Dataset · Hugging Face

mteb/sts15-sts

Name: sts15-sts
Creator: mteb
License: unknown
Keywords: task_categories:sentence-similarity, task_ids:semantic-similarity-scoring, annotations_creators:human-annotated, multilinguality:monolingual, language:eng, license:unknown, size_categories:1K<n<10K, format:json, modality:text, library:datasets, library:pandas, library:polars, library:mlcroissant, arxiv:2502.13595, arxiv:2210.07316

STS15 An MTEB dataset Massive Text Embedding Benchmark SemEval STS 2015 dataset Task category t2t Domains Blog, News, Web, Written, Spoken Reference https://www.aclweb.org/anthology/S15-2010 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task = mteb.get_tasks(["STS15"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more about… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts15-sts.

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Author / organization	mteb
License (as declared)	unknown
Downloads	9K (as reported by Hugging Face)
Likes	2
Created	20 April 2022
Last updated at source	24 February 2026

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17 Oct 2026 Description: STS15 An MTEB dataset Massive Text Embedding Benchmark SemEval STS 2015 dataset Task category t2t Domains Blog, News, Web, Written, Spoken Reference https://www.aclweb.org/anthology/S15-2010 How to evaluate on this task You can evaluate an → STS15 An MTEB dataset Massive Text Embedding Benchmark SemEval STS 2015 dataset Task category t2t Domains Blog, News, Web, Written, Spoken Reference https://www.aclweb.org/anthology/S15-2010 How to evaluate on this task You can evaluate an

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