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.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence