Update README.md

README.md

@@ -2690,38 +2690,10 @@ similarities = cos_sim(embeddings[0], embeddings[1:])
 print('similarities:', similarities)
 ```
 
-### 3. Using API
+### 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!
 
-### 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