In this study, we utilized the "transformers" library developed by the Hugging Face community in conjunction with the PyTorch deep learning framework to perform our natural language processing tasks. All models are trained on an Nvidia RTX 4090 with a 9:1 trainvalidation split. Early stopping is applied with a patience of 200 epochs. The learning rate is set to 1e-5, and a batch size of 32 is used for the pre-trained BERT-base-cased model, which is employed for regression tasks by adding a regression head. Specifically, we append a linear layer to the BERT architecture, which maps the hidden representation of the [CLS] (located at the first position of the sequence) token to a single regression output. Given that BERT-base-cased has a hidden size of 768, the linear layer has dimensions of 768 to 1. The name "BERT-base-cased" refers to the model's size, with 12 transformer encoder blocks and 110 million parameters, in contrast to the larger BERT-large model (340 million parameters), and is case-sensitive.