metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: LayoutLMv3_5_entities_4
    results: []

LayoutLMv3_5_entities_4

This model is a fine-tuned version of microsoft/layoutlmv3-large on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2514
Precision: 0.8762
Recall: 0.8519
F1: 0.8638
Accuracy: 0.9739

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-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: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.56	100	0.2241	0.8571	0.8333	0.8451	0.9691
No log	5.13	200	0.2210	0.8952	0.8704	0.8826	0.9758
No log	7.69	300	0.2300	0.9029	0.8611	0.8815	0.9758
No log	10.26	400	0.2630	0.8922	0.8426	0.8667	0.9720
0.0021	12.82	500	0.2692	0.8980	0.8148	0.8544	0.9710
0.0021	15.38	600	0.2414	0.9	0.8333	0.8654	0.9729
0.0021	17.95	700	0.2617	0.875	0.8426	0.8585	0.9729
0.0021	20.51	800	0.2558	0.8713	0.8148	0.8421	0.9720
0.0021	23.08	900	0.2581	0.8725	0.8241	0.8476	0.9729
0.0006	25.64	1000	0.2574	0.8679	0.8519	0.8598	0.9739
0.0006	28.21	1100	0.2806	0.88	0.8148	0.8462	0.9710
0.0006	30.77	1200	0.3032	0.8958	0.7963	0.8431	0.9691
0.0006	33.33	1300	0.2627	0.8889	0.8148	0.8502	0.9729
0.0006	35.9	1400	0.2661	0.8980	0.8148	0.8544	0.9720
0.0006	38.46	1500	0.2650	0.9	0.8333	0.8654	0.9739
0.0006	41.03	1600	0.2543	0.8835	0.8426	0.8626	0.9729
0.0006	43.59	1700	0.2593	0.8911	0.8333	0.8612	0.9739
0.0006	46.15	1800	0.2494	0.8857	0.8611	0.8732	0.9749
0.0006	48.72	1900	0.2494	0.8857	0.8611	0.8732	0.9749
0.0002	51.28	2000	0.2514	0.8762	0.8519	0.8638	0.9739

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3