This approach involves the creation of a dataset comprising pairs of queries and their relevant chunks (code snippets or documentation), which serve as the foundation for training a Cross-Encoder model like BERT for relevance scoring. The aim is to enhance the accuracy of code generation tasks by leveraging these relevance scores.
- Data Preparation: Construct pairs of queries and corresponding relevant chunks. These chunks can be code snippets, documentation excerpts, or any other relevant textual content.
- Dataset Role: These pairs form the dataset on which the Cross-Encoder model will be trained. The dataset is designed to capture the relevance of each chunk to its associated query.