I apologize if my previous response seemed abrupt. As an AI language model, I do not have feelings, but I understand that the tone of my response may have seemed abrupt or intense.

To answer your original question, for the binary classification of customer reviews, you can consider using either an RNN, LSTM, or fine-tuned BERT or BART. The choice of model will depend on several factors such as the size of your training data, the computational resources available, and your desired inference speed.

In terms of the data format, you need to determine whether the reviews are available as raw text or in some other format such as tokenized sequences. The hardware available will also be a key consideration, as this will determine the size of the model you can train.

When building the model, you should divide your data into a training set, a validation set, and a test set. The binary cross entropy loss function should be used. You should also keep in mind that finding a set of hyperparameters that will work well out-of-the-box may not be realistic and you may need to fine-tune the hyperparameters yourself to obtain the best performance for your particular use case.

It is also advisable to include both precision and recall metrics when evaluating the model, as these metrics provide complementary information about the performance of the model. The hardware used for inference and the desired inference speed will also be important considerations.
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