metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - layoutlm_v3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cord_100
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: layoutlm_v3
          type: layoutlm_v3
          config: cord
          split: test
          args: cord
        metrics:
          - name: Precision
            type: precision
            value: 0.9297856614929786
          - name: Recall
            type: recall
            value: 0.9416167664670658
          - name: F1
            type: f1
            value: 0.9356638155448121
          - name: Accuracy
            type: accuracy
            value: 0.9393039049235993

layoutlmv3-finetuned-cord_100

This model is a fine-tuned version of microsoft/layoutlmv3-base on the layoutlm_v3 dataset. It achieves the following results on the evaluation set:

Loss: 0.2976
Precision: 0.9298
Recall: 0.9416
F1: 0.9357
Accuracy: 0.9393

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	4.17	250	1.0222	0.7468	0.7949	0.7701	0.8014
1.3962	8.33	500	0.5292	0.8414	0.8735	0.8571	0.8778
1.3962	12.5	750	0.3844	0.9049	0.9192	0.9120	0.9249
0.335	16.67	1000	0.3302	0.9243	0.9326	0.9285	0.9342
0.335	20.83	1250	0.3062	0.9204	0.9349	0.9276	0.9406
0.1419	25.0	1500	0.2931	0.9268	0.9386	0.9327	0.9414
0.1419	29.17	1750	0.2925	0.9248	0.9386	0.9316	0.9359
0.0801	33.33	2000	0.2963	0.9276	0.9394	0.9334	0.9359
0.0801	37.5	2250	0.2916	0.9283	0.9401	0.9342	0.9363
0.0584	41.67	2500	0.2976	0.9298	0.9416	0.9357	0.9393

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2