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_vimal
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cord-layoutlmv3
          type: cord-layoutlmv3
          config: cord
          split: test
          args: cord
        metrics:
          - name: Precision
            type: precision
            value: 0.717948717948718
          - name: Recall
            type: recall
            value: 0.7368421052631579
          - name: F1
            type: f1
            value: 0.7272727272727273
          - name: Accuracy
            type: accuracy
            value: 0.7333333333333333

layoutlmv3-finetuned-cord_vimal

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: 1.8321
Precision: 0.7179
Recall: 0.7368
F1: 0.7273
Accuracy: 0.7333

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: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	125.0	250	1.2027	0.7564	0.7763	0.7662	0.7481
0.8449	250.0	500	1.3990	0.7089	0.7368	0.7226	0.7333
0.8449	375.0	750	1.5343	0.7179	0.7368	0.7273	0.7333
0.0296	500.0	1000	1.6144	0.75	0.75	0.75	0.7407
0.0296	625.0	1250	1.6898	0.7179	0.7368	0.7273	0.7333
0.0134	750.0	1500	1.7402	0.7179	0.7368	0.7273	0.7333
0.0134	875.0	1750	1.7888	0.7179	0.7368	0.7273	0.7333
0.0089	1000.0	2000	1.8041	0.7179	0.7368	0.7273	0.7333
0.0089	1125.0	2250	1.8209	0.7179	0.7368	0.7273	0.7333
0.0073	1250.0	2500	1.8321	0.7179	0.7368	0.7273	0.7333

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2