Mark model card as gated (public listing)

b63c591 verified about 2 months ago

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language:
  - fr
license: mit
library_name: transformers
pipeline_tag: text-classification
base_model: microsoft/layoutlmv3-base
tags:
  - transformers
  - safetensors
  - layoutlmv3
  - text-classification
  - sequence-classification
  - document-layout-analysis
  - document-understanding
  - bulletin-officiel
  - french-government
  - sommaire
  - ocr
  - document-ai
  - private
datasets:
  - custom
metrics:
  - accuracy
  - f1
widget:
  - text: SOMMAIRE
    example_title: Sommaire banner
  - text: Dahir n° 1-23-45 portant promulgation
    example_title: Titre segment
model-index:
  - name: sommaire-layoutlmv3-classifier
    results:
      - task:
          type: text-classification
          name: Sommaire segment layout classification (9 classes)
        dataset:
          type: custom
          name: SommaireTOC BO segments (held-out test, n=1800)
        metrics:
          - type: f1
            name: F1 macro (test)
            value: 0.9738
          - type: accuracy
            name: Accuracy (test)
            value: 0.9867
          - type: f1
            name: F1 micro (test)
            value: 0.9867
          - type: f1
            name: F1 weighted (test)
            value: 0.9867
      - task:
          type: text-classification
          name: Validation (checkpoint selection)
        dataset:
          type: custom
          name: SommaireTOC BO segments (val)
        metrics:
          - type: f1
            name: F1 macro (val, best)
            value: 0.968
          - type: accuracy
            name: Accuracy (val, best)
            value: 0.9861
          - type: epoch
            name: Best epoch
            value: 15

SommaireTOC Segment Classifier — LayoutLMv3 (gated)

Fine-tuned microsoft/layoutlmv3-base for segment-level layout classification on French Bulletin Officiel (BO) SOMMAIRE / TOC pages.

Each annotated region (page crop image + OCR words + word boxes) is encoded as one sequence; the model predicts one of 9 layout classes (LayoutLMv3ForSequenceClassification).

Gated model — trained on proprietary/internal BO annotations. Best weights from run run_20260809_031930 (checkpoint-21990 / epoch 15).

Model description

Property	Value
Architecture	`LayoutLMv3ForSequenceClassification`
Base model	`microsoft/layoutlmv3-base`
Task	Segment-level sequence classification
Classes	9 SommaireTOC layout roles
Input	Page/region image + word tokens + word boxes (0–1000)
Parameters	~126M
Best checkpoint	`checkpoint-21990` (epoch 15)
Selection metric	`eval_f1_macro`

Layout classes (9)

id	label	Role
0	`Titre`	TOC title line
1	`Page_title`	Page number associated with a title
2	`Som_Section`	Section banner inside sommaire
3	`Sommaire`	SOMMAIRE header / banner
4	`Page`	Standalone page number
5	`Meta_Data`	Header/footer metadata
6	`Tex_PAR`	Text block — PARTICULIER
7	`Tex_GEN`	Text block — GENERAL
8	`Tex_AVIS`	Text block — AVIS

Training data

Item	Value
Segments (pipeline scale)	15,398
Corrected GT titles (ref)	3,697
Task	`sommaire_segment_classification`
Run id	`run_20260809_031930`
Domain	French Bulletin Officiel SOMMAIRE pages

See metrics/run_config.json.

Training hyperparameters

Parameter	Value
Epochs requested	30
Epochs to best	15 (early stopping patience 5; run stopped ~20)
Batch size (per device)	4
Gradient accumulation	2
Effective batch size	8
Learning rate	2e-5
Warmup ratio	0.1
Weight decay	0.01
FP16	true
Optimizer metric	`f1_macro`
Best val F1 macro	0.9680
Best global step	21990

Summaries: metrics/trainer_summary.json, metrics/val_best_metrics.json

Evaluation results

Held-out test set (n=1800)

From metrics/test_metrics.json:

Metric	Value
Accuracy	0.9867
F1 macro	0.9738
F1 micro	0.9867
F1 weighted	0.9867
Precision macro	0.9700
Recall macro	0.9790
Mean confidence	0.9971

Per-class F1 (test)

Class	Precision	Recall	F1	Support
Meta_Data	1.00	0.99	0.994	90
Page	0.94	0.95	0.945	100
Page_title	0.99	0.99	0.989	593
Som_Section	0.98	0.97	0.978	231
Sommaire	1.00	0.99	0.995	96
Tex_AVIS	0.88	1.00	0.933	7
Tex_GEN	1.00	0.92	0.960	26
Tex_PAR	0.95	1.00	0.974	38
Titre	1.00	0.99	0.994	619

Validation (best checkpoint)

Metric	Value
F1 macro	0.9680
Accuracy	0.9861
Epoch	15

Plots

Example pages

Usage

from transformers import AutoProcessor, LayoutLMv3ForSequenceClassification
import torch
from PIL import Image

repo = "AvoCahDoe/sommaire-layoutlmv3-classifier"
processor = AutoProcessor.from_pretrained(repo, apply_ocr=False)
model = LayoutLMv3ForSequenceClassification.from_pretrained(repo)
model.eval()

image = Image.open("region_crop.png").convert("RGB")
# words / boxes: list[str], list[list[int]] normalized 0–1000
encoding = processor(
    image,
    words=words,
    boxes=boxes,
    return_tensors="pt",
    truncation=True,
    padding="max_length",
    max_length=512,
)
with torch.no_grad():
    logits = model(**encoding).logits
pred_id = int(logits.argmax(-1).item())
print(model.config.id2label[pred_id])

Repository layout

model.safetensors, config.json
tokenizer / preprocessor files
metrics/   # test, val, run_config
plots/     # curves + confusion / comparisons
examples/  # BO page images

Intended use

Classification stage of SommaireTOC after region proposal
Labels feed title / page extraction and pdf_page → BO_page mapping

Limitations

Tuned for French BO SOMMAIRE segments; other layouts may degrade
Rare classes (Tex_AVIS) have low support in test
Private weights; do not redistribute without authorization
Requires word-level OCR + boxes (processor apply_ocr=False in the snippet above)

Related models

Region proposal: AvoCahDoe/sommaire-pp-doclayout-l-proposal
Earlier public segment classifier: AvoCahDoe/layoutlmv3-bo-segments

Citation

If you use this model in work derived from the SommaireTOC pipeline, please cite the Hub repo and base model microsoft/layoutlmv3-base.