Instructions to use Gehan77/cv-match-browser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Gehan77/cv-match-browser with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'Gehan77/cv-match-browser');
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Download README.md from Gehan77/cv-match-browser: direct link, hf CLI and curl.
- Browser
- Download file 549 Bytes
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https://huggingface.co/Gehan77/cv-match-browser/resolve/main/README.md
- Command line
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hf download hf://Gehan77/cv-match-browser/README.md
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curl -L -o README.md https://huggingface.co/Gehan77/cv-match-browser/resolve/main/README.md
549 Bytes
metadata
library_name: transformers.js
pipeline_tag: text-classification
CV match model for browser inference
This is the ONNX export of the Sri Lankan job-resume Cross-Encoder. It is intended to run locally in a browser using Transformers.js, so the free static Hugging Face Space does not need a paid CPU runtime.
The model accepts a job description as the first text and a candidate profile as the second text. Apply a sigmoid to the single output logit and compare the resulting probability with the decision threshold 0.4399277865886688.