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Ce Msmarco MiniLM L6 V2
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Ce Msmarco MiniLM L6 V2

Developed by metarank
This is a sentence transformer model based on the MiniLM-L6 architecture, capable of mapping text to a 384-dimensional vector space, suitable for semantic search and text similarity calculation tasks.
Text Embedding EnglishOpen Source License:Apache-2.0#Semantic similarity calculation#384-dimensional vector embedding#Sentence reordering
Downloads 20
Release Time : 4/11/2023

Model Overview

This model is an ONNX version specifically designed for the Metarank reranker, enabling efficient semantic similarity calculations. It can convert sentences and paragraphs into dense vector representations, supporting applications such as clustering and semantic search.

Model Features

Efficient Vector Conversion
Efficiently maps sentences and paragraphs to a 384-dimensional dense vector space.
ONNX Optimization
ONNX format optimized for Metarank, improving inference efficiency.
Multi-dataset Training
Trained on multiple high-quality datasets including s2orc and ms_marco.

Model Capabilities

Text vectorization
Semantic similarity calculation
Text clustering
Semantic search

Use Cases

Information Retrieval
Search Engine Result Reordering
Used to improve the relevance ranking of search engine results.
Enhances the relevance and accuracy of search results.
Question Answering Systems
Question-Answer Matching
Calculates the semantic similarity between user questions and candidate answers.
Improves the accuracy of question-answering systems.
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