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Medical Embedded V2

Developed by shtilev
This is a multilingual sentence embedding model capable of mapping sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks such as clustering and semantic search.
Downloads 516
Release Time : 3/31/2025

Model Overview

Based on the DistilBERT architecture, this model is optimized for multilingual text processing, supporting the conversion of sentences in different languages into a unified vector representation for cross-language semantic comparison.

Model Features

Multilingual Support
Supports sentence embeddings in 14 languages, capable of handling cross-language semantic tasks.
Efficient Architecture
Lightweight architecture based on DistilBERT, reducing computational resource requirements while maintaining performance.
Unified Vector Space
Maps sentences in different languages to the same 512-dimensional vector space, facilitating cross-language comparison.

Model Capabilities

Sentence embedding
Semantic similarity calculation
Cross-language text matching
Text clustering
Feature extraction

Use Cases

Information Retrieval
Cross-language Document Retrieval
Achieves relevance retrieval of documents in different languages using a unified vector space
Improves retrieval efficiency in multilingual environments
Text Analysis
Multilingual Text Clustering
Automatically groups similar content in different languages
Discovers cross-language similar content
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