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Reranker Msmarco MiniLM L12 H384 Uncased Lambdaloss

Developed by tomaarsen
This is a cross-encoder model fine-tuned on MiniLM-L12-H384-uncased for text re-ranking and semantic search tasks.
Downloads 1,019
Release Time : 3/14/2025

Model Overview

The model computes scores for text pairs, primarily used in text re-ranking and semantic search scenarios, effectively improving the accuracy of search results.

Model Features

Efficient Text Re-ranking
Capable of quickly computing relevance scores for text pairs, suitable for large-scale search result reordering.
LambdaLoss Optimization
Trained using the LambdaLoss function, optimizing ranking task metrics.
Lightweight Model
Based on the MiniLM architecture, reducing model size and computational requirements while maintaining performance.

Model Capabilities

Text Relevance Scoring
Search Result Re-ranking
Semantic Search
Q&A System Ranking

Use Cases

Information Retrieval
Search Engine Result Optimization
Re-rank preliminary search engine results to improve the ranking of relevant results.
Achieved an average precision of 0.6352 on the NanoMSMARCO dataset.
Q&A Systems
Answer Candidate Ranking
Rank multiple candidate answers generated by a Q&A system by relevance.
Achieved an average precision of 0.7174 on the NanoNQ dataset.
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