README.md

metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Output_LayoutLMv3_v7
    results: []
datasets:
  - Noureddinesa/LayoutLmv3_v1

Output_LayoutLMv3_v7

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

• Loss: 0.1075
• Precision: 0.7928
• Recall: 0.8
• F1: 0.7964
• 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: 12
• eval_batch_size: 12
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• training_steps: 2600

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	9.09	100	0.4138	0.0	0.0	0.0	0.8962
No log	18.18	200	0.2709	0.1667	0.0273	0.0469	0.9014
No log	27.27	300	0.2003	0.6234	0.4364	0.5134	0.9360
No log	36.36	400	0.1711	0.6496	0.6909	0.6696	0.9481
0.3384	45.45	500	0.1624	0.6667	0.7273	0.6957	0.9498
0.3384	54.55	600	0.1502	0.6803	0.7545	0.7155	0.9550
0.3384	63.64	700	0.1428	0.7227	0.7818	0.7511	0.9602
0.3384	72.73	800	0.1452	0.7049	0.7818	0.7414	0.9550
0.3384	81.82	900	0.1260	0.7544	0.7818	0.7679	0.9671
0.0995	90.91	1000	0.1254	0.7544	0.7818	0.7679	0.9671
0.0995	100.0	1100	0.1211	0.7863	0.8364	0.8106	0.9706
0.0995	109.09	1200	0.1093	0.7739	0.8091	0.7911	0.9706
0.0995	118.18	1300	0.1081	0.7946	0.8091	0.8018	0.9723
0.0995	127.27	1400	0.1108	0.7778	0.8273	0.8018	0.9723
0.0608	136.36	1500	0.1115	0.7627	0.8182	0.7895	0.9706
0.0608	145.45	1600	0.1034	0.8053	0.8273	0.8161	0.9740
0.0608	154.55	1700	0.1050	0.7895	0.8182	0.8036	0.9723
0.0608	163.64	1800	0.1093	0.7739	0.8091	0.7911	0.9706
0.0608	172.73	1900	0.1043	0.7965	0.8182	0.8072	0.9723
0.0443	181.82	2000	0.1048	0.8036	0.8182	0.8108	0.9758
0.0443	190.91	2100	0.1067	0.8036	0.8182	0.8108	0.9758
0.0443	200.0	2200	0.1069	0.8036	0.8182	0.8108	0.9740
0.0443	209.09	2300	0.1083	0.7928	0.8	0.7964	0.9723
0.0443	218.18	2400	0.1079	0.7928	0.8	0.7964	0.9723
0.0381	227.27	2500	0.1076	0.7928	0.8	0.7964	0.9723
0.0381	236.36	2600	0.1075	0.7928	0.8	0.7964	0.9723

Framework versions

• Transformers 4.29.2
• Pytorch 2.2.2+cu121
• Datasets 2.18.0
• Tokenizers 0.13.3