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--- |
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library_name: transformers.js |
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--- |
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https://huggingface.co/google/siglip-large-patch16-256 with ONNX weights to be compatible with Transformers.js. |
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## Usage (Transformers.js) |
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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: |
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```bash |
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npm i @xenova/transformers |
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``` |
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**Example:** Zero-shot image classification w/ `Xenova/siglip-large-patch16-256`: |
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```js |
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import { pipeline } from '@xenova/transformers'; |
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const classifier = await pipeline('zero-shot-image-classification', 'Xenova/siglip-large-patch16-256'); |
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const url = 'http://images.cocodataset.org/val2017/000000039769.jpg'; |
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const output = await classifier(url, ['2 cats', '2 dogs'], { |
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hypothesis_template: 'a photo of {}', |
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}); |
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console.log(output); |
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// [ |
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// { score: 0.3086719512939453, label: '2 cats' }, |
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// { score: 0.0000623430241830647, label: '2 dogs' } |
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// ] |
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``` |
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**Example:** Compute text embeddings with `SiglipTextModel`. |
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```javascript |
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import { AutoTokenizer, SiglipTextModel } from '@xenova/transformers'; |
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// Load tokenizer and text model |
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const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-large-patch16-256'); |
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const text_model = await SiglipTextModel.from_pretrained('Xenova/siglip-large-patch16-256'); |
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// Run tokenization |
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const texts = ['a photo of 2 cats', 'a photo of 2 dogs']; |
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const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true }); |
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// Compute embeddings |
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const { pooler_output } = await text_model(text_inputs); |
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// Tensor { |
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// dims: [ 2, 768 ], |
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// type: 'float32', |
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// data: Float32Array(1536) [ ... ], |
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// size: 1536 |
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// } |
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``` |
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**Example:** Compute vision embeddings with `SiglipVisionModel`. |
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```javascript |
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import { AutoProcessor, SiglipVisionModel, RawImage} from '@xenova/transformers'; |
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// Load processor and vision model |
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const processor = await AutoProcessor.from_pretrained('Xenova/siglip-large-patch16-256'); |
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const vision_model = await SiglipVisionModel.from_pretrained('Xenova/siglip-large-patch16-256'); |
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// Read image and run processor |
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const image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg'); |
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const image_inputs = await processor(image); |
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// Compute embeddings |
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const { pooler_output } = await vision_model(image_inputs); |
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// Tensor { |
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// dims: [ 1, 768 ], |
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// type: 'float32', |
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// data: Float32Array(768) [ ... ], |
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// size: 768 |
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// } |
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``` |
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--- |
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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`). |
