Mixqg Base

🚀 MixQG（基础大小模型）

MixQG是一种全新的问题生成模型，它在包含多种答案类型的问答数据集集合上进行了预训练。该模型在论文MixQG: Neural Question Generation with Mixed Answer Types中被提出，相关代码发布在这个仓库中。

🚀 快速开始

MixQG是一个预训练的问题生成模型，可用于基于给定的上下文和答案生成问题。

💻 使用示例

基础用法

使用Huggingface的pipeline抽象：

from transformers import pipeline

nlp = pipeline("text2text-generation", model='Salesforce/mixqg-base', tokenizer='Salesforce/mixqg-base')
    
CONTEXT = "In the late 17th century, Robert Boyle proved that air is necessary for combustion."
ANSWER = "Robert Boyle"

def format_inputs(context: str, answer: str):
    return f"{answer} \\n {context}"

text = format_inputs(CONTEXT, ANSWER)

nlp(text)
# should output [{'generated_text': 'Who proved that air is necessary for combustion?'}]

高级用法

直接使用预训练模型：

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained('Salesforce/mixqg-base')
model = AutoModelForSeq2SeqLM.from_pretrained('Salesforce/mixqg-base')

CONTEXT = "In the late 17th century, Robert Boyle proved that air is necessary for combustion."
ANSWER = "Robert Boyle"

def format_inputs(context: str, answer: str):
    return f"{answer} \\n {context}"

text = format_inputs(CONTEXT, ANSWER)

input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=32, num_beams=4)
output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(output)
# should output "Who proved that air is necessary for combustion?"

📚 详细文档

引用

@misc{murakhovska2021mixqg,
      title={MixQG: Neural Question Generation with Mixed Answer Types}, 
      author={Lidiya Murakhovs'ka and Chien-Sheng Wu and Tong Niu and Wenhao Liu and Caiming Xiong},
      year={2021},
      eprint={2110.08175},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

🔧 技术细节

伦理考量

此版本仅用于支持学术论文的研究目的。我们的模型、数据集和代码并非专门为所有下游用途而设计或评估。我们强烈建议用户在部署此模型之前，评估并解决与准确性、安全性和公平性相关的潜在问题。我们鼓励用户考虑人工智能的常见局限性，遵守适用法律，并在选择用例时采用最佳实践，特别是在错误或滥用可能对人们的生活、权利或安全产生重大影响的高风险场景中。有关用例的进一步指导，请参考我们的AUP和AI AUP。