor = await pipeline("feature-extraction", "mixedbread-ai/mxbai-embed-large-v1", {
+    dtype: "fp32", // Options: "fp32", "fp16", "q8"
 });
 
 // Generate sentence embeddings
 const docs = [
-    'Represent this sentence for searching relevant passages: A man is eating a piece of bread',
-    'A man is eating food.',
-    'A man is eating pasta.',
-    'The girl is carrying a baby.',
-    'A man is riding a horse.',
+    "Represent this sentence for searching relevant passages: A man is eating a piece of bread",
+    "A man is eating food.",
+    "A man is eating pasta.",
+    "The girl is carrying a baby.",
+    "A man is riding a horse.",
 ]
-const output = await extractor(docs, { pooling: 'cls' });
+const output = await extractor(docs, { pooling: "cls" });
 
 // Compute similarity scores
 const [source_embeddings, ...document_embeddings ] = output.tolist();