metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - cord-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cord_300
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cord-layoutlmv3
          type: cord-layoutlmv3
          config: cord
          split: train
          args: cord
        metrics:
          - name: Precision
            type: precision
            value: 0.9325426241660489
          - name: Recall
            type: recall
            value: 0.9416167664670658
          - name: F1
            type: f1
            value: 0.9370577281191806
          - name: Accuracy
            type: accuracy
            value: 0.9363327674023769

layoutlmv3-finetuned-cord_300

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

Loss: 0.3434
Precision: 0.9325
Recall: 0.9416
F1: 0.9371
Accuracy: 0.9363

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: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	4.17	250	1.0379	0.7204	0.7829	0.7504	0.7941
1.4162	8.33	500	0.5642	0.8462	0.8772	0.8614	0.8820
1.4162	12.5	750	0.3836	0.9055	0.9184	0.9119	0.9206
0.3211	16.67	1000	0.3209	0.9139	0.9296	0.9217	0.9334
0.3211	20.83	1250	0.2962	0.9275	0.9386	0.9330	0.9435
0.1191	25.0	1500	0.2979	0.9254	0.9379	0.9316	0.9402
0.1191	29.17	1750	0.3079	0.9282	0.9386	0.9334	0.9355
0.059	33.33	2000	0.3039	0.9232	0.9364	0.9298	0.9325
0.059	37.5	2250	0.3254	0.9248	0.9386	0.9316	0.9355
0.0342	41.67	2500	0.3404	0.9246	0.9364	0.9305	0.9334
0.0342	45.83	2750	0.3386	0.9354	0.9431	0.9392	0.9355
0.0226	50.0	3000	0.3274	0.9354	0.9431	0.9392	0.9359
0.0226	54.17	3250	0.3282	0.9341	0.9446	0.9393	0.9393
0.017	58.33	3500	0.3475	0.9319	0.9424	0.9371	0.9363
0.017	62.5	3750	0.3367	0.9340	0.9431	0.9385	0.9372
0.0145	66.67	4000	0.3434	0.9325	0.9416	0.9371	0.9363

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1