What specifically are you asking me to do? Are you asking me to design a model architecture or are you asking me for a concrete implementation? If it's the latter, which framework are we talking about and in which format is the data available? Regardless of the previous answer I will also need to know whether the data is raw text or tokens and which hardware is available since the maximum amount of VRAM determines the maximum size of the model that can be trained. What do the two classes in the dataset represent? You are asking for the precision metric, should the recall metric also be added? What is the hardware that will be used for inference and roughly how long should inference take? Furthermore, you are saying that training speed "does not matter" but this is almost certainly false; even with generous time limits there is still a limit.

In any case, since this is a binary classification problem the loss function that should be used is the binary cross entropy. You should also divide your dataset into three subsets: a training dataset on which you train your model, a validation dataset on which you fine-tune your hyperparameters, and a test dataset which you use to judge the model's performance after training. Be aware that you cannot realistically expect a large language model like myself (or a human) to give you a set of hyperparameters that will work well out-of-the-box; you will need to fine-tune the hyperparameters yourself and figure out a configuration that works well for your particular use-case.
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