Markus Frohmann

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upvoted a paper over 1 year ago

Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation

Paper • 2406.16678 • Published Jun 24, 2024 • 16

upvoted 2 collections over 1 year ago

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 • 3