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

Loss: 0.2144
Precision: 0.9386
Recall: 0.9491
F1: 0.9438
Accuracy: 0.9516

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-05
train_batch_size: 5
eval_batch_size: 5
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.56	250	1.0830	0.6854	0.7582	0.7200	0.7725
1.4266	3.12	500	0.5944	0.8379	0.8630	0.8503	0.8680
1.4266	4.69	750	0.3868	0.8828	0.9079	0.8952	0.9155
0.4084	6.25	1000	0.3146	0.9133	0.9304	0.9218	0.9338
0.4084	7.81	1250	0.2658	0.9240	0.9371	0.9305	0.9419
0.2139	9.38	1500	0.2432	0.9299	0.9439	0.9368	0.9474
0.2139	10.94	1750	0.2333	0.9291	0.9416	0.9353	0.9482
0.1478	12.5	2000	0.2098	0.9358	0.9491	0.9424	0.9529
0.1478	14.06	2250	0.2134	0.9379	0.9491	0.9435	0.9516
0.1124	15.62	2500	0.2144	0.9386	0.9491	0.9438	0.9516

Framework versions

Transformers 4.22.1
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1

Sebabrata/layoutlmv3-finetuned-cord_100

layoutlmv3-finetuned-cord_100

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Sebabrata/
layoutlmv3-finetuned-cord_100