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  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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- IndoSBERT is a modification of `https://huggingface.co/indobenchmark/indobert-large-p1` that has been fine-tuned using the siamese network scheme inspired by SBERT.
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  This model was fine-tuned with the STS Dataset (2012-2016) which was machine-translated into Indonesian languange.
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  This model can provide meaningful semantic sentence embeddings for Indonesian sentences.
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  ```
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  ## Citing & Authors
 
 
 
 
 
 
 
 
 
 
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- <!--- Describe where people can find more information -->
 
 
 
 
 
 
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  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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+ IndoSBERT is a modification of `https://huggingface.co/indobenchmark/indobert-large-p1` that has been fine-tuned using the siamese network scheme inspired by SBERT (Reimers et al., 2019).
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  This model was fine-tuned with the STS Dataset (2012-2016) which was machine-translated into Indonesian languange.
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  This model can provide meaningful semantic sentence embeddings for Indonesian sentences.
 
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  ```
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  ## Citing & Authors
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+ ```
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+ @inproceedings{reimers-2019-sentence-bert,
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+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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+ author = "Reimers, Nils and Gurevych, Iryna",
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+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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+ month = "11",
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+ year = "2019",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://arxiv.org/abs/1908.10084",
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+ }
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+ @article{author = {Diana, Denaya},
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+ title = {IndoSBERT: Indonesian SBERT for Semantic Textual Similarity tasks},
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+ year = {2023},
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+ url = {https://huggingface.co/denaya/indoSBERT-large}
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+ }
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+ ```