The structTuningNEAR dataset is a subset of the original nearData dataset, specially prepared for the structure-aware finetuning of a pre-trained LLM. 

The Structure-Aware Finetuning approach instructs the model with dApps trees and their corresponding readme files. It aim to give the model a good knowledge of the whole dApp logic so that when a user asks it to create an app, the model will primarily provide an output focused on the big-picture structure and its description. The goal of Structure-Aware Finetuning is to bypass the limited logic of the 'next-token prediction', which sometimes spins the model in 'dumb loops' while iterating over complex coding challenges. Structure-aware code LLMs should also be of great use for code understanding and code discussion.

The structTuningNEAR dataset is made of: 
- nearDappsTrees: 3414 text files representing the tree structure extracted from the nearDapps files. 
- nearDappsReadme: 23166 readme files extracted in text formats from the nearDapps files.