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Bge M3 Spa Law Qa

Developed by littlejohn-ai
A Spanish sentence embedding model fine-tuned based on BAAI/bge-m3, specifically optimized for the legal domain, suitable for semantic search and information retrieval tasks.
Downloads 309
Release Time : 7/22/2024

Model Overview

This model maps sentences and paragraphs into a 1024-dimensional dense vector space, applicable for various tasks such as semantic text similarity, semantic search, paraphrase mining, text classification, and clustering, especially suitable for processing Spanish legal texts.

Model Features

Legal Domain Optimization
Fine-tuned specifically for Spanish legal texts, excelling in semantic understanding and retrieval tasks within the legal domain.
Long Text Support
Supports sequences up to 8192 tokens, ideal for processing long texts such as legal documents.
High-Dimensional Embedding
Generates 1024-dimensional dense vector representations, capable of capturing rich semantic information from texts.
Multi-Task Support
Applicable for various natural language processing tasks, including semantic similarity calculation, information retrieval, text classification, and more.

Model Capabilities

Semantic Text Similarity Calculation
Semantic Search
Paraphrase Mining
Text Classification
Text Clustering
Information Retrieval

Use Cases

Legal Information Retrieval
Legal Q&A System
Used to build Q&A systems in the legal domain, quickly retrieving relevant legal provisions and case law.
Achieved a MAP@100 score of 0.6991 in evaluations
Legal Document Analysis
Analyzing and classifying large volumes of legal documents to extract key information.
Legal Text Similarity Calculation
Calculating semantic similarity between legal provisions, contract clauses, and other texts.
Government Agency Applications
Regulation Retrieval System
Assisting government staff in quickly locating relevant regulations and policies.
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