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@@ -4,4 +4,75 @@ library_name: transformers.js
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  https://huggingface.co/google/siglip-base-patch16-384 with ONNX weights to be compatible with Transformers.js.
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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`).
 
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  https://huggingface.co/google/siglip-base-patch16-384 with ONNX weights to be compatible with Transformers.js.
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+ ## Usage (Transformers.js)
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+
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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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+
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+ **Example:** Zero-shot image classification w/ `Xenova/siglip-base-patch16-384`:
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+ ```js
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+ import { pipeline } from '@xenova/transformers';
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+
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+ const classifier = await pipeline('zero-shot-image-classification', 'Xenova/siglip-base-patch16-384');
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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: 0.24518242478370667, label: '2 cats' },
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+ // { score: 0.00004750826701638289, label: '2 dogs' }
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+ // ]
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+ ```
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+
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+ **Example:** Compute text embeddings with `SiglipTextModel`.
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+
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+ ```javascript
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+ import { AutoTokenizer, SiglipTextModel } from '@xenova/transformers';
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+
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+ // Load tokenizer and text model
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+ const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-base-patch16-384');
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+ const text_model = await SiglipTextModel.from_pretrained('Xenova/siglip-base-patch16-384');
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+
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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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+
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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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+
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+ **Example:** Compute vision embeddings with `SiglipVisionModel`.
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+
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+ ```javascript
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+ import { AutoProcessor, SiglipVisionModel, RawImage} from '@xenova/transformers';
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+
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+ // Load processor and vision model
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+ const processor = await AutoProcessor.from_pretrained('Xenova/siglip-base-patch16-384');
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+ const vision_model = await SiglipVisionModel.from_pretrained('Xenova/siglip-base-patch16-384');
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+
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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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+
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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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+ ---
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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`).