Upload ONNX weights + add transformers.js code/tags (#2)

- Upload ONNX weights (7a055cbd36c0b250e778017460b956afe6a15c83)
- Add transformers.js sample code + tags (affc1c96c21894a0ebf5ad0d68aa0544d0e02943)
- Update README.md (38287a9818172d5ebac83a92cd611b2252beea6b)


Co-authored-by: Joshua <[email protected]>

README.md

@@ -1,6 +1,7 @@
 ---
 tags:
 - mteb
+- transformers.js
 model-index:
 - name: mxbai-angle-large-v1
   results:
@@ -2703,6 +2704,39 @@ similarities = cos_sim(embeddings[0], embeddings[1:])
 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:
+```bash
+npm i @xenova/transformers
+```
+
+You can then use the model to compute embeddings like this:
+
+```js
+import { pipeline, cos_sim } from '@xenova/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
+});
+
+// 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.',
+]
+const output = await extractor(docs, { pooling: 'cls' });
+
+// Compute similarity scores
+const [source_embeddings, ...document_embeddings ] = output.tolist();
+const similarities = document_embeddings.map(x => cos_sim(source_embeddings, x));
+console.log(similarities); // [0.7919578577247139, 0.6369278664248345, 0.16512018371357193, 0.3620778366720027]
+```
+
 ### Using API
 
 You’ll be able to use the models through our API as well. The API is coming soon and will have some exciting features. Stay tuned!

onnx/model.onnx

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+size 1336854282

onnx/model_quantized.onnx

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