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Reranker MiniLM L6 H384 Uncased Gooaq 5 Epoch 1995000

Developed by ayushexel
This is a cross-encoder model fine-tuned from nreimers/MiniLM-L6-H384-uncased, designed for computing scores of text pairs, suitable for text re-ranking and semantic search tasks.
Downloads 24
Release Time : 3/31/2025

Model Overview

This model is a cross-encoder specifically designed to compute similarity scores for text pairs, applicable in tasks such as information retrieval and question-answering systems for text re-ranking.

Model Features

Efficient Text Re-ranking
Accurately computes similarity scores for text pairs, effectively improving the ranking quality of retrieval systems.
Based on MiniLM Architecture
Utilizes the lightweight MiniLM architecture, enhancing inference efficiency while maintaining performance.
Multi-dataset Validation
Validated on multiple datasets (gooaq, NanoMSMARCO, etc.), demonstrating stable performance.

Model Capabilities

Text Similarity Calculation
Semantic Search
Question-Answering System Re-ranking
Information Retrieval Optimization

Use Cases

Information Retrieval
Search Engine Result Re-ranking
Re-ranks search engine results to improve the ranking of the most relevant results.
Achieved an NDCG@10 of 0.5149 on the gooaq development set.
Question-Answering Systems
Candidate Answer Ranking
Ranks multiple candidate answers generated by a question-answering system based on relevance.
Achieved an NDCG@10 of 0.4065 on the NanoNQ dataset.
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