Add transformers.js tag and example code (#3)

- Add transformers.js tag and example code (1d6119f75512283ae79aef1138ff26ff53caa4ff)
- Update README.md (fc727a35ef69e27686c8c1f60780dbe8523b9140)


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

README.md

@@ -1,6 +1,7 @@
 ---
 tags:
 - mteb
+- transformers.js
 model-index:
 - name: mxbai-embed-2d-large-v1
   results:
@@ -2693,6 +2694,35 @@ print('similarities:', similarities)
 
 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!
 
+### 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 as follows:
+
+```js
+import { pipeline, cos_sim } from '@xenova/transformers';
+
+// Create a feature-extraction pipeline
+const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-2d-large-v1', {
+    quantized: false, // (Optional) remove this line to use the 8-bit quantized model
+});
+
+// Compute sentence embeddings (with `cls` pooling)
+const sentences = ['Who is german and likes bread?', 'Everybody in Germany.' ];
+const output = await extractor(sentences, { pooling: 'cls' });
+
+// Set embedding size and truncate embeddings
+const new_embedding_size = 768;
+const truncated = output.slice(null, [0, new_embedding_size]);
+
+// Compute cosine similarity
+console.log(cos_sim(truncated[0].data, truncated[1].data)); // 0.6979532021425204
+```
+
 ## Evaluation
 
 Please find more information in our [blog post](https://mixedbread.ai/blog/mxbai-embed-2d-large-v1).