metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - data_cartas_layoutv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-letter_100
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: data_cartas_layoutv3
          type: data_cartas_layoutv3
          config: default
          split: test
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.7411894273127754
          - name: Recall
            type: recall
            value: 0.8672680412371134
          - name: F1
            type: f1
            value: 0.7992874109263659
          - name: Accuracy
            type: accuracy
            value: 0.9631952889969916

layoutlmv3-finetuned-letter_100

This model is a fine-tuned version of microsoft/layoutlmv3-base on the data_cartas_layoutv3 dataset. It achieves the following results on the evaluation set:

Loss: 0.1884
Precision: 0.7412
Recall: 0.8673
F1: 0.7993
Accuracy: 0.9632

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-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 3000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	3.57	250	0.4503	0.2934	0.2242	0.2542	0.8928
0.5521	7.14	500	0.2833	0.4291	0.4639	0.4458	0.9209
0.5521	10.71	750	0.2116	0.5702	0.6753	0.6183	0.9437
0.173	14.29	1000	0.1786	0.6414	0.7835	0.7053	0.9562
0.173	17.86	1250	0.1772	0.6815	0.8492	0.7562	0.9581
0.077	21.43	1500	0.1737	0.7144	0.8737	0.7861	0.9616
0.077	25.0	1750	0.1768	0.7311	0.8724	0.7955	0.9615
0.0441	28.57	2000	0.1694	0.7726	0.8273	0.7990	0.9646
0.0441	32.14	2250	0.1874	0.7400	0.8621	0.7964	0.9620
0.0293	35.71	2500	0.1862	0.7321	0.8698	0.7951	0.9622
0.0293	39.29	2750	0.1887	0.7332	0.8711	0.7962	0.9620
0.0237	42.86	3000	0.1884	0.7412	0.8673	0.7993	0.9632

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3