Update README.md

README.md

@@ -4,4 +4,75 @@ library_name: transformers.js
 
 https://huggingface.co/google/siglip-base-patch16-384 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/@xenova/transformers) using:
+```bash
+npm i @xenova/transformers
+```
+
+**Example:** Zero-shot image classification w/ `Xenova/siglip-base-patch16-384`:
+```js
+import { pipeline } from '@xenova/transformers';
+
+const classifier = await pipeline('zero-shot-image-classification', 'Xenova/siglip-base-patch16-384');
+const url = 'http://images.cocodataset.org/val2017/000000039769.jpg';
+const output = await classifier(url, ['2 cats', '2 dogs'], {
+    hypothesis_template: 'a photo of {}',
+});
+console.log(output);
+// [
+//   { score: 0.24518242478370667, label: '2 cats' },
+//   { score: 0.00004750826701638289, label: '2 dogs' }
+// ]
+```
+
+**Example:** Compute text embeddings with `SiglipTextModel`.
+
+```javascript
+import { AutoTokenizer, SiglipTextModel } from '@xenova/transformers';
+
+// Load tokenizer and text model
+const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-base-patch16-384');
+const text_model = await SiglipTextModel.from_pretrained('Xenova/siglip-base-patch16-384');
+
+// Run tokenization
+const texts = ['a photo of 2 cats', 'a photo of 2 dogs'];
+const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true });
+
+// Compute embeddings
+const { pooler_output } = await text_model(text_inputs);
+// Tensor {
+//   dims: [ 2, 768 ],
+//   type: 'float32',
+//   data: Float32Array(1536) [ ... ],
+//   size: 1536
+// }
+```
+
+**Example:** Compute vision embeddings with `SiglipVisionModel`.
+
+```javascript
+import { AutoProcessor, SiglipVisionModel, RawImage} from '@xenova/transformers';
+
+// Load processor and vision model
+const processor = await AutoProcessor.from_pretrained('Xenova/siglip-base-patch16-384');
+const vision_model = await SiglipVisionModel.from_pretrained('Xenova/siglip-base-patch16-384');
+
+// Read image and run processor
+const image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg');
+const image_inputs = await processor(image);
+
+// Compute embeddings
+const { pooler_output } = await vision_model(image_inputs);
+// Tensor {
+//   dims: [ 1, 768 ],
+//   type: 'float32',
+//   data: Float32Array(768) [ ... ],
+//   size: 768
+// }
+```
+
+---
+
 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`).