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Climate Science Reranker

Developed by nicolauduran45
This is a cross-encoder based climate science text reranking model, specifically designed for semantic search and text relevance ranking in the field of climate science.
Downloads 26
Release Time : 5/12/2025

Model Overview

The model calculates scores for text pairs, which can be used for text reranking and semantic search tasks in the climate science domain, fine-tuned based on the MiniLM-L6-v2 architecture.

Model Features

Optimized for Climate Science
Fine-tuned specifically for climate science texts, enabling better understanding of domain-specific terminology and concepts.
High-Performance Reranking
Achieved an NDCG@10 score of 0.7068 on climate science evaluation datasets, demonstrating excellent performance.
Efficient Inference
Based on the MiniLM architecture, it maintains high performance while offering efficient inference.

Model Capabilities

Text Relevance Scoring
Semantic Search Reranking
Climate Science Text Understanding

Use Cases

Academic Research
Climate Science Literature Retrieval
Used in climate science literature retrieval systems to improve search result relevance.
Achieved NDCG@10 of 0.7068 on climate science evaluation datasets
Research Paper Recommendation
Recommends the most relevant climate science research papers based on user queries.
Information Retrieval
Climate Policy Document Retrieval
Helps policymakers quickly find policy documents related to specific climate issues.
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