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ParminderzHuggingFace/intelligent-candidate-discovery-models
intelligent-candidate-discovery-models is a feature extraction model from ParminderzHuggingFace. Use it when you need embeddings to search or compare text. The card lists the license as mit.
Pre-computed retrieval artifacts for the Redrob AI — India Runs Data & AI Challenge (Track 1) candidate discovery and ranking system.
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Updated Aug 27, 2026
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From the Hugging Face model README
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.
| File | Purpose |
|---|---|
| aiss.index | 768-dimensional FAISS IndexFlatIP semantic search index |
| candidate_lookup.pkl | Candidate ID → candidate profile metadata lookup |
| embedding_metadata.pkl | Metadata associated with the generated candidate embeddings |
The embeddings were generated using BAAI/bge-base-en-v1.5.
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
The deployment separates source code, candidate data, and pre-computed retrieval artifacts:
The artifacts in this repository are pre-computed offline and are not regenerated during application startup.