Sbert Large Mt Nlu Ru
This is a Russian sentence embedding model based on PyTorch and Transformers, supporting multi-task processing and capable of generating high-quality sentence vector representations.
Downloads 10.25k
Release Time : 3/2/2022
Model Overview
This model is specifically designed for Russian, utilizing the BERT architecture and optimized for sentence embedding quality through multi-task learning, suitable for various natural language understanding tasks.
Model Features
Multi-task learning
Optimizes model performance through a multi-task learning framework, enhancing sentence embedding quality.
Case-sensitive
The model can distinguish between uppercase and lowercase letters, preserving case information in the text.
High-quality embeddings
Generates high-quality sentence vector representations using average token embedding methods.
Model Capabilities
Sentence vector generation
Text similarity calculation
Semantic search
Use Cases
Information retrieval
Semantic search
Using sentence embeddings for semantic similarity search.
Performed excellently in the Russian SuperGLUE evaluation.
Text analysis
Text classification
Using sentence embeddings as features for text classification.
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