Model Bert Base Multilingual Uncased 10 Epochs
This is a sentence embedding model based on sentence-transformers, which can map text to a 256-dimensional vector space and is suitable for semantic search and clustering tasks.
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Release Time : 3/2/2022
Model Overview
This model can convert sentences and paragraphs into 256-dimensional dense vector representations, which can be used for tasks such as calculating sentence similarity, semantic search, and text clustering.
Model Features
Efficient Text Representation
Convert text into 256-dimensional dense vectors while preserving semantic information
Pretrained Architecture
Based on the Transformer architecture of BERT, with powerful semantic understanding capabilities
Easy to Use
Can be easily called through the sentence-transformers library
Model Capabilities
Text Vectorization
Semantic Similarity Calculation
Text Clustering
Semantic Search
Use Cases
Information Retrieval
Semantic Search
Document retrieval based on semantics rather than keyword matching
Text Analysis
Text Clustering
Automatically group documents with similar semantics
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