metadata

library_name: transformers
base_model: layoutlmv3
tags:
  - generated_from_trainer
datasets:
  - mp-02/sroie
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-base-sroie
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: mp-02/sroie
          type: mp-02/sroie
        metrics:
          - name: Precision
            type: precision
            value: 0.9236398345529748
          - name: Recall
            type: recall
            value: 0.9625331564986738
          - name: F1
            type: f1
            value: 0.9426855008930022
          - name: Accuracy
            type: accuracy
            value: 0.9821007282798235

layoutlmv3-base-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: 0.0639
Precision: 0.9236
Recall: 0.9625
F1: 0.9427
Accuracy: 0.9821

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 3000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.5	100	0.1464	0.9081	0.8488	0.8775	0.9645
No log	5.0	200	0.0821	0.9322	0.9294	0.9308	0.9791
No log	7.5	300	0.0746	0.9204	0.9469	0.9335	0.9796
No log	10.0	400	0.0685	0.9213	0.9506	0.9357	0.9802
0.1644	12.5	500	0.0657	0.9192	0.9586	0.9385	0.9809
0.1644	15.0	600	0.0678	0.9071	0.9649	0.9351	0.9796
0.1644	17.5	700	0.0636	0.9242	0.9625	0.9430	0.9822
0.1644	20.0	800	0.0643	0.9238	0.9609	0.9420	0.9819
0.1644	22.5	900	0.0620	0.9254	0.9629	0.9438	0.9824
0.0331	25.0	1000	0.0639	0.9236	0.9625	0.9427	0.9821
0.0331	27.5	1100	0.0632	0.9249	0.9639	0.9440	0.9825
0.0331	30.0	1200	0.0619	0.9268	0.9615	0.9439	0.9825
0.0331	32.5	1300	0.0640	0.9216	0.9665	0.9435	0.9823
0.0331	35.0	1400	0.0653	0.9201	0.9665	0.9428	0.9820

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1