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Roberta Base Bne Finetuned Msmarco Qa Es Mnrl Mn

Developed by dariolopez
This is a Spanish-based sentence-transformers model specifically designed for question-answering scenarios, capable of mapping sentences and paragraphs into a 768-dimensional vector space, suitable for semantic search and clustering tasks.
Downloads 347.38k
Release Time : 5/3/2023

Model Overview

This model is a fine-tuned version based on PlanTL-GOB-ES/roberta-base-bne, trained using the Spanish-translated MS-MARCO dataset, focusing on question-answering tasks.

Model Features

Spanish optimization
Designed specifically for Spanish, especially suitable for Spanish question-answering scenarios.
High-dimensional vector mapping
Can map sentences and paragraphs into a 768-dimensional dense vector space, supporting semantic search and clustering tasks.
Efficient training
Trained using MultipleNegativesRankingLoss, optimizing performance in question-answering scenarios.

Model Capabilities

Sentence similarity calculation
Semantic search
Text clustering

Use Cases

Information retrieval
Question-answering system
Used to build Spanish question-answering systems, quickly retrieving answers related to questions.
Can accurately match questions with relevant text segments.
Content recommendation
Similar content recommendation
Recommends similar texts or paragraphs based on user input.
Provides highly relevant recommendation results.
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