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Cross Encoder Umberto Stsb
Cross-encoder model for Italian sentence similarity calculation based on the Umberto architecture
Downloads 34
Release Time : 4/4/2022
Model Overview
This model is trained using the SentenceTransformers framework and is specifically designed to calculate semantic similarity between two Italian sentences, outputting a similarity score between 0 and 1.
Model Features
Italian-specific
Sentence similarity calculation model optimized specifically for Italian
Semantic understanding
Capable of deep semantic understanding of sentences, beyond superficial similarity
Efficient prediction
Adopts a cross-encoder architecture, achieving a good balance between accuracy and efficiency
Model Capabilities
Sentence similarity calculation
Semantic similarity evaluation
Italian text processing
Use Cases
Natural Language Processing
Information retrieval
Matching semantically similar Italian queries and documents in search systems
Improves the relevance of search results
Question answering systems
Determining the semantic match between user questions and candidate answers
Enhances the accuracy of question-answering systems
Text deduplication
Identifying Italian texts with different expressions but the same meaning
Effectively reduces duplicate content
Marco Lodola, Monument to Umberto Eco, Alessandria 2019
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