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.
So just to be clear, you aren't building _deep_ learning models, or even NN-based models automatically?
Sorry I think I explained poorly. Plexe does build deep learning models automatically. When it gets a dataset and a problem description, it automatically evaluates various model architectures (NNs being one of them).
Plexe experiments with multiple approaches - from traditional algorithms like gradient boosting to deep neural networks. It runs the training jobs and compares performance metrics across different architectures to identify which solution best fits your specific data and problem constraints.
Oh okay! In that case, my faith is restored. Sounds like a cool project.
No, not by default. In fact, the default installation of plexe doesn't include deep learning libraries.
Plexe _can_ build deep learning models using `torch` and `transformers`, and often the experimentation process will include some NN-based solutions as well, but that's just one of the ML frameworks available to the agent. It can also build models using xgboost, scikit-learn, and several others.
You can also explicitly tell Plexe not to use neural nets, if that's a requirement.