This is a BERT model fine-tuned on a high-quality English financial dataset. It generates a financial relevance score for each piece of text, and based on this score, different quality financial data can be filtered by strategically setting thresholds. For the complete data cleaning process, please refer to YiZhao.

To collect training samples, we use the Qwen-72B model to thoroughly annotate small batches of samples extracted from English datasets, and scored them from 0 to 5 based on financial relevance. Given the uneven class distribution in the labeled samples, we apply undersampling techniques to ensure class balance. As a result, the final English training dataset contains nearly 50,000 samples. During the training process, we fix the embedding layer and encoder layer, and save the model parameters that achieve optimal performance based on the F1 score.

Here is an example code snippet for generating financial relevance scores using this model.