Update README.md

README.md

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 During the data selection process, the **Chinese Fineweb Edu** dataset adopted a strategy similar to that of Fineweb-Edu, with a focus on the educational value and content quality of the data. The specific selection steps are as follows:
 
-1. **Educational Value Assessment**: Initially, the qwen2-7b-Instruct model was used to evaluate the educational value of the samples. The model provided a score ranging from 0 to 5 based on the relevance and quality of the content. In the preliminary selection phase, we selected approximately 100,000 high-scoring samples.
+1. **Educational Value Assessment**: Initially, the csg-wukong-enterprise scoring model was used to evaluate the educational value of the samples. The model provided a score ranging from 0 to 5 based on the relevance and quality of the content. In the preliminary selection phase, we selected approximately 100,000 high-scoring samples.
 2. **Scoring Model Training**: Using these 100,000 samples, a BERT model was trained to score a larger pre-training dataset. This step ensured that the model could effectively identify content with high educational value.
 3. **Data Selection**: Next, the trained BERT model was used to comprehensively score the raw data, retaining only data with a score greater than 4. This selection process significantly enhanced the quality and relevance of the dataset, ensuring its applicability in the educational domain.
 4. **MinHash Deduplication**: To avoid the negative impact of duplicate content on model training, the dataset was deduplicated using the MinHash algorithm. This method ensured the uniqueness of the data while preserving a diverse range of educational content.