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license: apache-2.0
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license: apache-2.0
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license: apache-2.0
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<p align="center">
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<img src="https://raw.githubusercontent.com/PandaVT/DataTager/main/assert/datatager_logo_right.png" width="650" style="margin-bottom: 0.2;"/>
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<p>
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<h5 align="center"> If you like our project, please give us a star ⭐ </h2>
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<h4 align="center"> [<a href="https://github.com/PandaVT/DataTager">GitHub</a> | <a href="https://datatager.com/">DataTager Home</a>]
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# Legal Question Enhancement Dataset
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## Description
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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.
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The Legal Question Enhancement dataset is a collection organized by DataTager to improve communication between lawyers and clients. This dataset helps identify gaps in client consultations and generates targeted questions, prompting clients to provide more detailed information about their cases. By refining clients' responses, this dataset can help lawyers quickly understand and more effectively resolve clients' issues. This dataset not only enhances the efficiency of client consultations but also helps lawyers gather more valuable case details during the initial consultation stage, enabling the formulation of more accurate legal strategies.
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## Usage
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This dataset is an important resource for developing legal AI tools, significantly improving the quality of information exchange in legal consultations. AI systems can use this dataset to prompt clients to elaborate on their case details, background information, and other related legal issues, enabling lawyers to make more informed decisions quickly. It can also be used for educational purposes, training law students and junior lawyers to identify and inquire about key details in client interactions. By utilizing this dataset, AI systems can generate targeted questions, helping lawyers efficiently obtain detailed information from clients during the initial consultation stage.
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## Citation
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Please cite this dataset in your work as follows:
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```
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@misc{ Extract Medical Information Dataset,
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author = {DataTager},
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title = {Extract Medical Information Dataset},
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year = {2024},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {https://github.com/PandaVT/DataTager}
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}
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```
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