metadata

language:
  - en
  - de
  - fr
  - ja
license: mit
tags:
  - generated_from_trainer
  - DocLayNet
  - COCO
  - PDF
  - IBM
  - Financial-Reports
  - Finance
  - Manuals
  - Scientific-Articles
  - Science
  - Laws
  - Law
  - Regulations
  - Patents
  - Government-Tenders
  - object-detection
  - image-segmentation
  - token-classification
datasets:
  - pierreguillou/DocLayNet-base
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384
    results: []

Document Understanding model (at line level)

This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base with the DocLayNet base dataset. It achieves the following results on the evaluation set:

Loss: 1.0003
Precision: 0.8584
Recall: 0.8584
F1: 0.8584
Accuracy: 0.8584

Model description

The model was finetuned at line level on chunk of 384 tokens with overlap of 128 tokens. Thus, the model was trained with all layout and text data of all pages of the dataset.

At inference time, a calculation of best probabilities give the label to each line bounding boxes.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7223	0.21	500	0.7765	0.7741	0.7741	0.7741	0.7741
0.4469	0.42	1000	0.5914	0.8312	0.8312	0.8312	0.8312
0.3819	0.62	1500	0.8745	0.8102	0.8102	0.8102	0.8102
0.3361	0.83	2000	0.6991	0.8337	0.8337	0.8337	0.8337
0.2784	1.04	2500	0.7513	0.8119	0.8119	0.8119	0.8119
0.2377	1.25	3000	0.9048	0.8166	0.8166	0.8166	0.8166
0.2401	1.45	3500	1.2411	0.7939	0.7939	0.7939	0.7939
0.2054	1.66	4000	1.1594	0.8080	0.8080	0.8080	0.8080
0.1909	1.87	4500	0.7545	0.8425	0.8425	0.8425	0.8425
0.1704	2.08	5000	0.8567	0.8318	0.8318	0.8318	0.8318
0.1294	2.29	5500	0.8486	0.8489	0.8489	0.8489	0.8489
0.134	2.49	6000	0.7682	0.8573	0.8573	0.8573	0.8573
0.1354	2.7	6500	0.9871	0.8256	0.8256	0.8256	0.8256
0.1239	2.91	7000	1.1430	0.8189	0.8189	0.8189	0.8189
0.1012	3.12	7500	0.8272	0.8386	0.8386	0.8386	0.8386
0.0788	3.32	8000	1.0288	0.8365	0.8365	0.8365	0.8365
0.0802	3.53	8500	0.7197	0.8849	0.8849	0.8849	0.8849
0.0861	3.74	9000	1.1420	0.8320	0.8320	0.8320	0.8320
0.0639	3.95	9500	0.9563	0.8585	0.8585	0.8585	0.8585
0.0464	4.15	10000	1.0768	0.8511	0.8511	0.8511	0.8511
0.0412	4.36	10500	1.1184	0.8439	0.8439	0.8439	0.8439
0.039	4.57	11000	0.9634	0.8636	0.8636	0.8636	0.8636
0.0469	4.78	11500	0.9585	0.8634	0.8634	0.8634	0.8634
0.0395	4.99	12000	1.0003	0.8584	0.8584	0.8584	0.8584

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2