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: 0.4115296803652968
          - name: Recall
            type: recall
            value: 0.5396706586826348
          - name: F1
            type: f1
            value: 0.46696891191709844
          - name: Accuracy
            type: accuracy
            value: 0.4350594227504245

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: 2.5624
Precision: 0.4115
Recall: 0.5397
F1: 0.4670
Accuracy: 0.4351

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.06	10	3.8065	0.1637	0.2582	0.2003	0.2585
No log	0.12	20	3.4787	0.4661	0.3862	0.4224	0.3353
No log	0.19	30	3.2587	0.4332	0.4731	0.4522	0.3667
No log	0.25	40	3.0615	0.4144	0.4873	0.4479	0.3846
No log	0.31	50	2.9052	0.3993	0.5090	0.4475	0.4024
No log	0.38	60	2.7819	0.3876	0.5165	0.4429	0.4143
No log	0.44	70	2.6853	0.3891	0.5202	0.4452	0.4164
No log	0.5	80	2.6245	0.3942	0.5269	0.4510	0.4236
No log	0.56	90	2.5777	0.4056	0.5352	0.4614	0.4312
No log	0.62	100	2.5624	0.4115	0.5397	0.4670	0.4351

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cpu
Datasets 2.12.0
Tokenizers 0.13.3