Dimensionality Reduction of Sarashina Embedding v2 using Matryoshka Representation Learning
Published:Dec 23, 2025 11:35
•1 min read
•Qiita NLP
Analysis
This article introduces an attempt to reduce the dimensionality of the Sarashina Embedding v2 model using Matryoshka representation learning. The author, Kushal Chottopaddae, a future employee of SoftBank, plans to share their work and knowledge gained from research papers on Qiita. The article's focus is on the practical application of dimensionality reduction techniques to improve the efficiency or performance of the Sarashina Embedding model. The use of Matryoshka representation learning suggests an interest in hierarchical or nested representations, potentially allowing for efficient storage or retrieval of information within the embedding space. The article is likely to delve into the specifics of the implementation and the results achieved.
Key Takeaways
- •The article focuses on dimensionality reduction of the Sarashina Embedding v2 model.
- •Matryoshka representation learning is used for dimensionality reduction.
- •The author plans to share their work and knowledge on Qiita.
Reference
“Hello, I am Kushal Chottopaddae, who will join SoftBank in 2026. I would like to share various efforts and knowledge gained from papers on Qiita. I will be posting various things, so thank you in advance.”