| base_model: openai/clip-vit-base-patch32 | |
| library_name: transformers.js | |
| https://huggingface.co/openai/clip-vit-base-patch32 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:** Perform zero-shot image classification with the `pipeline` API. | |
| ```js | |
| const classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32'); | |
| const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg'; | |
| const output = await classifier(url, ['tiger', 'horse', 'dog']); | |
| // [ | |
| // { score: 0.9993917942047119, label: 'tiger' }, | |
| // { score: 0.0003519294841680676, label: 'horse' }, | |
| // { score: 0.0002562698791734874, label: 'dog' } | |
| // ] | |
| ``` | |
| --- | |
| 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`). |