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rafmacalaba/lfm2.5-350M-datause-prwp
lfm2.5-350M-datause-prwp is a text generation model from rafmacalaba. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
LoRA SFT of LiquidAI/LFM2.5-350M for data-mention extraction: emit the data-bearing phrases in a text as compact JSON (datamentions with datamention/specificitytype).
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Updated Aug 21, 2026
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From the Hugging Face model README
LoRA SFT of LiquidAI/LFM2.5-350M for data-mention extraction: emit the
data-bearing phrases in a text as compact JSON
(data_mentions with data_mention/specificity_type).
LiquidAI/LFM2.5-350Mrafmacalaba/data-use-mention-sftJaccard entity-level matching: acronym-aware span clustering +
Hungarian optimal bipartite match (match thr=0.5), F0.5
primary. Aligned with rafmacalaba/gliner_datause_extended. The
holdout is rafmacalaba/data-use-mention-sft, so numbers are not
directly comparable to the GLiNER model's data-use-mentions-extended
holdout.
| label | tp | fp | fn | precision | recall | f0.5 | f1 |
|---|---|---|---|---|---|---|---|
| overall | 9888 | 2537 | 2414 | 0.7958 | 0.8038 | 0.7974 | 0.7998 |
| label | tp | fp | fn | precision | recall | f0.5 | f1 |
|---|---|---|---|---|---|---|---|
| descriptive | 4034 | 1930 | 1762 | 0.6764 | 0.6960 | 0.6802 | 0.6861 |
| named | 4183 | 1281 | 1175 | 0.7656 | 0.7807 | 0.7685 | 0.7731 |
| vague | 488 | 539 | 662 | 0.4752 | 0.4243 | 0.4641 | 0.4483 |
| label | tp | fp | fn | precision | recall | f0.5 | f1 |
|---|---|---|---|---|---|---|---|
| prwp | 9888 | 2537 | 2414 | 0.7958 | 0.8038 | 0.7974 | 0.7998 |
| label | tp | fp | fn | precision | recall | f0.5 | f1 |
|---|---|---|---|---|---|---|---|
| general_prwp | 9888 | 2537 | 2414 | 0.7958 | 0.8038 | 0.7974 | 0.7998 |
| gold | predicted |
|---|---|
| {"data_mentions":[{"data_mention":"administrative data","specificity_type":"descriptive"}]} | {"data_mentions":[{"data_mention":"administrative data","specificity_type":"descriptive"}]} |
| {"data_mentions":[{"data_mention":"World Development Indicators","specificity_type":"named"},{"data_mention":"CPHS","spe | {"data_mentions":[{"data_mention":"World Development Indicators","specificity_type":"named"},{"data_mention":"CPHS","spe |
| {"data_mentions":[{"data_mention":"ES data","specificity_type":"named"}]} | {"data_mentions":[{"data_mention":"ES data","specificity_type":"named"}]} |
| {"data_mentions":[]} | {"data_mentions":[{"data_mention":"EUROMOD I4.0+","specificity_type":"named"}]} |
| {"data_mentions":[{"data_mention":"World Development Indicators","specificity_type":"named"},{"data_mention":"IMF World | {"data_mentions":[{"data_mention":"World Development Indicators","specificity_type":"named"},{"data_mention":"IMF World |
| {"data_mentions":[]} | {"data_mentions":[]} |
| {"data_mentions":[{"data_mention":"UNESCO-provided data","specificity_type":"descriptive"}]} | {"data_mentions":[{"data_mention":"PIRLS","specificity_type":"named"},{"data_mention":"UNESCO-provided data","specificit |
| {"data_mentions":[{"data_mention":"IHPS dataset","specificity_type":"named"},{"data_mention":"IHS4 data","specificity_ty | {"data_mentions":[{"data_mention":"IHS4 dataset","specificity_type":"named"},{"data_mention":"IHS4 data","specificity_ty |
| {"data_mentions":[{"data_mention":"HFSSS","specificity_type":"named"}]} | {"data_mentions":[{"data_mention":"HFSSS","specificity_type":"named"}]} |
| {"data_mentions":[{"data_mention":"HFCS data","specificity_type":"named"}]} | {"data_mentions":[{"data_mention":"HFCS data","specificity_type":"named"}]} |
| {"data_mentions":[{"data_mention":"data on patents and publications","specificity_type":"descriptive"}]} | {"data_mentions":[{"data_mention":"data on patents and publications","specificity_type":"descriptive"}]} |
| {"data_mentions":[{"data_mention":"UN COMTRADE Statistics","specificity_type":"named"}]} | {"data_mentions":[{"data_mention":"UN COMTRADE Statistics","specificity_type":"named"}]} |