metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - cord-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cord_500
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cord-layoutlmv3
          type: cord-layoutlmv3
          config: cord
          split: train
          args: cord
        metrics:
          - name: Precision
            type: precision
            value: 0.9509293680297398
          - name: Recall
            type: recall
            value: 0.9573353293413174
          - name: F1
            type: f1
            value: 0.9541215964192465
          - name: Accuracy
            type: accuracy
            value: 0.9609507640067911

layoutlmv3-finetuned-cord_500

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

Loss: 0.2339
Precision: 0.9509
Recall: 0.9573
F1: 0.9541
Accuracy: 0.9610

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: 1e-05
train_batch_size: 5
eval_batch_size: 5
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	2.5	250	0.9950	0.7114	0.7784	0.7434	0.7903
1.3831	5.0	500	0.5152	0.8483	0.8787	0.8632	0.8816
1.3831	7.5	750	0.3683	0.9013	0.9154	0.9083	0.9240
0.3551	10.0	1000	0.3051	0.9201	0.9304	0.9252	0.9363
0.3551	12.5	1250	0.2636	0.9375	0.9424	0.9399	0.9457
0.1562	15.0	1500	0.2498	0.9385	0.9476	0.9430	0.9508
0.1562	17.5	1750	0.2380	0.9414	0.9499	0.9456	0.9559
0.0863	20.0	2000	0.2355	0.9400	0.9491	0.9445	0.9542
0.0863	22.5	2250	0.2268	0.9451	0.9536	0.9493	0.9601
0.0512	25.0	2500	0.2277	0.9429	0.9513	0.9471	0.9588
0.0512	27.5	2750	0.2315	0.9473	0.9551	0.9512	0.9593
0.0358	30.0	3000	0.2294	0.9509	0.9573	0.9541	0.9605
0.0358	32.5	3250	0.2330	0.9458	0.9543	0.9501	0.9593
0.028	35.0	3500	0.2374	0.9487	0.9558	0.9523	0.9597
0.028	37.5	3750	0.2374	0.9501	0.9558	0.9530	0.9593
0.0244	40.0	4000	0.2339	0.9509	0.9573	0.9541	0.9610

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1