https://github.com/lucidrains/tableformer-pytorch

Implementation of TableFormer, Robust Transformer Modeling for Table-Text Encoding, in Pytorch
https://github.com/lucidrains/tableformer-pytorch

artificial-intelligence attention-mechanism deep-learning table transformers

Last synced: 9 months ago
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Implementation of TableFormer, Robust Transformer Modeling for Table-Text Encoding, in Pytorch

• Host: GitHub
• URL: https://github.com/lucidrains/tableformer-pytorch
• Owner: lucidrains
• License: mit
• Created: 2022-03-29T17:44:16.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2022-03-29T21:34:48.000Z (over 4 years ago)
• Last Synced: 2025-03-30T00:14:02.675Z (over 1 year ago)
• Topics: artificial-intelligence, attention-mechanism, deep-learning, table, transformers
• Homepage:
• Size: 291 KB
• Stars: 37
• Watchers: 2
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          


## Tableformer - Pytorch (wip)

Implementation of TableFormer, Robust Transformer Modeling for Table-Text Encoding, in Pytorch. The claim of this paper is that through attentional biases, they can make transformers more robust to perturbations to the table in question. They show improved results compared to TAPAS

## Citations

```bibtex

@article{Yang2022TableFormerRT,

  title   = {TableFormer: Robust Transformer Modeling for Table-Text Encoding},

  author  = {Jingfeng Yang and Aditya Gupta and Shyam Upadhyay and Luheng He and Rahul Goel and Shachi Paul},

  journal = {ArXiv},

  year    = {2022},

  volume = {abs/2203.00274}

}

```