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Yoruba Embedding Model
This is a multilingual semantic model trained using knowledge distillation methods, specifically optimized for the Yoruba language, capable of handling cross-language sentence similarity tasks.
Downloads 26
Release Time : 12/20/2023
Model Overview
This model implements the method described in the paper 'Enabling Monolingual Sentence Embeddings with Multilingual Capabilities through Knowledge Distillation,' focusing on improving semantic understanding in multilingual environments, with special optimization for Yoruba language performance.
Model Features
Multilingual Semantic Understanding
Enables cross-language semantic understanding through knowledge distillation methods.
Yoruba Language Optimization
Specifically optimized for performance in the Yoruba language.
Sentence Similarity Calculation
Accurately calculates semantic similarity between sentences in different languages.
Model Capabilities
Multilingual sentence embedding
Cross-language semantic similarity calculation
Yoruba text processing
Use Cases
Cross-Language Information Retrieval
Multilingual Document Matching
Find semantically similar content across documents in different languages.
Machine Translation Assistance
Translation Quality Assessment
Evaluate the quality of translation results through sentence similarity.
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