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rafmacalaba/
lfm2.5-350M-datause-multitask

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lfm2.5-350M-datause-multitask

LoRA SFT of LiquidAI/LFM2.5-350M for data-mention provenance attributes (producer / year / geography / acronym) and usage/impact classification (data_type / usage_action / impact_label / usage_summary).

Training

base model: LiquidAI/LFM2.5-350M
dataset: rafmacalaba/data-use-sft
epochs: 3
learning rate: 0.0002
LoRA: r=16 alpha=32 dropout=0.05
completion-only masking (loss on assistant JSON turn)

real holdout

Holdout n=5471. Exact string match of each emitted attribute against the gold label.

attribute	tp	fp	fn	precision	recall	f0.5	f1
producer	1093	393	356	0.7355	0.7543	0.7392	0.7448
year	1034	278	316	0.7881	0.7659	0.7836	0.7769
geography	1612	476	415	0.7720	0.7953	0.7766	0.7835
acronym	1238	217	178	0.8509	0.8743	0.8554	0.8624
overall	4977	1364	1265	0.7849	0.7973	0.7874	0.7911

Usage/impact macro-F1 (per head):

data_type: 0.5934
usage_action: 0.5131
impact_label: 0.4816
usage_summary: mean_sim=0.6700 grounded_rate=0.9264

Verbatim rate (emitted values that are substrings of the context): 6317/6341 = 0.9962

synthetic holdout

Holdout n=575. Exact string match of each emitted attribute against the gold label.

attribute	tp	fp	fn	precision	recall	f0.5	f1
producer	123	27	26	0.8200	0.8255	0.8211	0.8227
year	119	20	35	0.8561	0.7727	0.8380	0.8123
geography	165	36	29	0.8209	0.8505	0.8267	0.8354
acronym	62	26	27	0.7045	0.6966	0.7029	0.7006
overall	469	109	117	0.8114	0.8003	0.8092	0.8058