lfm2.5-Encoder-350M-datause-extended

Fine-tune of LiquidAI/LFM2.5-Encoder-350M for BIO data-use mention tagging (dataset / survey / census / registry mentions in economics research papers).

Labels

• NAMED_DATA — a proper name, title, or acronym of a specific data source
• DESCRIPTIVE_DATA — a source described in words but not named
• VAGUE_DATA — generic data wording with no identifiable source

Training

• base model: LiquidAI/LFM2.5-Encoder-350M
• dataset: rafmacalaba/data-use-mentions-extended (bio config)
• epochs: 5
• learning rate: 2e-05
• batch size: 16
• precision: bf16

Evaluation (holdout, label-agnostic)

thr	tp	fp	fn	precision	recall	f0.5	f1
0.10	7803	1715	4761	0.8198	0.6211	0.7705	0.7067
0.20	7803	1715	4761	0.8198	0.6211	0.7705	0.7067
0.30	7803	1715	4761	0.8198	0.6211	0.7705	0.7067
0.40	7787	1702	4777	0.8206	0.6198	0.7707	0.7062
0.50	7658	1625	4906	0.8249	0.6095	0.7705	0.7011
0.60	7083	1304	5481	0.8445	0.5638	0.7680	0.6761
0.70	6560	1057	6004	0.8612	0.5221	0.7622	0.6501

Best F0.5: 0.7707 (thr=0.4) Best F1: 0.7067 (thr=0.1)