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_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: 0.8760330578512396
          - name: Recall
            type: recall
            value: 0.8983050847457628
          - name: F1
            type: f1
            value: 0.8870292887029289
          - name: Accuracy
            type: accuracy
            value: 0.9146919431279621

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: 0.4548
Precision: 0.8760
Recall: 0.8983
F1: 0.8870
Accuracy: 0.9147

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	31.25	250	0.7175	0.7874	0.8475	0.8163	0.8768
1.0535	62.5	500	0.4279	0.8843	0.9068	0.8954	0.9147
1.0535	93.75	750	0.4042	0.8760	0.8983	0.8870	0.9147
0.0727	125.0	1000	0.4065	0.8760	0.8983	0.8870	0.9194
0.0727	156.25	1250	0.4290	0.8843	0.9068	0.8954	0.9147
0.0245	187.5	1500	0.4511	0.8760	0.8983	0.8870	0.9100
0.0245	218.75	1750	0.4594	0.8760	0.8983	0.8870	0.9147
0.0155	250.0	2000	0.4566	0.8760	0.8983	0.8870	0.9147
0.0155	281.25	2250	0.4489	0.8760	0.8983	0.8870	0.9147
0.0125	312.5	2500	0.4548	0.8760	0.8983	0.8870	0.9147

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3