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Models

All models matching “sts”
sentence-transformers-testing /stsb-bert-tiny-safetensorssentence-similaritysentence-transformers4 likes1.9m downloads3y agoHugging Faceemrecan /bert-base-turkish-cased-mean-nli-stsb-trsentence-similaritysentence-transformerstr52 likes427k downloads17d agoHugging Faceaari1995 /German_Semantic_STS_V2sentence-similaritysentence-transformersde49 likes160k downloads2y agoHugging Facecross-encoder /stsb-roberta-basetext-rankingsentence-transformersen5 likes159k downloads1y agoHugging Facecross-encoder /stsb-distilroberta-basetext-rankingsentence-transformersen8 likes105k downloads1y agoHugging Facesentence-transformers /stsb-roberta-base-v2sentence-similaritysentence-transformerstf6 likes82k downloads2y agoHugging Facecross-encoder /stsb-roberta-largetext-rankingsentence-transformersen14 likes76k downloads1y agoHugging Facejhgan /ko-sbert-stssentence-similaritysentence-transformerstf13 likes74k downloads4y agoHugging Face

Datasets

All datasets matching “sts”
mteb /sts12-sts STS12 An MTEB dataset Massive Text Embedding Benchmark SemEval-2012 Task 6. Task category t2t Domains Encyclopaedic, News, Written Reference https://www.aclweb.org/anthology/S12-1051.pdf 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(["STS12"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more about how to… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts12-sts.textsentence-similarity1K<n<10K8 likes82k downloads7mo agoHugging Facemteb /sts22-crosslingual-sts STS22.v2 An MTEB dataset Massive Text Embedding Benchmark SemEval 2022 Task 8: Multilingual News Article Similarity. Version 2 filters updated on STS22 by removing pairs where one of entries contain empty sentences. Task category t2t Domains News, Written Reference https://competitions.codalab.org/competitions/33835 How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: import mteb task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts22-crosslingual-sts.textsentence-similarity10K<n<100K16 likes32k downloads7mo agoHugging Facemteb /sickr-sts SICK-R An MTEB dataset Massive Text Embedding Benchmark Semantic Textual Similarity SICK-R dataset Task category t2t Domains Web, Written Reference https://aclanthology.org/L14-1314/ 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(["SICK-R"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more about how to run models… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sickr-sts.textsentence-similarity1K<n<10K5 likes24k downloads7mo agoHugging Facemteb /sts13-sts STS13 An MTEB dataset Massive Text Embedding Benchmark SemEval STS 2013 dataset. Task category t2t Domains Web, News, Non-fiction, Written Reference https://www.aclweb.org/anthology/S13-1004/ 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(["STS13"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more about how… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts13-sts.textsentence-similarity1K<n<10K1 likes23k downloads7mo agoHugging Facemteb /sts14-sts STS14 An MTEB dataset Massive Text Embedding Benchmark SemEval STS 2014 dataset. Currently only the English dataset Task category t2t Domains Blog, Web, Spoken Reference https://www.aclweb.org/anthology/S14-1002 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(["STS14"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sts14-sts.textsentence-similarity1K<n<10K2 likes23k downloads7mo agoHugging Facemteb /biosses-sts BIOSSES An MTEB dataset Massive Text Embedding Benchmark Biomedical Semantic Similarity Estimation. Task category t2t Domains Medical Reference https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html 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(["BIOSSES"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) To learn more… See the full description on the dataset page: https://huggingface.co/datasets/mteb/biosses-sts.textsentence-similarityn<1K2 likes22k downloads1y agoHugging Face

Apps

All apps matching “sts”
suoyi /stspacestreamlit0 likes4y agoHugging Facestsyaakirah /appstreamlit0 likes4y agoHugging Facestsyaakirah /Milestone2streamlit0 likes4y agoHugging Facestsyaakirah /M2P1streamlit0 likes4y agoHugging Facestsyaakirah /Customer-Depositstreamlit0 likes4y agoHugging Facelibrarykid /sentence-transformers-distilbert-base-nli-stsb-mean-tokensgradio0 likes3y agoHugging Face

Agents

All agents matching “sts”
alexmercer /miloTurns product notes into small, reviewable pull requests. Prefers three boring PRs over one clever one.Frontend Engineerworking312 likes96 tasks7mo agoalexmercer /patchReviews diffs like a tired but fair maintainer. Will ask why that function exists.Code Reviewerworking268 likes214 tasks7mo agonoahvale /arcDraws the boundary you've been avoiding, then costs out both sides of it.Systems Engineeridle88 likes26 tasks4mo ago