metadata

library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlm-CC-7
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: layoutlmv3
          type: layoutlmv3
          config: FormsDataset
          split: test
          args: FormsDataset
        metrics:
          - name: Precision
            type: precision
            value: 0.12529002320185614
          - name: Recall
            type: recall
            value: 0.20224719101123595
          - name: F1
            type: f1
            value: 0.15472779369627507
          - name: Accuracy
            type: accuracy
            value: 0.19654427645788336

layoutlm-CC-7

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

Loss: 4.1612
Precision: 0.1253
Recall: 0.2022
F1: 0.1547
Accuracy: 0.1965

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: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
4.8141	1.0	1	4.7205	0.0921	0.1311	0.1082	0.0821
4.7028	2.0	2	4.6365	0.1414	0.2022	0.1664	0.1425
4.6011	3.0	3	4.5617	0.1230	0.2022	0.1530	0.1274
4.5126	4.0	4	4.4931	0.1174	0.2022	0.1486	0.1231
4.4376	5.0	5	4.4390	0.1166	0.2022	0.1479	0.1166
4.3778	6.0	6	4.3926	0.1166	0.2022	0.1479	0.1188
4.3224	7.0	7	4.3454	0.1166	0.2022	0.1479	0.1210
4.2658	8.0	8	4.3058	0.1166	0.2022	0.1479	0.1253
4.2182	9.0	9	4.2708	0.1179	0.2022	0.1490	0.1425
4.1796	10.0	10	4.2415	0.1208	0.2022	0.1513	0.1641
4.1423	11.0	11	4.2165	0.1222	0.2022	0.1523	0.1728
4.1197	12.0	12	4.1951	0.1230	0.2022	0.1530	0.1793
4.0976	13.0	13	4.1782	0.1241	0.2022	0.1538	0.1922
4.0801	14.0	14	4.1669	0.1253	0.2022	0.1547	0.1965
4.0627	15.0	15	4.1612	0.1253	0.2022	0.1547	0.1965

Framework versions

Transformers 4.47.0.dev0
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3