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 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.
+In the Chinese Fineweb edu v2 version, the data selection scoring model has undergone a significant upgrade, utilizing the larger and more powerful OpenCSG csg-wukong-enterprise V2 model. The training data for this model has been increased to 1 million entries, covering a variety of text types such as books, news, blogs, and 25% English data. Compared to the previous version, the csg-wukong-enterprise V2 model boasts a larger parameter count and deeper semantic understanding, excelling particularly in Chinese text comprehension and processing. The model not only performs more detailed analysis of text structure and content but also captures deeper semantic and emotional nuances embedded in 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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-## Increased Training Data Size and Content Diversity
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-The size and diversity of the training data are key factors in influencing the performance of pretrained models. In Chinese Fineweb Edu v2, the scale of the training data has been significantly expanded to 1.88 million high-quality entries. This includes various types of Chinese texts such as books, news, and blogs, and introduces more representative domains, covering topics like education, technology, history, culture, and current affairs.
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-Moreover, Fineweb2 enhances the model’s cross-linguistic understanding by incorporating 25% English data. This not only increases the dataset's diversity but also equips the model to handle not just Chinese content but also cross-linguistic tasks. This establishes a strong foundation for future NLP tasks in mixed Chinese and English contexts and provides extensive training resources to enhance the model's multilingual capabilities.
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-By introducing a variety of data types and languages, Fineweb2 not only improves the model's performance in Chinese settings but also expands its potential for global applications, showcasing its powerful capabilities in multilingual tasks.
+This improvement means that during the data selection process, the model can more accurately assess the educational value, writing quality, and practical application of the text. Especially when dealing with high-demand texts in education and technology, the Fineweb2 scoring model ensures high quality and consistency in the selection results. This advancement significantly enhances the reliability of the data selection, providing stronger support for subsequent model training.
 
 
 # Prompt Improvements
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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**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。
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-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。
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-## 训练数据规模和内容多样性提升
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-数据集的规模和内容的多样性是影响预训练模型表现的关键因素之一。在Chinese Fineweb edu v2中，训练数据的规模从之前的版本显著扩展到了188万条高质量数据。这不仅包括了**书籍、新闻、博客**等多种类型的中文文本，还引入了更多具有代表性的领域，涵盖了**教育、科技、历史、文化、时事**等多种主题。
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-更值得一提的是，Fineweb2为了提升模型的跨语言理解能力，**包含了25%的英��数据**。这不仅增强了数据集的多样性，使得模型不仅能处理中文内容，还具备了跨语言的适应性。这为未来中文和英文混合场景中的自然语言处理任务打下了坚实的基础，也为模型的多语言处理能力提供了广泛的训练资源。
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-通过引入多元化的数据类型和语言，Fineweb2不仅提升了模型在中文环境中的表现，还拓展了它在全球应用中的潜力，使其在多语言任务中展现出更为强大的能力。
+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OpenCSG csg-wukong-enterprise V2模型。该模型的训练数据增加到100万条，涵盖了多种类型的文本，如书籍、新闻、博客，以及25%的英文数据。相比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。
 
 
 ## Prompt改进