I am just trying to understand and an honest question: Are we getting a fine tuned model from the 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.
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.
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.