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@@ -7,13 +7,15 @@ https://huggingface.co/openai/clip-vit-base-patch32 with ONNX weights to be comp
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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:** Perform zero-shot image classification with the `pipeline` API.
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  ```js
 
 
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  const classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32');
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  const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
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  const output = await classifier(url, ['tiger', 'horse', 'dog']);
 
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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/@huggingface/transformers) using:
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  ```bash
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+ npm i @huggingface/transformers
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  ```
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  **Example:** Perform zero-shot image classification with the `pipeline` API.
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  ```js
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+ import { pipeline } from '@huggingface/transformers';
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+
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  const classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32');
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  const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
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  const output = await classifier(url, ['tiger', 'horse', 'dog']);