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_100
    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: 0.8719646799116998
          - name: Recall
            type: recall
            value: 0.8869760479041916
          - name: F1
            type: f1
            value: 0.8794063079777364
          - name: Accuracy
            type: accuracy
            value: 0.8790322580645161

layoutlmv3-finetuned-cord_100

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: 0.7215
Precision: 0.8720
Recall: 0.8870
F1: 0.8794
Accuracy: 0.8790

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	12.5	250	1.0892	0.7345	0.7867	0.7597	0.7806
1.3039	25.0	500	0.7150	0.8054	0.8428	0.8237	0.8281
1.3039	37.5	750	0.6320	0.8335	0.8615	0.8473	0.8540
0.2171	50.0	1000	0.6427	0.8651	0.8832	0.8741	0.8722
0.2171	62.5	1250	0.6640	0.8672	0.8847	0.8759	0.8765
0.0654	75.0	1500	0.6758	0.8650	0.8825	0.8737	0.8731
0.0654	87.5	1750	0.7028	0.8684	0.8840	0.8761	0.8765
0.0338	100.0	2000	0.7252	0.8710	0.8847	0.8778	0.8769
0.0338	112.5	2250	0.7227	0.8710	0.8847	0.8778	0.8778
0.0257	125.0	2500	0.7215	0.8720	0.8870	0.8794	0.8790

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1