Natural language to SQL in low-code platforms

no code implementations • 29 Aug 2023 • Sofia Aparicio, Samuel Arcadinho, João Nadkarni, David Aparício, João Lages, Mariana Lourenço, Bartłomiej Matejczyk, Filipe Assunção

Alongside this, we describe the entire pipeline, which comprises a feedback loop that allows us to quickly collect production data and use it to retrain our SQL generation model.

RWKV: Reinventing RNNs for the Transformer Era

5 code implementations • 22 May 2023 • Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, Xuzheng He, Haowen Hou, Jiaju Lin, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartlomiej Koptyra, Hayden Lau, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Bolun Wang, Johan S. Wind, Stanislaw Wozniak, Ruichong Zhang, Zhenyuan Zhang, Qihang Zhao, Peng Zhou, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu

This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.

Computational Efficiency Natural Language Inference

T5QL: Taming language models for SQL generation

no code implementations • 21 Sep 2022 • Samuel Arcadinho, David Aparício, Hugo Veiga, António Alegria

Automatic SQL generation has been an active research area, aiming at streamlining the access to databases by writing natural language with the given intent instead of writing SQL.

Code Generation Re-Ranking +3

Time Series Imputation

no code implementations • 22 Mar 2019 • Samuel Arcadinho, Paulo Mateus

Multivariate time series is a very active topic in the research community and many machine learning tasks are being used in order to extract information from this type of data.

BIG-bench Machine Learning Imputation +2