metadata

library_name: transformers
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: 0.9501488095238095
          - name: Recall
            type: recall
            value: 0.9558383233532934
          - name: F1
            type: f1
            value: 0.9529850746268657
          - name: Accuracy
            type: accuracy
            value: 0.9639219015280136

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: 0.2004
Precision: 0.9501
Recall: 0.9558
F1: 0.9530
Accuracy: 0.9639

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
training_steps: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.5625	250	1.1111	0.6740	0.7552	0.7123	0.7695
1.4577	3.125	500	0.5810	0.8362	0.8675	0.8516	0.8697
1.4577	4.6875	750	0.3787	0.8903	0.9109	0.9005	0.9219
0.4114	6.25	1000	0.2920	0.9167	0.9311	0.9239	0.9406
0.4114	7.8125	1250	0.2640	0.9161	0.9311	0.9235	0.9380
0.2215	9.375	1500	0.2366	0.9297	0.9409	0.9353	0.9474
0.2215	10.9375	1750	0.2232	0.9407	0.9491	0.9449	0.9571
0.1486	12.5	2000	0.2083	0.9450	0.9513	0.9482	0.9601
0.1486	14.0625	2250	0.1981	0.9480	0.9551	0.9515	0.9639
0.1129	15.625	2500	0.2004	0.9501	0.9558	0.9530	0.9639

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0