pandalla/datatager_standard_finance_question

--- license: apache-2.0 --- --- license: apache-2.0 --- <p align="center"> <img src="https://raw.githubusercontent.com/PandaVT/DataTager/main/assert/datatager_logo_right.png" width="650" style="margin-bottom: 0.2;"/> <p> <h5 align="center"> If you like our project, please give us a star ⭐ </h2> <h4 align="center"> [<a href="https://github.com/PandaVT/DataTager">GitHub</a> | <a href="https://datatager.com/">DataTager Home</a>] # Standard Finance Question ## Prompt for Training When training your model with this dataset, prepend the following prompt to each input instance: ``` 将非标准或口语化的金融咨询转换为标准的、正式的语句。这一转换旨在清晰表达用户的咨询意图，同时提高语句的专业度和易理解性。 ``` ## Description AnyTaskTune is a publication by the DataTager team. We advocate for rapid training of large models suitable for specific business scenarios through task-specific fine-tuning. We have open-sourced several datasets across various domains such as legal, medical, education, and HR, and this dataset is one of them. This dataset, titled "Standard Finance Question," is crucial in improving the efficiency of financial services by ensuring that queries are precisely articulated. The standardization process simplifies the understanding of complex financial queries and facilitates faster and more accurate responses from financial institutions. ## Usage The "Standard Finance Question" dataset is particularly valuable for training AI systems aimed at processing financial dialogues. By converting non-standard financial expressions into standardized queries, these AI models can assist in automating parts of the initial customer inquiry process. This not only reduces the time financial professionals spend in understanding client issues but also enhances the accuracy and relevance of the financial advice provided. Additionally, the dataset can be used in educational settings to train financial professionals on interpreting and reformulating customer questions. ## Citation Please cite this dataset in your work as follows: ``` @misc{ Extract Medical Information Dataset, author = {DataTager}, title = {Extract Medical Information Dataset}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\url{https://github.com/PandaVT/DataTager}} } ```--- license: apache-2.0 --- --- license: apache-2.0 --- <p align="center"> <img src="https://raw.githubusercontent.com/PandaVT/DataTager/main/assert/datatager_logo_right.png" width="650" style="margin-bottom: 0.2;"/> <p> <h5 align="center"> 如果您喜爱本项目，请为我们点亮 ⭐ 星标 </h5> <h4 align="center"> [<a href="https://github.com/PandaVT/DataTager">GitHub</a> | <a href="https://datatager.com/">DataTager 主页</a>] # 标准金融咨询问题 ## 训练提示词 使用本数据集训练模型时，请在每个输入样本前添加以下提示词： 将非标准或口语化的金融咨询转换为标准的、正式的语句。这一转换旨在清晰表达用户的咨询意图，同时提高语句的专业度和易理解性。 ## 数据集说明 AnyTaskTune 是 DataTager 团队发布的一项研究成果。我们倡导通过针对特定任务的微调，快速训练适配特定业务场景的大语言模型（Large Language Model，LLM）。我们已在法律、医疗、教育、人力资源等多个领域开源了若干数据集，本数据集便是其中之一。 本数据集命名为“标准金融咨询问题”，其核心价值在于通过确保咨询问题表述精准，提升金融服务效率。标准化流程可简化对复杂金融咨询的理解，助力金融机构更快、更准确地作出响应。 ## 使用方法 “标准金融咨询问题”数据集对于训练用于处理金融对话的AI系统尤为宝贵。通过将非标准化的金融表述转换为标准化咨询，此类AI模型可助力自动化部分初始客户咨询流程。此举不仅能减少金融专业人员理解客户诉求的时间，还可提升所提供金融建议的准确性与相关性。此外，该数据集还可用于教育场景，帮助金融专业人员学习如何解读并重构客户问题。 ## 引用方式 请在您的研究工作中按以下方式引用本数据集： @misc{ Extract Medical Information Dataset, author = {DataTager}, title = {Extract Medical Information Dataset}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {url{https://github.com/PandaVT/DataTager}} }