metadata

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: layoutlmv3-invoice
    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: 0.9698412698412698
          - name: Recall
            type: recall
            value: 0.9591836734693877
          - name: F1
            type: f1
            value: 0.9644830307813733
          - name: Accuracy
            type: accuracy
            value: 0.9708383961117861

layoutlmv3-invoice

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: 0.1889
Precision: 0.9698
Recall: 0.9592
F1: 0.9645
Accuracy: 0.9708

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 3500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	5.32	250	0.3826	0.9471	0.9278	0.9374	0.9465
0.8433	10.64	500	0.1720	0.9697	0.9560	0.9628	0.9684
0.8433	15.96	750	0.1631	0.9714	0.9608	0.9661	0.9684
0.0347	21.28	1000	0.1548	0.9746	0.9639	0.9692	0.9733
0.0347	26.6	1250	0.1700	0.9698	0.9576	0.9637	0.9672
0.0116	31.91	1500	0.1812	0.9667	0.9576	0.9621	0.9648
0.0116	37.23	1750	0.1513	0.9683	0.9592	0.9637	0.9721
0.0066	42.55	2000	0.1555	0.9730	0.9623	0.9676	0.9757
0.0066	47.87	2250	0.1729	0.9714	0.9592	0.9652	0.9708
0.0048	53.19	2500	0.1854	0.9761	0.9623	0.9692	0.9721
0.0048	58.51	2750	0.1863	0.9714	0.9592	0.9652	0.9696
0.0037	63.83	3000	0.1813	0.9761	0.9623	0.9692	0.9733
0.0037	69.15	3250	0.1903	0.9698	0.9592	0.9645	0.9708
0.0034	74.47	3500	0.1889	0.9698	0.9592	0.9645	0.9708

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0