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ParminderzHuggingFace/intelligent-candidate-discovery-models

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Intelligent Candidate Discovery — Model Artifacts

Pre-computed retrieval artifacts for the Redrob AI — India Runs Data & AI Challenge (Track 1) candidate discovery and ranking system.

This repository contains the FAISS search index and associated metadata required by the deployed application for semantic candidate retrieval.

Artifacts

FilePurpose
aiss.index768-dimensional FAISS IndexFlatIP semantic search index
candidate_lookup.pklCandidate ID → candidate profile metadata lookup
embedding_metadata.pklMetadata associated with the generated candidate embeddings

The embeddings were generated using BAAI/bge-base-en-v1.5.

Usage

The production application downloads these artifacts automatically when they are not available locally.

Live application:

https://huggingface.co/spaces/ParminderzHuggingFace/redrob-ai-candidate-ranking

The candidate profile database (candidates.jsonl) is maintained separately in the Hugging Face Dataset repository:

https://huggingface.co/datasets/ParminderzHuggingFace/india-runs-candidates

Architecture

The deployment separates source code, candidate data, and pre-computed retrieval artifacts:

  • —GitHub (ParminderSinghGithub/India-Runs) → application source code
  • —Hugging Face Dataset (ParminderzHuggingFace/india-runs-candidates) → candidates.jsonl
  • —Hugging Face Model (ParminderzHuggingFace/intelligent-candidate-discovery-models) → FAISS index and embedding metadata
  • —Hugging Face Space (ParminderzHuggingFace/redrob-ai-candidate-ranking) → live Streamlit application

The artifacts in this repository are pre-computed offline and are not regenerated during application startup.