T5 End2end Question Generation

🚀 t5端到端问题生成

该模型是 t5-base 在squad数据集上的微调版本，用于根据上下文生成问题。它能帮助用户基于给定的文本内容自动生成相关问题，提升信息获取和处理的效率。

👉 如果你想了解如何微调t5模型以实现相同功能，可以参考这个 教程

示例

Context: "Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace."

Questions:
    Who created Python?,
    When was Python first released?
    What is Python's design philosophy?

评估集结果

该模型在评估集上取得了以下结果：

• 损失值：1.5691

🚀 快速开始

模型使用

from transformers import T5ForConditionalGeneration, T5TokenizerFast

hfmodel = T5ForConditionalGeneration.from_pretrained("ThomasSimonini/t5-end2end-question-generation")

text= "The abolition of feudal privileges by the National Constituent Assembly on 4 August 1789 and the Declaration \\nof the Rights of Man and of the Citizen (La Déclaration des Droits de l'Homme et du Citoyen), drafted by Lafayette \\nwith the help of Thomas Jefferson and adopted on 26 August, paved the way to a Constitutional Monarchy \\n(4 September 1791 – 21 September 1792). Despite these dramatic changes, life at the court continued, while the situation \\nin Paris was becoming critical because of bread shortages in September. On 5 October 1789, a crowd from Paris descended upon Versailles \\nand forced the royal family to move to the Tuileries Palace in Paris, where they lived under a form of house arrest under \\nthe watch of Lafayette's Garde Nationale, while the Comte de Provence and his wife were allowed to reside in the \\nPetit Luxembourg, where they remained until they went into exile on 20 June 1791."

def run_model(input_string, **generator_args):
  generator_args = {
  "max_length": 256,
  "num_beams": 4,
  "length_penalty": 1.5,
  "no_repeat_ngram_size": 3,
  "early_stopping": True,
  }
  input_string = "generate questions: " + input_string + " </s>"
  input_ids = tokenizer.encode(input_string, return_tensors="pt")
  res = hfmodel.generate(input_ids, **generator_args)
  output = tokenizer.batch_decode(res, skip_special_tokens=True)
  output = [item.split("<sep>") for item in output]
  return output
  
run_model(text)

=> [['When did the National Constituent Assembly abolish feudal privileges?',
  ' Who drafted the Declaration of the Rights of Man and of the Citizen?',
  ' When was the Constitutional Monarchy established?',
  ' What was the name of the Declaration that paved the way to a constitutional monarchy?',
  '']]

训练超参数

以下是训练过程中使用的超参数：

训练结果

框架版本

• Transformers 4.10.3
• Pytorch 1.9.0+cu102
• Datasets 1.12.1
• Tokenizers 0.10.3

📄 许可证

本项目采用Apache-2.0许可证。