metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-large
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Output_LayoutLMv3_v1
    results: []

Output_LayoutLMv3_v1

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: 0.2030
Precision: 0.8
Recall: 0.8319
F1: 0.8156
Accuracy: 0.9743

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: 2
eval_batch_size: 2
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	2.27	100	0.3343	0.2051	0.0354	0.0604	0.8943
No log	4.55	200	0.1934	0.7143	0.6858	0.6998	0.9524
No log	6.82	300	0.1541	0.7344	0.7832	0.7580	0.9590
No log	9.09	400	0.1375	0.7542	0.8009	0.7768	0.9648
0.2233	11.36	500	0.1323	0.7915	0.8230	0.8069	0.9695
0.2233	13.64	600	0.1395	0.8	0.8142	0.8070	0.9695
0.2233	15.91	700	0.1495	0.7773	0.8186	0.7974	0.9686
0.2233	18.18	800	0.1444	0.8103	0.8319	0.8210	0.9752
0.2233	20.45	900	0.1732	0.7550	0.8319	0.7916	0.9676
0.0375	22.73	1000	0.1553	0.7966	0.8319	0.8139	0.9743
0.0375	25.0	1100	0.1639	0.7924	0.8274	0.8095	0.9724
0.0375	27.27	1200	0.1598	0.8034	0.8319	0.8174	0.9752
0.0375	29.55	1300	0.1723	0.8069	0.8319	0.8192	0.9743
0.0375	31.82	1400	0.1929	0.7810	0.8363	0.8077	0.9724
0.0188	34.09	1500	0.1940	0.7866	0.8319	0.8086	0.9714
0.0188	36.36	1600	0.1904	0.7932	0.8319	0.8121	0.9724
0.0188	38.64	1700	0.1910	0.7899	0.8319	0.8103	0.9724
0.0188	40.91	1800	0.2083	0.7801	0.8319	0.8051	0.9705
0.0188	43.18	1900	0.1880	0.8	0.8319	0.8156	0.9743
0.0123	45.45	2000	0.1902	0.8069	0.8319	0.8192	0.9752
0.0123	47.73	2100	0.1894	0.8095	0.8274	0.8184	0.9752
0.0123	50.0	2200	0.1833	0.8210	0.8319	0.8264	0.9771
0.0123	52.27	2300	0.1911	0.8069	0.8319	0.8192	0.9752
0.0123	54.55	2400	0.1972	0.8	0.8319	0.8156	0.9743
0.0086	56.82	2500	0.1924	0.8139	0.8319	0.8228	0.9762
0.0086	59.09	2600	0.1983	0.8	0.8319	0.8156	0.9743
0.0086	61.36	2700	0.2033	0.8	0.8319	0.8156	0.9743
0.0086	63.64	2800	0.2039	0.8	0.8319	0.8156	0.9743
0.0086	65.91	2900	0.2026	0.8	0.8319	0.8156	0.9743
0.0084	68.18	3000	0.2030	0.8	0.8319	0.8156	0.9743

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2