metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
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: test
          args: cord
        metrics:
          - name: Precision
            type: precision
            value: 0.9561011904761905
          - name: Recall
            type: recall
            value: 0.9618263473053892
          - name: F1
            type: f1
            value: 0.958955223880597
          - name: Accuracy
            type: accuracy
            value: 0.9702886247877759

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: 0.1726
Precision: 0.9561
Recall: 0.9618
F1: 0.9590
Accuracy: 0.9703

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	1.56	250	1.0075	0.7597	0.8046	0.7815	0.8145
1.3907	3.12	500	0.5155	0.8388	0.8683	0.8533	0.8841
1.3907	4.69	750	0.3486	0.8917	0.9117	0.9016	0.9283
0.3755	6.25	1000	0.2722	0.9211	0.9356	0.9283	0.9435
0.3755	7.81	1250	0.2399	0.9356	0.9461	0.9408	0.9533
0.1857	9.38	1500	0.2170	0.9376	0.9454	0.9415	0.9542
0.1857	10.94	1750	0.1917	0.9510	0.9588	0.9549	0.9660
0.1236	12.5	2000	0.1821	0.9502	0.9573	0.9538	0.9652
0.1236	14.06	2250	0.1870	0.9538	0.9588	0.9563	0.9669
0.0858	15.62	2500	0.1741	0.9583	0.9633	0.9608	0.9711
0.0858	17.19	2750	0.1726	0.9561	0.9611	0.9586	0.9690
0.0708	18.75	3000	0.1726	0.9561	0.9618	0.9590	0.9703

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0