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Rhetoribert

Developed by KaiserML
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.
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Release Time : 1/24/2025

Model Overview

Maps sentences in academic texts to a 768-dimensional vector space, encoding them based on their rhetorical functions, suitable for tasks like functional text similarity, limitation analysis, and rhetorical function classification.

Model Features

Long Text Processing Capability
Supports sequences up to 8192 tokens, suitable for processing long paragraphs in academic literature.
Rhetorical Function Encoding
Optimized specifically for rhetorical functions in academic texts (e.g., stating research objectives, describing methods).
Multi-dimensional Similarity
Trained with MatryoshkaLoss, supporting multi-granularity similarity calculations from 64 to 768 dimensions.
Efficient Retrieval
Achieves a 94.15% nDCG@10 metric on scientific literature retrieval tasks.

Model Capabilities

Academic Text Embedding Generation
Functional Text Similarity Calculation
Scientific Literature Retrieval
Rhetorical Function Classification
Academic Text Clustering Analysis

Use Cases

Academic Research
Literature Retrieval System
Matches relevant research literature based on rhetorical functions.
Achieves 90% accuracy@1 on test sets.
Paper Writing Assistance
Identifies reference sentences with similar rhetorical functions to current writing content.
Educational Technology
Academic Writing Assessment
Analyzes the completeness of rhetorical functions in student papers.
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