Pith Number

pith:6AYQL7GP

pith:2019:6AYQL7GPK53UOJG44P4JOAEH4X

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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Iryna Gurevych, Nils Reimers

Sentence-BERT uses siamese and triplet training on BERT to create fixed sentence embeddings that support fast cosine-similarity comparisons while matching original accuracy.

arxiv:1908.10084 v1 · 2019-08-27 · cs.CL

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Claims

C1strongest claim

we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT.

C2weakest assumption

That fine-tuning BERT with siamese and triplet networks produces standalone sentence embeddings whose cosine similarities accurately reflect semantic similarity at the level of the original pairwise BERT inference.

C3one line summary

Sentence-BERT adapts BERT with siamese and triplet networks to produce sentence embeddings for efficient cosine-similarity comparisons, cutting computation time from hours to seconds on similarity search while matching BERT accuracy.

References

38 extracted · 38 resolved · 5 Pith anchors

[1] Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Inigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, and Janyce Wiebe 2015

[2] Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2014. https://doi.org/10.3115/v1/S14-2010 S em E val 2014 · doi:10.3115/v1/s14-2010

[3] Eneko Agirre, Carmen Banea, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez - Agirre, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2016. http://aclweb.org/anthology/S/S16/S16-1081.pdf SemEval-2016 Task 2016

[4] Eneko Agirre, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, and Weiwei Guo. 2013. https://www.aclweb.org/anthology/S13-1004 * SEM 2013 shared task: Semantic Textual Similarity . In Second Joint Confer 2013

[5] Eneko Agirre, Mona Diab, Daniel Cer, and Aitor Gonzalez-Agirre. 2012. http://dl.acm.org/citation.cfm?id=2387636.2387697 SemEval-2012 Task 6: A Pilot on Semantic Textual Similarity . In Proceedings of 2012

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Cited by

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Receipt and verification

First computed	2026-07-04T23:59:53.701495Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f03105fccf57774724dce3f8970087e5f2bb4b017ebb448e1d078a171acb956c

Aliases

arxiv: 1908.10084 · arxiv_version: 1908.10084v1 · doi: 10.48550/arxiv.1908.10084 · pith_short_12: 6AYQL7GPK53U · pith_short_16: 6AYQL7GPK53UOJG4 · pith_short_8: 6AYQL7GP

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/6AYQL7GPK53UOJG44P4JOAEH4X \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f03105fccf57774724dce3f8970087e5f2bb4b017ebb448e1d078a171acb956c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "aab77e2d42b30ca5519979a32ff7f90487fe7e727b45fefa440feeec411de6ed",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2019-08-27T08:50:17Z",
    "title_canon_sha256": "53a538c63e761741689bc54af71fddd3f514ca77502874d1ba18aa7479f59a04"
  },
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  "source": {
    "id": "1908.10084",
    "kind": "arxiv",
    "version": 1
  }
}