metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - cord-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cord_100
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cord-layoutlmv3
          type: cord-layoutlmv3
          config: cord
          split: test
          args: cord
        metrics:
          - name: Precision
            type: precision
            value: 0.9458456973293768
          - name: Recall
            type: recall
            value: 0.9543413173652695
          - name: F1
            type: f1
            value: 0.9500745156482863
          - name: Accuracy
            type: accuracy
            value: 0.9605263157894737

layoutlmv3-finetuned-cord_100

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: 0.2092
Precision: 0.9458
Recall: 0.9543
F1: 0.9501
Accuracy: 0.9605

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: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.56	250	0.9809	0.7284	0.7829	0.7547	0.7890
1.3679	3.12	500	0.5431	0.8426	0.8653	0.8538	0.8727
1.3679	4.69	750	0.3871	0.8939	0.9147	0.9042	0.9198
0.3879	6.25	1000	0.3038	0.9175	0.9326	0.9250	0.9389
0.3879	7.81	1250	0.2561	0.9255	0.9386	0.9320	0.9448
0.2076	9.38	1500	0.2329	0.9342	0.9454	0.9397	0.9533
0.2076	10.94	1750	0.2166	0.9458	0.9536	0.9497	0.9605
0.1404	12.5	2000	0.2144	0.9488	0.9566	0.9527	0.9622
0.1404	14.06	2250	0.2147	0.9495	0.9573	0.9534	0.9626
0.109	15.62	2500	0.2092	0.9458	0.9543	0.9501	0.9605

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1