metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Output_LayoutLMv3_v5
    results: []
datasets:
  - Noureddinesa/LayoutLmv3_v1

Output_LayoutLMv3_v5

This model is a fine-tuned version of microsoft/layoutlmv3-large on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3054
Precision: 0.8505
Recall: 0.8273
F1: 0.8387
Accuracy: 0.9723

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-06
train_batch_size: 3
weight_decay = 0.1 (Regularization)
eval_batch_size: 3
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 3000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.38	100	0.2910	0.7636	0.7636	0.7636	0.9637
No log	4.76	200	0.2822	0.8318	0.8091	0.8203	0.9706
No log	7.14	300	0.2942	0.8148	0.8	0.8073	0.9689
No log	9.52	400	0.2821	0.7909	0.7909	0.7909	0.9671
0.0005	11.9	500	0.2896	0.7909	0.7909	0.7909	0.9671
0.0005	14.29	600	0.2914	0.8241	0.8091	0.8165	0.9706
0.0005	16.67	700	0.2912	0.8095	0.7727	0.7907	0.9689
0.0005	19.05	800	0.2578	0.8241	0.8091	0.8165	0.9706
0.0005	21.43	900	0.2830	0.8241	0.8091	0.8165	0.9706
0.0005	23.81	1000	0.2878	0.8411	0.8182	0.8295	0.9723
0.0005	26.19	1100	0.3151	0.8113	0.7818	0.7963	0.9689
0.0005	28.57	1200	0.3142	0.7706	0.7636	0.7671	0.9637
0.0005	30.95	1300	0.2972	0.8273	0.8273	0.8273	0.9723
0.0005	33.33	1400	0.2866	0.8148	0.8	0.8073	0.9706
0.0004	35.71	1500	0.2737	0.8288	0.8364	0.8326	0.9723
0.0004	38.1	1600	0.2653	0.8532	0.8455	0.8493	0.9740
0.0004	40.48	1700	0.2740	0.8108	0.8182	0.8145	0.9706
0.0004	42.86	1800	0.2861	0.8198	0.8273	0.8235	0.9706
0.0004	45.24	1900	0.2904	0.7788	0.8	0.7892	0.9671
0.0004	47.62	2000	0.2899	0.7788	0.8	0.7892	0.9671
0.0004	50.0	2100	0.2957	0.8108	0.8182	0.8145	0.9689
0.0004	52.38	2200	0.2962	0.8505	0.8273	0.8387	0.9723
0.0004	54.76	2300	0.2962	0.8505	0.8273	0.8387	0.9723
0.0004	57.14	2400	0.3057	0.8505	0.8273	0.8387	0.9723
0.0002	59.52	2500	0.3070	0.8505	0.8273	0.8387	0.9723
0.0002	61.9	2600	0.3050	0.8505	0.8273	0.8387	0.9723
0.0002	64.29	2700	0.3050	0.8505	0.8273	0.8387	0.9723
0.0002	66.67	2800	0.3052	0.8505	0.8273	0.8387	0.9723
0.0002	69.05	2900	0.3052	0.8505	0.8273	0.8387	0.9723
0.0	71.43	3000	0.3054	0.8505	0.8273	0.8387	0.9723

Framework versions

Transformers 4.29.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.13.3