metadata

library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-large
tags:
  - generated_from_trainer
datasets:
  - mp-02/cord
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-large-cord
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: mp-02/cord
          type: mp-02/cord
        metrics:
          - name: Precision
            type: precision
            value: 0.970467596390484
          - name: Recall
            type: recall
            value: 0.980115990057995
          - name: F1
            type: f1
            value: 0.975267930750206
          - name: Accuracy
            type: accuracy
            value: 0.973924977127173

layoutlmv3-large-cord

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

Loss: 0.1373
Precision: 0.9705
Recall: 0.9801
F1: 0.9753
Accuracy: 0.9739

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: 10
eval_batch_size: 10
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	1.25	100	0.5589	0.8209	0.8583	0.8392	0.8394
No log	2.5	200	0.1936	0.9433	0.9644	0.9537	0.9579
No log	3.75	300	0.1456	0.9569	0.9760	0.9664	0.9698
No log	5.0	400	0.1368	0.9584	0.9743	0.9663	0.9726
0.4619	6.25	500	0.1448	0.9689	0.9809	0.9749	0.9744
0.4619	7.5	600	0.1286	0.9689	0.9818	0.9753	0.9753
0.4619	8.75	700	0.1311	0.9697	0.9809	0.9753	0.9748
0.4619	10.0	800	0.1335	0.9721	0.9809	0.9765	0.9758
0.4619	11.25	900	0.1355	0.9689	0.9793	0.9740	0.9753
0.0424	12.5	1000	0.1373	0.9705	0.9801	0.9753	0.9739

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu118
Datasets 2.21.0
Tokenizers 0.19.1