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 a fast tokenizer, like you would any other Hugging Face Transformer model and tokenizer. Below is a minimum working example using the [SynFinTabs](https://huggingface.co/datasets/ethanbradley/synfintabs) dataset.

```python3
>>> from typing import List, Tuple
>>> from datasets import load_dataset
>>> from transformers import LayoutLMForQuestionAnswering, LayoutLMTokenizerFast
>>> import torch
>>> 
>>> synfintabs_dataset = load_dataset("ethanbradley/synfintabs")
>>> model = LayoutLMForQuestionAnswering.from_pretrained("ethanbradley/fintabqa")
>>> tokenizer = LayoutLMTokenizerFast.from_pretrained(
...     "microsoft/layoutlm-base-uncased")
>>> 
>>> def normalise_boxes(
...         boxes: List[List[int]],
...         old_image_size: Tuple[int, int],
...         new_image_size: Tuple[int, int]) -> List[List[int]]:
...     old_im_w, old_im_h = old_image_size
...     new_im_w, new_im_h = new_image_size
... 
...     return [[
...         max(min(int(x1 / old_im_w * new_im_w), new_im_w), 0),
...         max(min(int(y1 / old_im_h * new_im_h), new_im_h), 0),
...         max(min(int(x2 / old_im_w * new_im_w), new_im_w), 0),
...         max(min(int(y2 / old_im_h * new_im_h), new_im_h), 0)
...     ] for (x1, y1, x2, y2) in boxes]
>>> 
>>> item = synfintabs_dataset['test'][0]
>>> question_dict = next(question for question in item['questions']
...     if question['id'] == item['question_id'])
>>> encoding = tokenizer(
...     question_dict['question'].split(),
...     item['ocr_results']['words'],
...     max_length=512,
...     padding="max_length",
...     truncation="only_second",
...     is_split_into_words=True,
...     return_token_type_ids=True,
...     return_tensors="pt")
>>> 
>>> word_boxes = normalise_boxes(
...     item['ocr_results']['bboxes'],
...     item['image'].crop(item['bbox']).size,
...     (1000, 1000))
>>> token_boxes = []
>>> 
>>> for i, s, w in zip(
...         encoding['input_ids'][0],
...         encoding.sequence_ids(0),
...         encoding.word_ids(0)):
...     if s == 1:
...         token_boxes.append(word_boxes[w])
...     elif i == tokenizer.sep_token_id:
...         token_boxes.append([1000] * 4)
...     else:
...         token_boxes.append([0] * 4)
>>> 
>>> encoding['bbox'] = torch.tensor([token_boxes])
>>> outputs = model(**encoding)
>>> start = encoding.word_ids(0)[outputs['start_logits'].argmax(-1)]
>>> end = encoding.word_ids(0)[outputs['end_logits'].argmax(-1)]
>>> 
>>> print(f"Target: {question_dict['answer']}")
Target: 6,980
>>> 
>>> print(f"Prediction: {' '.join(item['ocr_results']['words'][start : end])}")
Prediction: 6,980
```

## 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}
}
```