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: test
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: layoutlmv3
          type: layoutlmv3
          config: InvoiceExtraction
          split: test
          args: InvoiceExtraction
        metrics:
          - name: Precision
            type: precision
            value: 0.9735537190082645
          - name: Recall
            type: recall
            value: 0.9751655629139073
          - name: F1
            type: f1
            value: 0.9743589743589743
          - name: Accuracy
            type: accuracy
            value: 0.9924257137308216

test

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: 0.0899
Precision: 0.9736
Recall: 0.9752
F1: 0.9744
Accuracy: 0.9924

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: 2
eval_batch_size: 2
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
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.5291	100	0.4956	0.7821	0.7724	0.7772	0.9413
No log	1.0582	200	0.1802	0.9285	0.9247	0.9266	0.9761
No log	1.5873	300	0.1465	0.9334	0.9512	0.9422	0.9841
No log	2.1164	400	0.1309	0.9447	0.9611	0.9528	0.9876
0.3392	2.6455	500	0.1095	0.9516	0.9594	0.9555	0.9891
0.3392	3.1746	600	0.1022	0.9573	0.9652	0.9613	0.9915
0.3392	3.7037	700	0.1081	0.9573	0.9661	0.9617	0.9918
0.3392	4.2328	800	0.0922	0.9726	0.9694	0.9710	0.9920
0.3392	4.7619	900	0.0930	0.9702	0.9702	0.9702	0.9916
0.0282	5.2910	1000	0.0899	0.9736	0.9752	0.9744	0.9924

Framework versions

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