AdarshSingh7647/Eklav-Reranker-AnswerOnly-Data

Eklav-Reranker-AnswerOnly-Data Training data for the Eklav paper. Task: passage reranking (BRIGHT / NevIR benchmarks) Method: Answer-only (no reasoning of any kind -- the no-CoT floor) Examples: 381,934 train / 3,857 held-out val Format: ShareGPT (system + conversations: [{from, value}]), used for LoRA SFT via LLaMA-Factory. Single-turn ShareGPT conversations. Each row: a query+passage relevance-judgment prompt (human turn) and a bare true/false judgment (gpt turn) -- no hint… See the full description on the dataset page: https://huggingface.co/datasets/AdarshSingh7647/Eklav-Reranker-AnswerOnly-Data.