README.md

metadata

library_name: transformers
pipeline_tag: table-question-answering
license: mit
datasets:
  - ethanbradley/synfintabs
language:
  - en
base_model:
  - microsoft/layoutlm-base-uncased

FinTabQA: Financial Table Question-Answering

A model for financial table question-answering using the LayoutLM architecture.

Quick start

To get started with FinTabQA, load it, and the tokenizer, like you would any other Hugging Face Transformer model.

from transformers import LayoutLMForQuestionAnswering, LayoutLMTokenizer

model = LayoutLMForQuestionAnswering.from_pretrained("ethanbradley/fintabqa")
tokenizer = LayoutLMTokenizer.from_pretrained(
    "microsoft/layoutlm-base-uncased")

Citation

If you use this model, please cite both the article using the citation below and the model itself.

@misc{bradley2024synfintabs,
      title         = {Syn{F}in{T}abs: A Dataset of Synthetic Financial Tables for Information and Table Extraction},
      author        = {Bradley, Ethan and Roman, Muhammad and Rafferty, Karen and Devereux, Barry},
      year          = {2024},
      eprint        = {2412.04262},
      archivePrefix = {arXiv},
      primaryClass  = {cs.LG},
      url           = {https://arxiv.org/abs/2412.04262}
}