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---
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](https://huggingface.co/microsoft/layoutlm-base-uncased) architecture.
## Quick start
To get started with FinTabQA, load it, and the tokenizer, like you would any other Hugging Face Transformer model.
```python3
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.
```bib
@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}
}
```
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