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@@ -86,4 +86,19 @@ for i in range(len(sentences)):
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  print(sen_1, '----->', sen_2, sim_score)
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  ```
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- ## Best MSE: 7.57528096437454
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  print(sen_1, '----->', sen_2, sim_score)
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  ```
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+ ## Best MSE: 7.57528096437454
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+
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+ ## Citation
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+
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+ If you use this model, please cite the following paper:
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+
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+ @INPROCEEDINGS{10754765,
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+ author={Uddin, Md. Shihab and Haque, Mohd Ariful and Rifat, Rakib Hossain and Kamal, Marufa and Gupta, Kishor Datta and George, Roy},
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+ booktitle={2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)},
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+ title={Bangla SBERT - Sentence Embedding Using Multilingual Knowledge Distillation},
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+ year={2024},
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+ volume={},
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+ number={},
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+ pages={495-500},
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+ keywords={Sentiment analysis;Machine learning algorithms;Accuracy;Text categorization;Semantics;Transformers;Mobile communication;Information retrieval;Machine translation;Sentence Similarity;Sentence Transformer;SBERT;Knowledge Distillation;Bangla NLP},
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+ doi={10.1109/UEMCON62879.2024.10754765}}