Update to Transformers.js v3.4

README.md

@@ -7,15 +7,15 @@ https://huggingface.co/BAAI/bge-m3 with ONNX weights to be compatible with Trans
 
 ## 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
 ```
 
 You can then use the model to compute embeddings, as follows:
 
 ```js
-import { pipeline } from '@xenova/transformers';
+import { pipeline } from '@huggingface/transformers';
 
 // Create a feature-extraction pipeline
 const extractor = await pipeline('feature-extraction', 'Xenova/bge-m3');
@@ -40,7 +40,7 @@ console.log(embeddings.tolist()); // Convert embeddings to a JavaScript list
 
 You can also use the model for retrieval. For example:
 ```js
-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', 'Xenova/bge-m3');

config.json

@@ -21,6 +21,11 @@
   "pad_token_id": 1,
   "position_embedding_type": "absolute",
   "transformers_version": "4.37.2",
+  "transformers.js_config": {
+    "use_external_data_format": {
+      "model.onnx": true,
+    }
+  },
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 250002