rafmacalaba/data-use-mention-sft
Data-mention extraction SFT Single-task ChatML messages dataset for data-mention extraction, built from the GLiNER2 labels in rafmacalaba/data-use-mentions. Static instructions live in the system message; dynamic text in the user message; the assistant emits compact JSON {"data_mentions":[{"data_mention":"<span>","specificity_type":"named|descriptive|vague"}]} (or {"data_mentions":[]} when none qualify). Each row also carries corpus (prwp or fcv) and origin (e.g. general_prwp… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-mention-sft.
Data-mention extraction SFT
Single-task ChatML messages dataset for data-mention extraction, built from the GLiNER2 labels in rafmacalaba/data-use-mentions. Static instructions live in the system message; dynamic text in the user message; the assistant emits compact JSON {"data_mentions":[{"data_mention":"<span>","specificity_type":"named|descriptive|vague"}]} (or {"data_mentions":[]} when none qualify).
Each row also carries corpus (prwp or fcv) and origin (e.g. general_prwp, fcv_pads_east_asia, jdc_operational, refugee_pads, reliefweb) for corpus/origin-filtered training. Filter with finetune_lfm2_data_mention.py --corpus prwp (or --origin ...).
{"train": 59970, "val": 12325, "holdout": 12531}
