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We then propose a new self-supervised method called Contrastive Tension (CT) to counter such biases. CT frames the training objective as a noise-contrastive task between the final layer representations of two independent models, in turn making the final layer representations suitable for feature extraction.
Jan 12, 2021
Contrastive Tension(CT) is a fully self-supervised algorithm for re-tuning already pre-trained transformer Language Models, and achieves State-Of-The-Art(SOTA) ...
Jul 28, 2022 · Wethen propose a new self-supervised method called Contrastive Tension (CT) tocounter such biases. CT frames the training objective as a noise- ...
AugCSE is presented, a unified framework to utilize diverse sets of data augmentations to achieve a better, general-purpose, sentence embedding model, ...
Semantic Re-tuning with Contrastive Tension · FREDRIK CARLSSON et. al. Related Organizations: Filter. Score, Organization, Technology, Search. 0.04, UNIVERSITY ...
Extracting semantically useful natural language sentence representations from pre-trained deep neural networks such as Transformers remains a challenge. We.
Mar 14, 2024 · Semantic re-tuning with contrastive tension. In International conference on learning rep- resentations. Daniel Cer, Mona Diab, Eneko Agirre ...
present in Semantic Re-Tuning With Contrastive Tension (CT) (Github) an unsupervised learning approach for sentence embeddings that just requires sentences.
Carlsson et al. present in Semantic Re-Tuning With Contrastive Tension (CT) an unsupervised method that uses two models: If the same sentences are passed to ...
Semantic Re-tuning with Contrastive Tension Fredrik Carlsson, Amaru Cuba Gyllensten, Evangelia Gogoulou, Erik Ylipää Hellqvist, Magnus Sahlgren ICLR 2021 ...