README.md

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_v2
    results: []
datasets:
  - Noureddinesa/LayoutLmv3

Output_LayoutLMv3_v2

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: 0.1240
• Precision: 0.8174
• Recall: 0.8319
• F1: 0.8246
• Accuracy: 0.9762

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: 3e-07
• 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: 3500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.27	100	0.5286	0.0	0.0	0.0	0.8867
No log	4.55	200	0.4075	0.0	0.0	0.0	0.8867
No log	6.82	300	0.3231	0.2258	0.0310	0.0545	0.8933
No log	9.09	400	0.2612	0.5546	0.2920	0.3826	0.9210
0.4595	11.36	500	0.2246	0.5897	0.4071	0.4817	0.9295
0.4595	13.64	600	0.2004	0.6869	0.6018	0.6415	0.9476
0.4595	15.91	700	0.1866	0.7019	0.6460	0.6728	0.9514
0.4595	18.18	800	0.1712	0.7419	0.7124	0.7269	0.96
0.4595	20.45	900	0.1599	0.7647	0.7478	0.7562	0.9638
0.1593	22.73	1000	0.1568	0.7729	0.7832	0.7780	0.9686
0.1593	25.0	1100	0.1476	0.7686	0.7788	0.7736	0.9686
0.1593	27.27	1200	0.1395	0.7930	0.7965	0.7947	0.9714
0.1593	29.55	1300	0.1372	0.8	0.8142	0.8070	0.9733
0.1593	31.82	1400	0.1356	0.8035	0.8142	0.8088	0.9743
0.0987	34.09	1500	0.1326	0.7939	0.8009	0.7974	0.9714
0.0987	36.36	1600	0.1292	0.7939	0.8009	0.7974	0.9714
0.0987	38.64	1700	0.1300	0.8017	0.8230	0.8122	0.9743
0.0987	40.91	1800	0.1260	0.8062	0.8097	0.8079	0.9724
0.0987	43.18	1900	0.1244	0.8017	0.8230	0.8122	0.9743
0.0689	45.45	2000	0.1228	0.8150	0.8186	0.8168	0.9752
0.0689	47.73	2100	0.1230	0.8087	0.8230	0.8158	0.9752
0.0689	50.0	2200	0.1225	0.8114	0.8186	0.8150	0.9743
0.0689	52.27	2300	0.1226	0.8114	0.8186	0.8150	0.9743
0.0689	54.55	2400	0.1237	0.8174	0.8319	0.8246	0.9762
0.0545	56.82	2500	0.1234	0.8122	0.8230	0.8176	0.9752
0.0545	59.09	2600	0.1240	0.8122	0.8230	0.8176	0.9752
0.0545	61.36	2700	0.1242	0.8122	0.8230	0.8176	0.9752
0.0545	63.64	2800	0.1241	0.8122	0.8230	0.8176	0.9752
0.0545	65.91	2900	0.1253	0.8190	0.8407	0.8297	0.9771
0.0491	68.18	3000	0.1235	0.8114	0.8186	0.8150	0.9743
0.0491	70.45	3100	0.1236	0.8166	0.8274	0.8220	0.9752
0.0491	72.73	3200	0.1231	0.8166	0.8274	0.8220	0.9752
0.0491	75.0	3300	0.1239	0.8190	0.8407	0.8297	0.9771
0.0491	77.27	3400	0.1241	0.8190	0.8407	0.8297	0.9771
0.0442	79.55	3500	0.1240	0.8174	0.8319	0.8246	0.9762

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2