| | --- |
| | tags: |
| | - text-classification |
| | - recruitment |
| | - forensics |
| | - security |
| | license: mit |
| | datasets: |
| | - dcata004/recruiter-harvesting-dataset-v1 |
| | pipeline_tag: text-classification |
| | --- |
| | |
| | # 🐍 V.I.P.E.R. Classification Engine (v1.0) |
| | **Maintainer:** [Cata Risk Lab](https://huggingface.co/Cata-Risk-Lab) |
| |
|
| | ## 🧠 Model Overview |
| | This repository contains the configuration and architecture definitions for the **V.I.P.E.R.** recruitment auditing system. It defines the risk thresholds and vectorization parameters used to detect "Resume Harvesting" attacks. |
| |
|
| | ## 🛠️ Configuration |
| | The model operates on a `TfidfVectorizer` pipeline optimized for short-text classification of email subjects and bodies. |
| |
|
| | - **Risk Threshold:** 0.75 (Confidence score required to flag as SPAM) |
| | - **Labels:** `['harvesting', 'legitimate']` |
| | - **Dataset:** Trained on forensic recruitment data (Swiss/US/UK). |
| |
|
| | ## ⚖️ Sovereign AI |
| | Designed for local inference to protect user data privacy. |
