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_200
    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: 0.9033923303834809
          - name: Recall
            type: recall
            value: 0.9169161676646707
          - name: F1
            type: f1
            value: 0.9101040118870729
          - name: Accuracy
            type: accuracy
            value: 0.9121392190152802

layoutlmv3-finetuned-cord_200

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: 0.4529
Precision: 0.9034
Recall: 0.9169
F1: 0.9101
Accuracy: 0.9121

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: 3000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	6.25	250	1.0785	0.6815	0.7575	0.7175	0.7780
1.3902	12.5	500	0.5871	0.8542	0.8683	0.8612	0.8604
1.3902	18.75	750	0.4572	0.8728	0.8937	0.8831	0.8905
0.298	25.0	1000	0.3947	0.8936	0.9117	0.9026	0.9092
0.298	31.25	1250	0.3925	0.8982	0.9177	0.9078	0.9117
0.1023	37.5	1500	0.4290	0.8908	0.9102	0.9004	0.9041
0.1023	43.75	1750	0.4220	0.8980	0.9162	0.9070	0.9117
0.0475	50.0	2000	0.4755	0.8944	0.9064	0.9004	0.8990
0.0475	56.25	2250	0.4635	0.8992	0.9147	0.9069	0.9070
0.0296	62.5	2500	0.4475	0.9019	0.9154	0.9086	0.9117
0.0296	68.75	2750	0.4484	0.9004	0.9139	0.9071	0.9079
0.0242	75.0	3000	0.4529	0.9034	0.9169	0.9101	0.9121

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1