Instructions to use saikumaru/svm-onnx-browser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use saikumaru/svm-onnx-browser with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("saikumaru/svm-onnx-browser", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Transformers.js
How to use saikumaru/svm-onnx-browser with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'saikumaru/svm-onnx-browser'); - Notebooks
- Google Colab
- Kaggle
SVM Text Classifier (ONNX)
This is an SVM model trained on Scikit-learn and exported to ONNX.
It requires embeddings from all-MiniLM-L6-v2 (384 dimensions).
Usage in Javascript
const session = await ort.InferenceSession.create('https://huggingface.co/saikumaru/svm-onnx-browser/resolve/main/model.onnx');
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Model tree for saikumaru/svm-onnx-browser
Base model
sentence-transformers/all-MiniLM-L6-v2