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README.md

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 ## Introduction
 We collected 55,183 unique license sentences from 3,238 open-source software (OSS) licenses.  From these, we randomly selected 19,883 sentences (36.03%) for joint annotation of license terms and attitudes, forming our supervised dataset. The remaining 35,300 license sentences constitute our unsupervised dataset. This unsupervised dataset is used exclusively for Domain-Adaptive Pre-Training (DAPT) to enhance the model's domain adaptability.  During the Supervised Fine-Tuning (SFT) stage, we randomly split the 19,883 annotated sentences from the supervised dataset into training, validation, and test sets at a ratio of 7:1:2, comprising 13,919, 1,988, and 3,976 license sentences, respectively.
 
+Based on this dataset, we developed [the first LLM dedicated to identifying OSS license terms](https://huggingface.co/AnonymousAuthors/License-Llama3-8B). [Our work has been accepted for publication in the proceedings of the 47th International Conference on Software Engineering(ICSE 2025).](https://conf.researchr.org/details/icse-2025/icse-2025-industry-challenge-track/6/Exploring-Large-Language-Models-for-Analyzing-Open-Source-License-Conflicts-How-Far-)
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 ## Definition of License Terms and Attitudes
 License terms refer to the formal and standardized descriptions of software usage conditions, with each term representing a type of action a user (licensee) may perform. We collected 27 common license terms, covering 14 rights and 13 obligations.