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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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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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```
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