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Bm25
A sparse text embedding model based on the BM25 algorithm, used to evaluate the relevance between documents and search queries
Downloads 192.38k
Release Time : 6/13/2024
Model Overview
This model implements BM25 (Best Match 25 algorithm), a ranking function used in search engines to evaluate the relevance of documents to a given search query. Primarily used for information retrieval and document relevance scoring tasks.
Model Features
Efficient Relevance Scoring
Based on the BM25 algorithm, it can efficiently evaluate the relevance between documents and queries
Sparse Embedding Representation
Generates sparse vector representations, suitable for large-scale document retrieval
Integration with Qdrant
Optimized for use with the Qdrant vector database
Model Capabilities
Document relevance scoring
Information retrieval
Search query matching
Use Cases
Information Retrieval
Search Engine Result Ranking
Used in search engines to score and rank the relevance of documents to queries
Improves the relevance and accuracy of search results
Document Retrieval System
Implements efficient document retrieval functionality in document management systems
Quickly finds the most relevant documents to a query
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