Update README.md

README.md

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 ## Enhanced Scoring Model
 
-In Chinese Fineweb Edu v2, the scoring model used for data filtering has been significantly upgraded to the Qwen2.5-14B model, which is larger in scale and more powerful than the model used in the previous version. Qwen2.5-14B boasts more parameters and deeper semantic understanding, especially excelling in the comprehension and processing of Chinese text. This model not only provides a more detailed analysis of the structure and content of the text but also captures deeper semantic and emotional nuances hidden within the language.
+In Chinese Fineweb Edu v2, the scoring model used for data filtering has been significantly upgraded to the csg-wukong-enterprise V2 model, which is larger in scale and more powerful than the model used in the previous version. csg-wukong-enterprise V2 boasts more parameters and deeper semantic understanding, especially excelling in the comprehension and processing of Chinese text. This model not only provides a more detailed analysis of the structure and content of the text but also captures deeper semantic and emotional nuances hidden within the language.
 
 This enhancement allows the model to more accurately assess the educational value, writing quality, and real-world application of the text during the filtering process. Particularly for high-demand texts like those in education and technology, the scoring model of Fineweb2 ensures high quality and consistency in the filtering results. This significant improvement boosts the reliability of data filtering, providing stronger support for subsequent model training.
 
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 ## 更强的打分模型
 
-在Chinese Fineweb edu v2版本中，数据筛选的打分模型进行了重大升级，采用了规模更大、性能更强的**Qwen2.5-14B**模型。相较于上一版本的打分模型，Qwen2.5-14B拥有**更大的参数量和更深层次的语义理解能力**，特别是在中文文本理解和处理方面表现出色。该模型不仅能对文本的结构、内容进行更细致的分析，还能有效捕捉隐藏在语言中的深层次语义和情感信息。
+在Chinese Fineweb edu v2版本中，数据筛选的打分模型进行了重大升级，采用了规模更大、性能更强的**csg-wukong-enterprise V2**模型。相较于上一版本的打分模型，csg-wukong-enterprise V2拥有**更大的参数量和更深层次的语义理解能力**，特别是在中文文本理解和处理方面表现出色。该模型不仅能对文本的结构、内容进行更细致的分析，还能有效捕捉隐藏在语言中的深层次语义和情感信息。
 
 这种提升意味着在数据筛选过程中，模型能够**更加精准地评估文本的教育价值、写作质量以及其对实际应用的价值**。尤其是在处理教育类、技术类等高要求的文本时，Fineweb2的打分模型确保了筛选结果的高质量和高一致性。这一进步显著提高了数据筛选的可靠性，为后续的模型训练提供了更有力的保障。
 
@@ -146,7 +146,7 @@ Usage of the Chinese Fineweb Edu dataset requires adherence to the OpenCSG Commu
 在审查这段网页摘录后：请简要地为您的评分进行合理的解释，最多不超过100字，最后以“教育得分：<分数>”的格式结束。请根据所列出的标准系统地赋予分数。
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-所有数据集合并后，样本的得分分布如下，通过Qwen2.5-14B模型对这些数据进行评分后，最终选取了**3分以上**的文本，总计达到**188M条数据**，约**420B tokens**。这些数据不仅数量庞大，且经过了严格的筛选和去重处理，确保了数据集的**高质量和高独特性**。这些经过打分的数据将在Fineweb2的数据集中用于训练大规模语言模型，帮助其在各类任务中实现更高的性能表现。
+所有数据集合并后，样本的得分分布如下，通过csg-wukong-enterprise V2模型对这些数据进行评分后，最终选取了**3分以上**的文本，总计达到**188M条数据**，约**420B tokens**。这些数据不仅数量庞大，且经过了严格的筛选和去重处理，确保了数据集的**高质量和高独特性**。这些经过打分的数据将在Fineweb2的数据集中用于训练大规模语言模型，帮助其在各类任务中实现更高的性能表现。
 
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 <img width="900px" alt="experiment" src="./distribution.png">