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@@ -17,7 +17,7 @@ metrics:
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  - spearmanr
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  ---
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  ## Model Description:
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- [**vietnamese-embedding-LongContext**](https://huggingface.co/dangvantuan/vietnamese-embedding-LongContext) is the Embedding Model for Vietnamese language with context length up to 8096 tokens. This model is a specialized sentence-embedding trained specifically for the Vietnamese language, which is built upon [gte-multilingual](Alibaba-NLP/gte-multilingual-base) and trained using the Multi-Negative Ranking Loss, Matryoshka2dLoss and SimilarityLoss.
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  ## Full Model Architecture
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  ```
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  **Spearman score**
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  | Model | [STSB] | [STS12]| [STS13] | [STS14] | [STS15] | [STS16] | [SICK] | Mean |
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  |-----------------------------------------------------------|---------|----------|----------|----------|----------|----------|---------|--------|
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- | [dangvantuan/vietnamese-embedding](https://huggingface.co/dangvantuan/vietnamese-embedding) |**84.84**| **79.04**| **85.30**| **81.38**| **87.06**| **79.95**| **79.58**| **82.45**|
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  | [dangvantuan/vietnamese-embedding-LongContext](https://huggingface.co/dangvantuan/vietnamese-embedding-LongContext) |85.25| 75.77| 83.82| 81.69| 88.48| 81.5| 78.2| 82.10|
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  ## Citation
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  journal={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
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  year={2020}
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  }
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- @article{thakur2020augmented,
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  title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks},
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  author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna},
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  journal={arXiv e-prints},
 
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  - spearmanr
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  ---
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  ## Model Description:
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+ [**vietnamese-embedding-LongContext**](https://huggingface.co/dangvantuan/vietnamese-embedding-LongContext) is the Embedding Model for Vietnamese language with context length up to 8096 tokens. This model is a specialized text-embedding trained specifically for the Vietnamese language, which is built upon [gte-multilingual](Alibaba-NLP/gte-multilingual-base) and trained using the Multi-Negative Ranking Loss, Matryoshka2dLoss and SimilarityLoss.
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  ## Full Model Architecture
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  ```
 
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  **Spearman score**
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  | Model | [STSB] | [STS12]| [STS13] | [STS14] | [STS15] | [STS16] | [SICK] | Mean |
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  |-----------------------------------------------------------|---------|----------|----------|----------|----------|----------|---------|--------|
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+ | [dangvantuan/vietnamese-embedding](https://huggingface.co/dangvantuan/vietnamese-embedding) |84.84| 79.04| 85.30| 81.38| 87.06| 79.95| 79.58| 82.45|
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  | [dangvantuan/vietnamese-embedding-LongContext](https://huggingface.co/dangvantuan/vietnamese-embedding-LongContext) |85.25| 75.77| 83.82| 81.69| 88.48| 81.5| 78.2| 82.10|
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  ## Citation
 
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  journal={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
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  year={2020}
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  }
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+ @article{thakur2020augmented,
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  title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks},
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  author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna},
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  journal={arXiv e-prints},