minicoil-training

What problem does it solve?

This Skill provides a comprehensive reference for training miniCOIL sparse neural retrieval models, enabling context-aware semantic search within inverted indexes.

Core Features & Use Cases

• Self-supervised per-word training: trains a tiny per-word linear layer (4D output) using triplet loss to create distinct sense representations.
• End-to-end training pipeline: from vocabulary construction to per-word training, sparse encoding, and BEIR evaluation, all aligned for deployment with Qdrant.
• Deployment-ready artifacts: outputs weight and bias for every word (word_layers.pt), enabling direct integration into sparse-vector search backends.

Quick Start

Run a demo by loading the per-word linear layers and encoding sample text into a 4D sparse vector.