Bntqa Mbart

🚀 BnTQA-mBart 孟加拉语表格问答模型

BnTQA - mBart 是一个用于孟加拉语表格问答的模型，它基于 Facebook 的 mBART - large - 50 模型，能够处理孟加拉语表格中的问答任务，为低资源语言的表格问答研究提供了有效的解决方案。

🚀 快速开始

本模型可用于孟加拉语表格问答任务，通过加载预训练模型和数据集，能够对表格中的问题进行回答。

✨ 主要特性

• 低资源语言支持：针对孟加拉语等低资源语言的表格问答任务进行优化。
• 大规模数据生成：采用全自动的大规模表格问答数据生成流程。
• 性能优越：在大规模数据集上训练的模型性能优于现有最先进的大语言模型。

📦 安装指南

文档未提供具体安装步骤，可参考相关依赖库的官方安装说明，如pandas、datasets、transformers等。

💻 使用示例

基础用法

import pandas as pd
from datasets import load_dataset
from transformers import MBartForConditionalGeneration
model = MBartForConditionalGeneration.from_pretrained("vaishali/BnTQA-mBart")
tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name, src_lang="bn_IN", tgt_lang="bn_IN")
forced_bos_id = forced_bos_token_id = tokenizer.lang_code_to_id["bn_IN"]


# linearize table
def process_header(headers: List):
  return "<কলাম> " + " | ".join(headers)

def process_row(row: List, row_index: int):
  en2bnDigits = {'0': '০',  '1': '১', '2': '২', '3': '৩', '4': '৪', '5': '৫', '6': '৬', '7': '৭', '8': '৮', '9': '৯', '.': '.'}
  row_str = ""
  row_cell_values = []
  for cell_value in row:
      if isinstance(cell_value, int) or isinstance(cell_value, float):
          cell_value = convert_engDigit_to_bengali(str(cell_value))
          row_cell_values.append(str(cell_value))
      else:
          row_cell_values.append(cell_value)
  row_str += " | ".join(row_cell_values)
  bn_row_index = []
  for c in str(row_index):
      bn_row_index.append(en2bnDigits[c])
  return "<রো " + "".join(bn_row_index) + "> " + row_str

def process_table(table_content: Dict):
  table_str = process_header(table_content["header"]) + " "
  for i, row_example in enumerate(table_content["rows"]):
      table_str += process_row(row_example, row_index=i + 1) + " "
  return table_str.strip()

# load the dataset
banglatableQA = load_dataset("vaishali/banglaTabQA")

for sample in banglatableQA['train']:
  question = sample['question']
  input_table = pd.read_json(sample['table'], orient='split')
  answer = pd.read_json(sample['answer'], orient='split')

  # create the input sequence: query + linearized input table
  table_content = {"header": list(input_table.columns)[1:], "rows": [list(row.values)[1:] for i, row in input_table.iterrows()]}
  linearized_inp_table = process_table(table_content)
  linearized_output_table = process_table({"name": None, "header": [translate_column(col) for col in list(answer.columns)], 
                             "rows": [list(row.values) for i, row in answer.iterrows()]})
  source = query + " " + linearized_inp_table
  target = linearized_output_table
  input = tokenizer(source,
                    return_tensors="pt",
                    padding="max_length",
                    truncation="longest_first",
                    max_length=1024,
                    add_special_tokens=True)

  with tokenizer.as_target_tokenizer():
    labels = tokenizer(target,
               return_tensors="pt",
               padding="max_length",
               truncation="longest_first",
               max_length=1024,
               add_special_tokens=True).input_ids

  # inference
  out = model.generate(input["input_ids"].to("cuda"), num_beams=5, return_dict_in_generate=True,
                                output_scores=True, max_length=1024)

📚 详细文档

模型信息

引用信息

@inproceedings{pal-etal-2024-table,
    title = "Table Question Answering for Low-resourced {I}ndic Languages",
    author = "Pal, Vaishali  and
      Kanoulas, Evangelos  and
      Yates, Andrew  and
      de Rijke, Maarten",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.5",
    pages = "75--92",
    abstract = "TableQA is the task of answering questions over tables of structured information, returning individual cells or tables as output. TableQA research has focused primarily on high-resource languages, leaving medium- and low-resource languages with little progress due to scarcity of annotated data and neural models. We address this gap by introducing a fully automatic large-scale tableQA data generation process for low-resource languages with limited budget. We incorporate our data generation method on two Indic languages, Bengali and Hindi, which have no tableQA datasets or models. TableQA models trained on our large-scale datasets outperform state-of-the-art LLMs. We further study the trained models on different aspects, including mathematical reasoning capabilities and zero-shot cross-lingual transfer. Our work is the first on low-resource tableQA focusing on scalable data generation and evaluation procedures. Our proposed data generation method can be applied to any low-resource language with a web presence. We release datasets, models, and code (https://github.com/kolk/Low-Resource-TableQA-Indic-languages).",
}

📄 许可证

本项目采用 MIT 许可证。