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README.md

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+# COIG-Kun Label Model 
+
+## Model Details
+- **Name:** Label Model
+- **Release Date:** 2023.12.04
+- **Github URL:** [Label Model on Huggingface](https://github.com/Zheng0428/COIG-Kun)
+- **Developers:** Tianyu Zheng*, Shuyue Guo*, Xingwei Qu, Xinrun Du, Wenhu Chen, Jie Fu, Wenhao Huang, Ge Zhang
+
+## Model Description
+The Label Model is a part of the Kun project, which aims to enhance language model training through a novel data augmentation paradigm, leveraging principles of self-alignment and instruction backtranslation. The model is specifically fine-tuned to generate high-quality instructional data, a critical component in the project's approach to data augmentation and language model training.
+
+## Intended Use
+- **Primary Use:** The Label Model is designed for generating instructional data to fine-tune language models.
+- **Target Users:** Researchers and developers in NLP and ML, particularly those working on language model training and data augmentation.
+
+## Training Data
+The Label Model is trained using approximately ten thousand high-quality seed instructions. These instructions were meticulously curated to ensure the effectiveness of the training process and to produce high-quality outputs for use as instructional data.
+
+## Training Process
+- **Base Model:** Yi-34B
+- **Epochs:** 6
+- **Learning Rate:** 1e-5
+- **Fine-Tuning Method:** The model was fine-tuned on high-quality seed instructions, with the responses to these instructions used as outputs and the instructions themselves as inputs.
+
+## Evaluation
+The Label Model was evaluated on its ability to generate high-quality instructional data, focusing on the relevancy, clarity, and usability of the instructions for language model training.
+
+## Limitations
+- The Label Model is optimized for Chinese and English instructional data generation.
+- The effectiveness of the model may vary based on the quality of the input seed data.
+
+## Ethical Considerations
+- Users should be aware of potential biases in the training data, which could be reflected in the model's outputs.
+- The model should not be used for generating harmful or misleading content.
+
+## Citing the Model
+To cite the Label Model in academic work, please use the following reference:
+
+```bibtex
+@misc{COIG-Kun,
+  title={Kun: Answer Polishment Saves Your Time for Using Intruction Backtranslation on Self-Alignment},
+  author={Tianyu, Zheng* and Shuyue, Guo* and Xingwei, Qu and Xinrun, Du and Wenhu, Chen and Jie, Fu and Wenhao, Huang and Ge, Zhang},
+  year={2023},
+  publisher={GitHub},
+  journal={GitHub repository},
+  howpublished={https://github.com/Zheng0428/COIG-Kun}
+}
+```
+