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--- |
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base_model: openai/clip-vit-base-patch32 |
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library_name: transformers.js |
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--- |
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https://huggingface.co/openai/clip-vit-base-patch32 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/@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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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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// [ |
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// { score: 0.9993917942047119, label: 'tiger' }, |
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// { score: 0.0003519294841680676, label: 'horse' }, |
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// { score: 0.0002562698791734874, label: 'dog' } |
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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`). |
