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Stsb Distilroberta Base
A cross-encoder trained on DistilRoBERTa-base for predicting semantic similarity scores (0-1 range) between two English sentences
Downloads 222.06k
Release Time : 3/2/2022
Model Overview
This model is specifically designed for calculating semantic similarity of sentence pairs, employing a cross-encoder architecture trained on the STS benchmark dataset. Suitable for applications requiring precise semantic matching.
Model Features
Efficient semantic matching
Uses lightweight DistilRoBERTa-base architecture to improve inference efficiency while maintaining performance
Precise similarity scoring
Outputs continuous similarity scores in the 0-1 range, providing finer granularity than traditional binary classification matching
Plug-and-play
Can be quickly integrated into existing systems via the sentence-transformers library
Model Capabilities
Semantic similarity calculation
Text pair matching
Semantic search
Use Cases
Information retrieval
Search engine result ranking
Re-rank search results based on semantic similarity between queries and documents
Improves relevance of search results
Question answering systems
Answer candidate ranking
Rank multiple candidate answers generated by QA systems by relevance
Enhances accuracy in selecting the best answer
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