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Stsb TinyBERT L4
A cross-encoder model trained on the TinyBERT-L4 architecture for predicting semantic similarity scores (0-1) between two sentences.
Downloads 17.62k
Release Time : 3/2/2022
Model Overview
This model is specifically designed to calculate the semantic similarity between two texts, outputting a score between 0 and 1, where higher scores indicate greater semantic similarity. Suitable for scenarios requiring precise text matching.
Model Features
Efficient Semantic Matching
Optimized based on the TinyBERT-L4 architecture, maintaining high accuracy while ensuring good inference efficiency.
Precise Similarity Scoring
Outputs a continuous score between 0-1, allowing for precise measurement of semantic similarity between text pairs.
Model Capabilities
Semantic Similarity Calculation
Text Pair Matching
Semantic Relevance Assessment
Use Cases
Information Retrieval
Search Result Ranking
Ranking search results based on semantic relevance
Improves the matching accuracy between search results and query intent.
Question Answering Systems
Answer Matching
Evaluating the semantic match between candidate answers and questions
Enhances the accuracy of question-answering systems.
Transformers
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