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Muffakir Embedding
M
Muffakir Embedding
Developed by mohamed2811
An Arabic sentence transformer trained on Egyptian legal books and synthetic data, optimized for semantic text similarity and information retrieval tasks.
Downloads 332
Release Time : 2/20/2025
Model Overview
This model maps Arabic sentences into 768-dimensional dense vectors, suitable for tasks such as legal document retrieval, text clustering, and similarity computation.
Model Features
Legal Domain Optimization
Trained using Egyptian legal books and LLM-generated synthetic data, excelling in legal document retrieval tasks.
Efficient Vector Representation
Generates compact 768-dimensional vector representations, balancing computational efficiency and semantic expressiveness.
Dual Loss Function
Combines MatryoshkaLoss and MultipleNegativesRankingLoss to optimize the embedding space.
Model Capabilities
Semantic Similarity Computation
Legal Document Retrieval
Text Clustering
QA System Support
Use Cases
Legal Technology
Legal Clause Retrieval
Quickly locate relevant legal provisions based on user queries.
High-accuracy semantic matching
Case Law Analysis
Assist legal research through similar case retrieval.
Enhances efficiency for legal professionals
Information Retrieval
Arabic Document Search
Build efficient Arabic-language search engines.
Improves search result relevance
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