库名称：transformers
任务标签：表格问答
许可证：mit
数据集：

• ethanbradley/synfintabs
  语言：
• en
  基础模型：
• microsoft/layoutlm-base-uncased

FinTabQA：金融表格问答系统

一个基于LayoutLM架构的金融表格问答模型。

快速开始

与使用其他Hugging Face Transformer模型和分词器类似，加载FinTabQA和一个快速分词器即可开始使用。以下是一个使用SynFinTabs数据集的最小工作示例。

>>> 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  

引用

如果您使用此模型，请同时引用以下文章和模型本身。

@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}  
}