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Minicoil V1

Developed by Qdrant
MiniCOIL is a sparse contextualized word-by-word embedding model specifically designed for efficient semantic similarity computation
Downloads 564
Release Time : 5/10/2025

Model Overview

This model represents text through sparse embeddings, supporting word-by-word contextual understanding, suitable for semantic search and similarity computation tasks

Model Features

Sparse embedding representation
Uses sparse vector representation for text, significantly reducing storage and computational requirements
Word-by-word contextual understanding
Capable of capturing semantic variations of words in different contexts
Efficient similarity computation
Rapidly computes semantic similarity through dot product operations
Stop word handling
Automatically filters stop words, focusing on meaningful semantic units

Model Capabilities

Semantic similarity computation
Context-dependent word vector generation
Sparse text representation
Efficient vector retrieval

Use Cases

Information retrieval
Semantic search
Enhances search engine's ability to understand query intent
Can distinguish meanings of polysemous words in different contexts
Natural language processing
Document similarity analysis
Computes semantic similarity between documents
Accurately identifies semantically related document pairs
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