Updated 10 months ago

https://github.com/amazon-science/robust-tableqa • Science 26%

Two approaches for robust TableQA: 1) ITR is a general-purpose retrieval-based approach for handling long tables in TableQA transformer models. 2) LI-RAGE is a robust framework for open-domain TableQA which addresses several limitations. (ACL 2023)

Updated 10 months ago

https://github.com/amazon-science/h3-indexer • Science 26%

The h3-indexer is an open source package for indexing geospatial data using PySpark, Apache Sedona and the H3 hierarchical spatial indexing system. The h3-indexer maps any number of vector-type geospatial data sets to H3 grids for efficient spatial analysis and querying.

Updated 10 months ago

https://github.com/amazon-science/job-posting-structure • Science 49%

Extract structured information from job postings.

Updated 10 months ago

https://github.com/amazon-science/spherical_diffusion_policy • Science 36%

[ICML 2025] Official implementation of Spherical Diffusion Policy: A SE(3) Equivariant Visuomotor Policy with Spherical Fourier Representation

Updated 10 months ago

https://github.com/amazon-science/omnimatch • Science 36%

OmniMatch: Joinability Discovery in Data Products

Updated 10 months ago

https://github.com/amazon-science/wqa-multi-sentence-inference • Science 49%

This repository contains code used for our Multi Sentence Inference NAACL'22 paper.

Updated 10 months ago

https://github.com/amazon-science/repoformer • Science 36%

Repoformer: Selective Retrieval for Repository-Level Code Completion (ICML 2024)

Updated 10 months ago

https://github.com/amazon-science/dq-bart • Science 36%

DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (ACL 2022)

Updated 10 months ago

https://github.com/amazon-science/street-reasoning • Science 26%

STREET: a multi-task and multi-step reasoning dataset

Updated 10 months ago

https://github.com/amazon-science/transformers-data-augmentation • Science 26%

Code associated with the "Data Augmentation using Pre-trained Transformer Models" paper