metadata
base_model: OFA-Sys/chinese-clip-vit-base-patch16
library_name: transformers.js
https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16 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 @xenova/transformers
Example: Zero-shot image classification w/ Xenova/chinese-clip-vit-base-patch16.
import { pipeline } from '@xenova/transformers';
// Create zero-shot image classification pipeline
const classifier = await pipeline('zero-shot-image-classification', 'Xenova/chinese-clip-vit-base-patch16');
// Set image url and candidate labels
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/pikachu.png';
const candidate_labels = ['杰尼龟', '妙蛙种子', '小火龙', '皮卡丘'] // Squirtle, Bulbasaur, Charmander, Pikachu in Chinese
// Classify image
const output = await classifier(url, candidate_labels);
console.log(output);
// [
// { score: 0.9926728010177612, label: '皮卡丘' }, // Pikachu
// { score: 0.003480620216578245, label: '妙蛙种子' }, // Bulbasaur
// { score: 0.001942147733643651, label: '杰尼龟' }, // Squirtle
// { score: 0.0019044597866013646, label: '小火龙' } // Charmander
// ]
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 and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).