metadata

library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - mp-02/cord
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-base-cord2
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: mp-02/cord
          type: mp-02/cord
        metrics:
          - name: Precision
            type: precision
            value: 0.9466882067851373
          - name: Recall
            type: recall
            value: 0.9614438063986874
          - name: F1
            type: f1
            value: 0.9540089540089539
          - name: Accuracy
            type: accuracy
            value: 0.9611161939615737

layoutlmv3-base-cord2

This model is a fine-tuned version of microsoft/layoutlmv3-base on the mp-02/cord dataset. It achieves the following results on the evaluation set:

Loss: 0.1856
Precision: 0.9467
Recall: 0.9614
F1: 0.9540
Accuracy: 0.9611

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
training_steps: 3000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	100	1.2612	0.6788	0.7629	0.7184	0.7685
No log	2.0	200	0.5621	0.8674	0.8802	0.8738	0.8916
No log	3.0	300	0.3639	0.8846	0.9114	0.8978	0.9186
No log	4.0	400	0.3197	0.9153	0.9393	0.9271	0.9410
0.8719	5.0	500	0.2304	0.9357	0.9549	0.9452	0.9543
0.8719	6.0	600	0.2069	0.9389	0.9573	0.9480	0.9556
0.8719	7.0	700	0.2081	0.9459	0.9606	0.9532	0.9593
0.8719	8.0	800	0.1901	0.9532	0.9688	0.9609	0.9666
0.8719	9.0	900	0.1559	0.9515	0.9647	0.9580	0.9671
0.136	10.0	1000	0.1856	0.9467	0.9614	0.9540	0.9611
0.136	11.0	1100	0.2020	0.9537	0.9631	0.9584	0.9629
0.136	12.0	1200	0.1908	0.9552	0.9631	0.9592	0.9620

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3