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Hypencoder.8 Layer

Developed by jfkback
Hypencoder is a dual-encoder model for information retrieval, consisting of a text encoder and a hypernetwork (Hypencoder), capable of converting text into small neural networks for computing relevance scores.
Downloads 18
Release Time : 2/12/2025

Model Overview

This model implements information retrieval through a dual-encoder architecture, where the text encoder converts items into 768-dimensional vectors, and the Hypencoder converts text into small neural networks to output relevance scores.

Model Features

Hypernetwork architecture
Uses Hypencoder to convert text into small neural networks, dynamically generating relevance scoring functions
Configurable hidden layers
Offers variants with 2/4/6/8 hidden layers, allowing selection of model complexity based on needs
Dual-encoder design
Combines traditional text encoders with innovative Hypencoder for efficient information retrieval

Model Capabilities

Text feature extraction
Relevance scoring
Information retrieval

Use Cases

Search engines
Query-document relevance assessment
Computes relevance scores between user queries and candidate documents
Effectively ranks retrieval results
Question answering systems
Answer candidate ranking
Ranks candidate answers in QA systems by relevance
Improves answer accuracy
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