Update README.md

README.md

@@ -19,6 +19,46 @@ NeoBERT is a **next-generation encoder** model for English text representation,
 
 ## 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/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+You can then compute embeddings using the pipeline API:
+
+```js
+import { pipeline } from "@huggingface/transformers";
+
+// Create feature extraction pipeline
+const extractor = await pipeline("feature-extraction", "onnx-community/NeoBERT-ONNX");
+
+// Compute embeddings
+const text = "NeoBERT is the most efficient model of its kind!";
+const embedding = await extractor(text, { pooling: "cls" });
+console.log(embedding.dims); // [1, 768]
+```
+
+Or manually with the model and tokenizer classes:
+```js
+import { AutoModel, AutoTokenizer } from "@huggingface/transformers";
+
+// Load model and tokenizer
+const model_id = "onnx-community/NeoBERT-ONNX";
+const tokenizer = await AutoTokenizer.from_pretrained(model_id);
+const model = await AutoModel.from_pretrained(model_id);
+
+// Tokenize input text
+const text = "NeoBERT is the most efficient model of its kind!";
+const inputs = tokenizer(text);
+
+// Generate embeddings
+const outputs = await model(inputs);
+const embedding = outputs.last_hidden_state.slice(null, 0);
+console.log(embedding.dims); // [1, 768]
+```
+
 ### ONNXRuntime
 
 ```py