metadata

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: 0.9690152801358234

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: 0.2087
Precision: 0.9640
Recall: 0.9767
F1: 0.9703
Accuracy: 0.9690

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: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.5625	250	0.2302	0.9594	0.9720	0.9657	0.9656
0.041	3.125	500	0.2176	0.9542	0.9705	0.9623	0.9618
0.041	4.6875	750	0.1903	0.9573	0.9736	0.9654	0.9682
0.0302	6.25	1000	0.2027	0.9602	0.9744	0.9672	0.9660
0.0302	7.8125	1250	0.2174	0.9670	0.9775	0.9722	0.9703
0.019	9.375	1500	0.2018	0.9640	0.9775	0.9707	0.9711
0.019	10.9375	1750	0.2084	0.9677	0.9783	0.9730	0.9694
0.0115	12.5	2000	0.2087	0.9640	0.9767	0.9703	0.9690

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1