metadata

library_name: transformers
base_model: layoutlmv3
tags:
  - generated_from_trainer
datasets:
  - mp-02/sroie
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-sroie
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: mp-02/sroie
          type: mp-02/sroie
        metrics:
          - name: Precision
            type: precision
            value: 0.9232981783317353
          - name: Recall
            type: recall
            value: 0.9578912466843501
          - name: F1
            type: f1
            value: 0.9402766476810415
          - name: Accuracy
            type: accuracy
            value: 0.981485280541594

layoutlmv3-finetuned-sroie

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

Loss: 0.0651
Precision: 0.9233
Recall: 0.9579
F1: 0.9403
Accuracy: 0.9815

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-06
train_batch_size: 6
eval_batch_size: 6
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 1500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.3810	250	0.0957	0.9075	0.9304	0.9188	0.9752
0.1943	4.7619	500	0.0699	0.9260	0.9456	0.9357	0.9805
0.1943	7.1429	750	0.0657	0.9291	0.9513	0.9400	0.9817
0.0485	9.5238	1000	0.0651	0.9233	0.9579	0.9403	0.9815
0.0485	11.9048	1250	0.0661	0.9155	0.9625	0.9384	0.9808
0.0397	14.2857	1500	0.0660	0.9161	0.9632	0.9391	0.9810

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1