metadata

library_name: transformers
base_model: layoutlmv3
tags:
  - generated_from_trainer
datasets:
  - mp-02/cord
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cord
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: mp-02/cord
          type: mp-02/cord
        metrics:
          - name: Precision
            type: precision
            value: 0.963984674329502
          - name: Recall
            type: recall
            value: 0.9767080745341615
          - name: F1
            type: f1
            value: 0.9703046664095644
          - name: Accuracy
            type: accuracy
            value: 0.9702886247877759

layoutlmv3-finetuned-cord

This model is a fine-tuned version of layoutlmv3 on the mp-02/cord dataset. It achieves the following results on the evaluation set:

Loss: 0.2044
Precision: 0.9640
Recall: 0.9767
F1: 0.9703
Accuracy: 0.9703

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-05
train_batch_size: 14
eval_batch_size: 14
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	1.7241	100	0.4595	0.8310	0.8859	0.8576	0.8786
No log	3.4483	200	0.2637	0.9284	0.9565	0.9423	0.9469
No log	5.1724	300	0.2096	0.9513	0.9697	0.9604	0.9626
No log	6.8966	400	0.2016	0.9512	0.9689	0.96	0.9622
0.3892	8.6207	500	0.2418	0.9453	0.9658	0.9555	0.9593
0.3892	10.3448	600	0.2149	0.9579	0.9713	0.9645	0.9660
0.3892	12.0690	700	0.2090	0.9608	0.9713	0.9660	0.9652
0.3892	13.7931	800	0.2202	0.9580	0.9728	0.9653	0.9673
0.3892	15.5172	900	0.2217	0.9595	0.9744	0.9669	0.9682
0.0278	17.2414	1000	0.2044	0.9640	0.9767	0.9703	0.9703

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1