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Cross Encoder Italian Bert Stsb
This model is a cross-encoder trained on Italian language for predicting semantic similarity scores between two sentences.
Downloads 312
Release Time : 5/10/2023
Model Overview
The model is trained using the Cross-Encoder class from SentenceTransformers, based on the Italian version of the STS benchmark dataset, and can predict semantic similarity scores between 0 and 1.
Model Features
Italian language support
Optimized specifically for Italian text, capable of accurately analyzing semantic similarity between Italian sentences.
Cross-encoder architecture
Adopts a cross-encoder architecture that processes two sentences simultaneously and directly outputs similarity scores, improving prediction accuracy.
Semantic similarity prediction
Can predict semantic similarity scores between two sentences, ranging from 0 to 1, suitable for various text matching tasks.
Model Capabilities
Semantic similarity calculation
Italian text processing
Sentence pair scoring
Use Cases
Text matching
Information retrieval
Used in retrieval systems to score the semantic match between queries and documents.
Improves the relevance of retrieval results
Question answering systems
Evaluates the semantic relevance between questions and candidate answers.
Enhances the accuracy of question answering systems
Natural language processing
Text deduplication
Identifies semantically similar texts to achieve deduplication.
Effectively reduces redundant information
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