Datasets:
| license: cc-by-4.0 | |
| tags: | |
| - instruction-following | |
| - sft | |
| - data-mentions | |
| - extraction | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: train.jsonl | |
| - split: val | |
| path: val.jsonl | |
| - split: holdout | |
| path: holdout.jsonl | |
| # 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}` | |