metadata

library_name: transformers
base_model: layoutlmv3
tags:
  - generated_from_trainer
datasets:
  - mp-02/cord
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cord
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: mp-02/cord
          type: mp-02/cord
        metrics:
          - name: Precision
            type: precision
            value: 0.9609494640122511
          - name: Recall
            type: recall
            value: 0.9743788819875776
          - name: F1
            type: f1
            value: 0.9676175790285273
          - name: Accuracy
            type: accuracy
            value: 0.9690152801358234

layoutlmv3-finetuned-cord

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

Loss: 0.1800
Precision: 0.9609
Recall: 0.9744
F1: 0.9676
Accuracy: 0.9690

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: 5e-05
train_batch_size: 10
eval_batch_size: 10
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.25	100	0.5802	0.8140	0.8696	0.8408	0.8574
No log	2.5	200	0.2946	0.9013	0.9433	0.9219	0.9329
No log	3.75	300	0.2259	0.9409	0.9635	0.9521	0.9571
No log	5.0	400	0.2496	0.9376	0.9565	0.9470	0.9482
0.4497	6.25	500	0.2174	0.9399	0.9596	0.9497	0.9546
0.4497	7.5	600	0.1812	0.9535	0.9713	0.9623	0.9648
0.4497	8.75	700	0.1699	0.9587	0.9720	0.9653	0.9699
0.4497	10.0	800	0.1810	0.9625	0.9752	0.9688	0.9690
0.4497	11.25	900	0.1789	0.9647	0.9767	0.9707	0.9694
0.0416	12.5	1000	0.1800	0.9609	0.9744	0.9676	0.9690

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1