# Scientific Literature Retrieval

This is a cross-encoder model fine-tuned from cross-encoder/ms-marco-MiniLM-L12-v2, specifically designed for re-ranking and semantic search tasks related to climate texts.

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.

This model is a sentence transformer fine-tuned from nomic-ai/nomic-embed-text-v1.5 on scientific literature datasets, specifically designed for analyzing rhetorical functions in academic texts, such as summarizing results and expressing limitations.

PhysBERT is a text embedding model specifically designed for physics, trained on 1.2 million physics papers, outperforming general models on physics-specific tasks.

A BERT model trained on the CORD19 coronavirus research paper dataset, fine-tuned for natural language inference tasks to generate universal sentence embeddings