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E5 Base Unsupervised

Developed by intfloat
The E5 Base Unsupervised Model is a text embedding model based on contrastive pre-training, suitable for sentence similarity and transformation tasks.
Downloads 940
Release Time : 1/31/2023

Model Overview

This model acquires text embeddings through weakly supervised contrastive pre-training, primarily used for sentence similarity calculation and sentence transformation tasks.

Model Features

Weakly Supervised Contrastive Pre-training
Uses weakly supervised contrastive learning for pre-training to improve the quality of text embeddings.
Prefix-Sensitive Input Type Differentiation
Differentiates input types via 'query:' and 'passage:' prefixes to optimize retrieval task performance.
Normalized Embedding Vectors
Outputs normalized embedding vectors for easier similarity calculation.

Model Capabilities

Text embedding generation
Sentence similarity calculation
Information retrieval

Use Cases

Information Retrieval
Passage Retrieval for Q&A Systems
Used to retrieve the most relevant passages for user queries
Effectively matches queries with relevant passages
Semantic Analysis
Sentence Similarity Calculation
Calculates the semantic similarity between two sentences
Provides normalized similarity scores
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