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Nomic Embed Text V2 Moe Unsupervised
This is an intermediate version of a multilingual Mixture of Experts (MoE) text embedding model, obtained through multi-stage contrastive training
Downloads 161
Release Time : 2/11/2025
Model Overview
This model is a multilingual text embedding model that adopts a Mixture of Experts (MoE) architecture, primarily used for text feature extraction and sentence similarity computation.
Model Features
Mixture of Experts Architecture
Adopts MoE architecture, enabling efficient processing of multilingual text embedding tasks
Multi-stage Contrastive Training
Optimized through multi-stage contrastive training, enhancing model performance
Multilingual Support
Supports text embedding processing in multiple languages
Model Capabilities
Text Feature Extraction
Sentence Similarity Computation
Multilingual Text Processing
Use Cases
Information Retrieval
Semantic Search
Used for building semantic search engines to improve search result relevance
Text Analysis
Document Clustering
Automatic document classification and clustering based on text similarity
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