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 JavaScript library from NPM using:
npm i @huggingface/transformers
Example: Perform zero-shot image classification with the pipeline
API.
import { pipeline } from '@huggingface/transformers';
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 π€ Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx
).
- Downloads last month
- 213,424
Inference API (serverless) does not yet support transformers.js models for this pipeline type.
Model tree for Xenova/clip-vit-base-patch32
Base model
openai/clip-vit-base-patch32