README.md

---
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}
}
```