metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - violations
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-violations-test
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: violations
          type: violations
          config: ViolationsExtraction
          split: test
          args: ViolationsExtraction
        metrics:
          - name: Precision
            type: precision
            value: 0.9482758620689655
          - name: Recall
            type: recall
            value: 0.9116022099447514
          - name: F1
            type: f1
            value: 0.9295774647887324
          - name: Accuracy
            type: accuracy
            value: 0.9502762430939227

layoutlmv3-violations-test

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

Loss: 0.3685
Precision: 0.9483
Recall: 0.9116
F1: 0.9296
Accuracy: 0.9503

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	9.0909	100	0.2997	0.9543	0.9227	0.9382	0.9558
No log	18.1818	200	0.3729	0.9425	0.9061	0.9239	0.9448
No log	27.2727	300	0.3408	0.9543	0.9227	0.9382	0.9558
No log	36.3636	400	0.3566	0.9483	0.9116	0.9296	0.9503
0.0997	45.4545	500	0.3685	0.9483	0.9116	0.9296	0.9503
0.0997	54.5455	600	0.3736	0.9483	0.9116	0.9296	0.9503
0.0997	63.6364	700	0.3866	0.9483	0.9116	0.9296	0.9503
0.0997	72.7273	800	0.3990	0.9483	0.9116	0.9296	0.9503
0.0997	81.8182	900	0.4018	0.9483	0.9116	0.9296	0.9503
0.001	90.9091	1000	0.3979	0.9483	0.9116	0.9296	0.9503

Framework versions

Transformers 4.42.1
Pytorch 2.3.1+cu118
Datasets 2.20.0
Tokenizers 0.19.1