Methods › Natural Language Processing › Sentence Embeddings › DeCLUTR
DeCLUTR
Introduced by John Giorgi et al. in DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
DeCLUTR is an approach for learning universal sentence embeddings that utilizes a self-supervised objective that does not require labelled training data. The objective learns universal sentence embeddings by training an encoder to minimize the distance between the embeddings of textual segments randomly sampled from nearby in the same document.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
-
Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation 18 Apr 2021 · 0 repositories · arXiv:2104.08945
-
Are Classes Clusters? 16 Apr 2021 · 0 repositories · arXiv:2104.07840
-
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations 5 Jun 2020 · 2 repositories · arXiv:2006.03659Syntology ran 0 of 1 samples · 1 unverified
Tasks archive 2025-07-28
17 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections