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Contriever Base Msmarco

Developed by nthakur
This is a version of the Contriever MSMARCO model adapted for the Sentence Transformer framework, capable of mapping text to a 768-dimensional dense vector space, suitable for semantic search and clustering tasks.
Downloads 2,243
Release Time : 6/9/2022

Model Overview

Based on the Contriever architecture, this model is specifically optimized for the MSMARCO dataset, enabling the conversion of sentences and paragraphs into high-quality embedding vectors for information retrieval and semantic similarity calculations.

Model Features

High-Quality Embedding Vectors
Generates 768-dimensional dense vector representations that capture semantic information of the text.
MSMARCO Optimization
Specifically optimized for the MSMARCO information retrieval dataset.
Sentence Transformer Compatibility
Adapted to the Sentence Transformer framework for ease of use and integration.

Model Capabilities

Text Embedding Generation
Semantic Similarity Calculation
Information Retrieval
Text Clustering

Use Cases

Information Retrieval
Document Search
Performs semantic document search using embedding vectors.
Delivers more relevant results compared to traditional keyword searches.
Text Analysis
Similar Question Identification
Identifies semantically similar questions or queries.
Useful for FAQ systems or Q&A platforms.
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