Plexe analyzes your data and task description, then builds custom ML models using standard Python libraries (like scikit-learn, XGBoost, etc.). If your problem is best solved by a regression model, it will build that. If classification is more appropriate, it will implement that instead.
Fine-tuning existing language models is also an option in Plexe's toolkit. For example, when we needed to classify prompt injections for LLMs, Plexe determined fine-tuning RoBERTa was the best approach. But for most structured data problems (like forecasting or recommendations), Plexe typically builds lightweight models from scratch that are trained directly on your dataset.
Fine-tuning existing language models is also an option in Plexe's toolkit. For example, when we needed to classify prompt injections for LLMs, Plexe determined fine-tuning RoBERTa was the best approach. But for most structured data problems (like forecasting or recommendations), Plexe typically builds lightweight models from scratch that are trained directly on your dataset.