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E5 Large V2 Onnx
E
E5 Large V2 Onnx
Developed by nixiesearch
This is a sentence transformer model that maps sentences and paragraphs into a dense vector space, suitable for tasks such as clustering and semantic search.
Downloads 114
Release Time : 11/7/2023
Model Overview
This model is the ONNX-converted version of intfloat/e5-large-v2, specifically designed for converting text into high-dimensional vector representations, supporting various natural language processing tasks.
Model Features
ONNX Optimization
Provides two optimized versions: Float32 version and QInt8 quantized version, with optimization level 2, suitable for different deployment needs.
Multi-dataset Training
The model was trained on multiple high-quality datasets including s2orc, stackexchange_xml, and ms_marco.
Efficient Semantic Representation
Capable of efficiently mapping sentences and paragraphs into a dense vector space while preserving semantic information.
Model Capabilities
Text Embedding
Semantic Similarity Calculation
Information Retrieval
Text Clustering
Use Cases
Information Retrieval
Document Search
Convert queries and documents into vectors to achieve semantic-based document retrieval.
Compared to keyword search, it better understands query intent.
Text Analysis
Similar Question Identification
Identify questions with different expressions but similar semantics.
Can be used in Q&A systems to reduce duplicate questions.
Transformers
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