🚀 FinTabQA:金融表格問答模型
FinTabQA是一個用於金融表格問答的模型,它採用了LayoutLM架構,能夠高效處理金融表格相關的問答任務,為金融信息的查詢和分析提供了有力支持。
🚀 快速開始
若要開始使用FinTabQA,你可以像加載其他Hugging Face Transformer模型和分詞器一樣,加載該模型和快速分詞器。以下是一個使用SynFinTabs數據集的最小工作示例:
基礎用法
>>> 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
📄 許可證
本項目採用MIT許可證。
📚 引用
如果您使用了此模型,請同時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}
}
屬性 |
詳情 |
庫名稱 |
transformers |
任務類型 |
表格問答 |
許可證 |
MIT |
訓練數據集 |
ethanbradley/synfintabs |
語言 |
英文 |
基礎模型 |
microsoft/layoutlm-base-uncased |