Update to Transformers.js v3

README.md

@@ -8,13 +8,18 @@ https://huggingface.co/jinaai/jina-embeddings-v2-small-en with ONNX weights to b
 
 ## Usage with 🤗 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
+```
+
+You can then use the model as follows:
 ```js
-// npm i @xenova/transformers
-import { pipeline, cos_sim } from '@xenova/transformers';
+import { pipeline, cos_sim } from '@huggingface/transformers';
 
 // Create feature extraction pipeline
 const extractor = await pipeline('feature-extraction', 'Xenova/jina-embeddings-v2-small-en',
-    { quantized: false } // Comment out this line to use the quantized version
+    { dtype: "fp32" } // Options: "fp32", "fp16", "q8", "q4"
 );
 
 // Generate embeddings
@@ -27,4 +32,6 @@ const output = await extractor(
 console.log(cos_sim(output[0].data, output[1].data));  // 0.9399812684139274 (unquantized) vs. 0.9341121503699659 (quantized)
 ```
 
+---
+
 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`).