Instructions to use bugsiesegal/form-field-labeling-florence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bugsiesegal/form-field-labeling-florence with PEFT:
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- Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
Formeze form-field labeler — LoRA adapter (lora-ocr-cleangt-v1e3, 2026-08)
Open-vocabulary form-field labeling adapter for the Formeze PDF pipeline, trained on the teacher-relabeled dataset revision (clean ground truth).
Recipe
- Base:
microsoft/Florence-2-base@5ca5edf5bd017b9919c05d08aebef5e4c7ac3bac - LoRA r=64, alpha=128, dropout 0.05, targets q/k/v/o_proj + fc1/fc2, PEFT 0.18.0
- Prompts:
<REGION_TO_DESCRIPTION>+ region tokens + k=12 nearest OCR tokens - Data:
bugsiesegal/form-fields-for-layout-labeled-pages@b2d482e0…(Gemini-3.5-flash teacher relabel, verbatim-caption style, degenerate labels filtered; includes 9 real authentication-form templates), template-grouped train split, 2 epochs + 1 continuation epoch (fresh-optimizer, initialized from the epoch-2 adapter — checkpoint resume is blocked on torch 2.5.1 by transformers' CVE-2025-32434 guard) - The immutable test split was never used for training or model selection.
Validation metrics (template-grouped validation split, 759 images, INT8 T4)
| metric | value | policy floor |
|---|---|---|
| classification semantic accuracy | 0.6048 | ≥ 0.60 |
| end-to-end semantic accuracy | 0.5202 | ≥ 0.45 |
| identity / financial / medical label acc | 0.658 / 0.661 / 0.653 | ≥ 0.60 each |
| authentication label acc (reported slice) | 0.867 (24 GT) | non-gating |
| detector ECE (Platt-calibrated) | 0.0100 | ≤ 0.12 |
Review-aid use only: labels are proposals requiring explicit user approval;
no calibrated label confidence exists. See the Formeze model card and
model-manifest.json for the confidence contract and release gating.
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Base model
microsoft/Florence-2-base
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