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IndoSBERT: Enhancing Indonesian Sentence Embeddings with Siamese Networks Fine-tuning paper by Independent / Community
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Paper

IndoSBERT: Enhancing Indonesian Sentence Embeddings with Siamese Networks Fine-tuning

by Independent / Community 00df88ac781304a479ccd64b2aa3e3f829c67822
Free2AITools Nexus Index
64.7
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 74
P: Popularity 49
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Sentence embeddings hold a crucial role in Indonesian NLP research, being applied in various domains such as essay scoring, summarization, text-to-image generation, text classification, and others. By this research, we present IndoSBERT, a modification of BERT that has been fine-tuned using the siamese network scheme inspired by SBERT. This model was fine-tuned with the STS Benchmark Dataset which was translated into Indonesian languange. Our model can provide meaningful semantic sentence emb...

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Semantic Scholar 7 Citations
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Registry ID 00df88ac781304a479ccd64b2aa3e3f829c67822
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{00df88ac781304a479ccd64b2aa3e3f829c67822,
  author = {Unknown},
  title = {IndoSBERT: Enhancing Indonesian Sentence Embeddings with Siamese Networks Fine-tuning Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00df88ac781304a479ccd64b2aa3e3f829c67822}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). IndoSBERT: Enhancing Indonesian Sentence Embeddings with Siamese Networks Fine-tuning [Paper]. Free2AITools. https://api.semanticscholar.org/00df88ac781304a479ccd64b2aa3e3f829c67822

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 74
Popularity (P) 49
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for IndoSBERT: Enhancing Indonesian Sentence Embeddings with Siamese Networks Fine-tuning: Authority (A:74), Popularity (P:49), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
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πŸ“ Executive Summary

"Sentence embeddings hold a crucial role in Indonesian NLP research, being applied in various domains such as essay scoring, summarization, text-to-image generation, text classification, and others. By this research, we present IndoSBERT, a modification of BERT that has been fine-tuned using the siamese network scheme inspired by SBERT. This model was fine-tuned with the STS Benchmark Dataset which was translated into Indonesian languange. Our model can provide meaningful semantic sentence emb..."

❝ Cite Node 

@article{Unknown2026IndoSBERT:,
  title={IndoSBERT: Enhancing Indonesian Sentence Embeddings with Siamese Networks Fine-tuning},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ7CitationsSemantic Scholar
πŸ›οΈ74AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈknowledge retrievalField

🏷️ Research Topics

embeddingsimage generationfine tuning
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author
Unknown
license
ArXiv
tags
paper, research, academic

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null
params billions
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