What question did this study set out to answer?

This research aims to evaluate the effectiveness of hyperbolic projection for compressing sentence embeddings.

January 14, 20260 citationsOpen Access

Contrastive Geometric Transfer: Efficient Sentence Embeddings via Hyperbolic Projection with 24× Compression

Key Points

This research aims to evaluate the effectiveness of hyperbolic projection for compressing sentence embeddings.
Used Contrastive Geometric Transfer method for compression.
Evaluated performance on STS-B benchmark with MPNet and MiniLM models.
Measured quality retention using Spearman correlation coefficient.
Achieved 24× compression from 768d to 32d with 97.4% quality retention.
Outperformed PCA by 5.9% at 32d and 16.7% at 16d.
Identified semantic degradation threshold at 5 dimensions.

Abstract

Experimental results for Contrastive Geometric Transfer (CGT), a method for compressing sentence embeddings via hyperbolic projection. Achieves 24× compression (768d→32d) with 97.4% quality retention (Spearman ρ=0.812 vs baseline 0.834). Outperforms PCA by 5.9% at 32d and 16.7% at 16d. Identifies semantic degradation threshold at 5 dimensions. Evaluated on STS-B benchmark using MPNet and MiniLM teacher models. Includes LaTeX manuscript, JSON experimental results, and Jupyter notebooks for reproducibility

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Cite This Study

Éric Reis (Sat,) studied this question.

synapsesocial.com/papers/6966f33b13bf7a6f02c0130d https://doi.org/https://doi.org/10.5281/zenodo.18225189

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