vab46/Clinical_trials_anchor-contextORpositive-ground-truth_LLM_LORA_ft
Dataset details:- This dataset is basically mapping of final anchor-positive pair data with their refernce answer. The given input data considered because:- (i) it had the had purest anchor-positive pairs with semantically bound anchors with context/positive. (ii) gave us the best result on final embedding fine tuning model. The anchor-context(positive)-reference_answer data has been generated via Qwen-2.5-7B teacher model with temperature 0.1 and a strict system prompt.… See the full description on the dataset page: https://huggingface.co/datasets/vab46/Clinical_trials_anchor-contextORpositive-ground-truth_LLM_LORA_ft.
Dataset details:-
- This dataset is basically mapping of **final anchor-positive pair data** with their refernce answer.
- The given input data considered because:-
(i) it had the had purest anchor-positive pairs with semantically bound anchors with context/positive.
(ii) gave us the best result on **final embedding fine tuning model**.
- The anchor-context(positive)-reference_answer data has been generated via Qwen-2.5-7B teacher model with temperature 0.1 and a strict system prompt.
- Each existing anchor-context has one to one mapping with reference_answers. These were howvere generated in chunk where 4 anchors mapped with a positive(in final data) were together feeded to teacher model to get 4 corresponding reference answers.
- The dataset can be used to fine tune LLMs for a particular niche domain. For instance we have used it to fine tune our **candidate model/generator for RAG CT pipeline** given (ranked)retieved chunks and query. Further it can be used for individual answer generation given just query.