Semantic Textual Similarity On Sts16

Métriques

Spearman Correlation

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Spearman Correlation	Paper Title	Repository
PromptEOL+CSE+LLaMA-30B	0.8627	Scaling Sentence Embeddings with Large Language Models	-
DiffCSE-RoBERTa-base	0.8212	DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings	-
Trans-Encoder-BERT-base-bi (unsup.)	0.8305	Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations	-
Trans-Encoder-RoBERTa-large-cross (unsup.)	0.8503	Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations	-
Trans-Encoder-BERT-large-bi (unsup.)	0.8481	Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations	-
BERTlarge-flow (target)	0.7763	On the Sentence Embeddings from Pre-trained Language Models	-
Trans-Encoder-RoBERTa-base-cross (unsup.)	0.8377	Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations	-
PromptEOL+CSE+OPT-13B	0.8590	Scaling Sentence Embeddings with Large Language Models	-
AnglE-LLaMA-13B	0.8700	AnglE-optimized Text Embeddings	-
PromCSE-RoBERTa-large (0.355B)	0.8496	Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning	-
IS-BERT-NLI	0.7016	An Unsupervised Sentence Embedding Method by Mutual Information Maximization	-
AnglE-LLaMA-7B	0.8691	AnglE-optimized Text Embeddings	-
Mirror-RoBERTa-base (unsup.)	0.78	Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders	-
DiffCSE-BERT-base	0.8054	DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings	-
PromptEOL+CSE+OPT-2.7B	0.8591	Scaling Sentence Embeddings with Large Language Models	-
Dino (STSb/̄	0.7718	Generating Datasets with Pretrained Language Models	-
SimCSE-RoBERTalarge	0.8393	SimCSE: Simple Contrastive Learning of Sentence Embeddings	-
AnglE-LLaMA-7B-v2	0.8700	AnglE-optimized Text Embeddings	-
SRoBERTa-NLI-large	0.7682	Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks	-
Mirror-BERT-base (unsup.)	0.743	Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders	-

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