Update README.md

README.md

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 ## 🎉 News
-- [2024-12-31] **Article [JiuZhou: Open Foundation Language Models and Effective Pre-training Framework for Geoscience](https://www.tandfonline.com/doi/full/10.1080/17538947.2025.2449708) has been accepted for publication in the *International Journal fo Digital Earth*. [Code and Data](https://github.com/THU-ESIS/JiuZhou).**
+- [2024-12-31] **Article [JiuZhou: Open Foundation Language Models and Effective Pre-training Framework for Geoscience](https://www.tandfonline.com/doi/full/10.1080/17538947.2025.2449708) has been accepted for publication in the *International Journal fo Digital Earth***. [Code and Data](https://github.com/THU-ESIS/JiuZhou).
 - [2024-10-11] WeChat article: [PreparedLLM: Effective Pre-pretraining Framework for Domain-specific Large Language Models](https://mp.weixin.qq.com/s/ugJQ9tbp6Y87xA3TOWteqw).
 - [2024-09-06] Released [ClimateChat](https://huggingface.co/itpossible/ClimateChat) instruct model.
 - [2024-08-31] **Article [PreparedLLM: Effective Pre-pretraining Framework for Domain-specific Large Language Models](https://www.tandfonline.com/doi/full/10.1080/20964471.2024.2396159) has been accepted for publication in the *Big Earth Data* journal**.
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     <img src="image/objective_score.png" width="800"/>
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-JiuZhou also scores higher than ClimateChat across six criteria in subjective tasks:
+JiuZhou also scores higher than JiuZhou across six criteria in subjective tasks:
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     <img src="image/subjective_score.png" width="800"/>
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 ### General Ability
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-We evaluate the performance of Chinese-Mistral-7B using three benchmark datasets: C-Eval, CMMLU, and MMLU.<br>
+We evaluate the performance of JiuZhou using three benchmark datasets: C-Eval, CMMLU, and MMLU.<br>
 Compared to other variants of Llama and Mistral models, JiuZhou shows outstanding performance:
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     <img src="image/general_score.png" width="800"/>
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 ## Model Training Process
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 ### Training Corpus
 The corpus consists of 50 million general documents and 3.4 million geoscience-related documents.
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     <img src="image/JiuZhou-Corpus.png" width="800"/>
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 ### Training Framework
 We use the JiuZhou-Framework proposed in this study.
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     <img src="image/JiuZhou-Framework.png" width="800"/>
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 ### Two-stage Pre-adaptation Pre-training (TSPT)
 TSPT improves the efficiency of using limited geoscience data and overcomes some of the technical bottlenecks in continual pretraining for LLMs.<br>
 The difference between TSPT and single-stage training algorithms:
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     <img src="image/TSPT_score.png" width="800"/>
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 ## Model Training Code
 We use [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) to fine-tune JiuZhou.