metadata

library_name: transformers
base_model: layoutlmv3
tags:
  - generated_from_trainer
datasets:
  - mp-02/cord-sroie
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-base-cord-sroie
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: mp-02/cord-sroie
          type: mp-02/cord-sroie
        metrics:
          - name: Precision
            type: precision
            value: 0.9399720800372267
          - name: Recall
            type: recall
            value: 0.9465791940018744
          - name: F1
            type: f1
            value: 0.9432640672425869
          - name: Accuracy
            type: accuracy
            value: 0.9813340410474168

layoutlmv3-base-cord-sroie

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

Loss: 0.0970
Precision: 0.9400
Recall: 0.9466
F1: 0.9433
Accuracy: 0.9813

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.2222	100	0.3258	0.8171	0.7685	0.7921	0.9363
No log	4.4444	200	0.1516	0.9078	0.8946	0.9011	0.9694
No log	6.6667	300	0.1085	0.9315	0.9175	0.9245	0.9761
No log	8.8889	400	0.1000	0.9382	0.9456	0.9419	0.9817
0.4015	11.1111	500	0.0970	0.9400	0.9466	0.9433	0.9813
0.4015	13.3333	600	0.1064	0.9505	0.9358	0.9431	0.9814
0.4015	15.5556	700	0.1095	0.9465	0.9372	0.9418	0.9812

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1