Update README.md

README.md

@@ -5,4 +5,36 @@ base_model: jmtzt/ijepa_vith16_1k
 
 https://huggingface.co/jmtzt/ijepa_vith16_1k with ONNX weights to be compatible with Transformers.js.
 
+## 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
+```
+
+**Example:** Image feature extraction with `onnx-community/ijepa_vith16_1k`.
+
+```js
+import { pipeline, cos_sim } from "@huggingface/transformers";
+
+// Create an image feature extraction pipeline
+const extractor = await pipeline(
+  "image-feature-extraction",
+  "onnx-community/ijepa_vith16_1k",
+  { dtype: "q8" },
+);
+
+// Compute image embeddings
+const url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
+const url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg"
+const output = await extractor([url_1, url_2]);
+const pooled_output = output.mean(1); // Apply mean pooling
+
+// Compute cosine similarity
+const similarity = cos_sim(pooled_output[0].data, pooled_output[1].data);
+console.log(similarity); // 0.5334921616321957
+```
+
+---
+
 Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).