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Medical Embedded V4

Developed by shtilev
This is a multilingual sentence embedding model that can map sentences and paragraphs to a 768-dimensional vector space, suitable for tasks such as clustering and semantic search.
Downloads 202
Release Time : 6/21/2025

Model Overview

This model is based on the XLM-RoBERTa architecture and can generate high-quality sentence embeddings. It supports multiple languages and is suitable for scenarios such as semantic similarity calculation and information retrieval.

Model Features

Multilingual support
Supports sentence embeddings for multiple languages including Arabic, Bulgarian, and Catalan
High-quality embeddings
Based on the XLM-RoBERTa architecture, generates high-quality 768-dimensional sentence embedding vectors
Semantic understanding
Can effectively capture the semantic information of sentences, suitable for semantic similarity calculation

Model Capabilities

Sentence embedding
Semantic similarity calculation
Multilingual text processing
Information retrieval
Text clustering

Use Cases

Information retrieval
Semantic search
Use sentence embeddings to implement document search based on semantics rather than keywords
Improve the relevance of search results
Text analysis
Text clustering
Automatically group documents with similar semantics
Implement unsupervised document classification
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