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## Acknowledgments

This model builds upon several foundational works and contributions:

### Base Architecture
- **XLM-RoBERTa**: This model uses XLM-RoBERTa as its base architecture
- Original paper: "Unsupervised Cross-lingual Representation Learning at Scale"
- Authors: Conneau et al.
- License: MIT License

### Training Methodology
We are grateful to the Beijing Academy of Artificial Intelligence (BAAI) for their
contributions to embedding research:

- **RetroMAE**: Self-supervised pre-training methodology
  - Paper: "RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder"
  - Authors: BAAI
  - arXiv: https://arxiv.org/abs/2205.12035

- **BGE-M3**: Multi-lingual embedding research
  - Paper: "BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings"
  - Authors: BAAI
  - arXiv: https://arxiv.org/abs/2402.03216

### Matryoshka Representation Learning
- Paper: "Matryoshka Representation Learning"
- Authors: Kusupati et al.
- Year: 2022
- arXiv: https://arxiv.org/abs/2205.13147

### Training Framework
- Sentence Transformers: https://www.sbert.net
- License: Apache 2.0

Users are encouraged to cite this model and the foundational works when using
it in research or applications.