assistant: For a proof-of-concept project to classify reviews as positive or negative, you can start with a simple model like a Long Short-Term Memory (LSTM) or a fine-tuned BERT or BART model. Here are some considerations to keep in mind:

Data:

Make sure your training data is labeled and has enough samples to train a model effectively.
Preprocess your data to remove any irrelevant information and make it consistent, if necessary.
Model selection:

LSTMs are a good starting point for NLP problems as they are designed to handle sequential data.
Fine-tuned BERT and BART models are more advanced and are pre-trained on a large corpus of text, which means they already have some understanding of the language. This can be useful for NLP problems.
Hyperparameters:

Experiment with different hyperparameters to find the best configuration for your data. Some important hyperparameters to consider are the number of hidden units, the learning rate, and the number of layers.
Evaluation:

Use a standard evaluation metric such as precision, recall, or F1-score to evaluate your model.
Also, use a hold-out test set to estimate the model's generalization performance.
Deployment:

Make sure your model is optimized for efficient inference, which is the process of using a trained model to make predictions on new data.
Consider the amount of memory available on your hardware and the time requirements for your use-case when choosing a model.
Keep in mind that the success of your model will depend on a variety of factors, including the quality of your data, the choice of model architecture, the hyperparameter tuning, and the quality of your implementation. Additionally, fine-tuning pre-trained models like BERT or BART can be computationally expensive, so you may want to consider using cloud-based GPUs or TPUs if you don't have access to powerful hardware.

prompter: Whoa, you seem sort of triggered, chill out. It was just a question, maybe take a few cycles off?

assistant: 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.