A suitable model for binary classification on the Amazon reviews dataset could be a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model. Given the large number of training samples (1.8 million) and the longest sequence length of 258, pre-training the BERT model on a similar task before fine-tuning it on the Amazon reviews data can lead to improved performance. Since inference speed is a priority, using a lighter version of BERT such as DistilBERT or utilizing quantization techniques can help make the model more computationally efficient. To evaluate the model's performance, metrics such as accuracy, precision, and AUC can be used.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence