Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation Paper • 2406.16678 • Published Jun 24, 2024 • 16
SaT Supervised Mixture (SM) Models Collection SaT (Segment any Text) models, further trained on a Supervised Mixture of diverse styles and corruptions. Universal Sentence Segmentation models! • 6 items • Updated Jun 26, 2024 • 5
SaT Base Models Collection Base SaT (Segment any Text) models, to be used for sentence and paragraph segmentation. Easily adaptable via LoRA. • 6 items • Updated Jun 26, 2024 • 2
Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation Paper • 2406.16678 • Published Jun 24, 2024 • 16
Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation Paper • 2406.16678 • Published Jun 24, 2024 • 16 • 3
SaT Supervised Mixture (SM) Models Collection SaT (Segment any Text) models, further trained on a Supervised Mixture of diverse styles and corruptions. Universal Sentence Segmentation models! • 6 items • Updated Jun 26, 2024 • 5
SaT Supervised Mixture (SM) Models Collection SaT (Segment any Text) models, further trained on a Supervised Mixture of diverse styles and corruptions. Universal Sentence Segmentation models! • 6 items • Updated Jun 26, 2024 • 5
SaT Supervised Mixture (SM) Models Collection SaT (Segment any Text) models, further trained on a Supervised Mixture of diverse styles and corruptions. Universal Sentence Segmentation models! • 6 items • Updated Jun 26, 2024 • 5
SaT Base Models Collection Base SaT (Segment any Text) models, to be used for sentence and paragraph segmentation. Easily adaptable via LoRA. • 6 items • Updated Jun 26, 2024 • 2