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E5 Small V2 Onnx
E
E5 Small V2 Onnx
Developed by nixiesearch
This is a sentence transformer model that maps text to a dense vector space, suitable for semantic search and clustering tasks.
Downloads 221
Release Time : 8/7/2023
Model Overview
This model is the ONNX-converted version of intfloat/e5-small-v2, specifically designed to convert sentences and paragraphs into high-dimensional vector representations, supporting applications such as semantic similarity calculation and information retrieval.
Model Features
ONNX Format Support
Provides both Float32 and QInt8 quantized ONNX formats for easy deployment on different platforms.
Efficient Vectorization
Efficiently converts text into dense vector representations, supporting fast similarity calculations.
Multi-dataset Training
Trained on multiple high-quality datasets including s2orc, stackexchange, and ms_marco.
Model Capabilities
Text Vectorization
Semantic Similarity Calculation
Information Retrieval
Text Clustering
Use Cases
Information Retrieval
Document Search
Converts queries and documents into vectors to achieve semantic-based document retrieval.
Improves the relevance of search results.
Text Analysis
Similar Question Identification
Identifies differently phrased but semantically similar questions.
Can be used for question deduplication in Q&A systems.
Transformers
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