metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - invoices
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: microsoft/layoutlmv3-base
model-index:
  - name: layoutlmv3-finetuned-invoice
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: invoices
          type: invoices
          config: sroie
          split: train
          args: sroie
        metrics:
          - type: precision
            value: 0.975
            name: Precision
          - type: recall
            value: 0.975
            name: Recall
          - type: f1
            value: 0.975
            name: F1
          - type: accuracy
            value: 0.975
            name: Accuracy

layoutlmv3-finetuned-invoice

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

Loss: 0.2299
Precision: 0.975
Recall: 0.975
F1: 0.975
Accuracy: 0.975

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	14.29	100	0.1616	0.975	0.975	0.975	0.975
No log	28.57	200	0.1909	0.975	0.975	0.975	0.975
No log	42.86	300	0.2046	0.975	0.975	0.975	0.975
No log	57.14	400	0.2134	0.975	0.975	0.975	0.975
0.1239	71.43	500	0.2299	0.975	0.975	0.975	0.975
0.1239	85.71	600	0.2309	0.975	0.975	0.975	0.975
0.1239	100.0	700	0.2342	0.975	0.975	0.975	0.975
0.1239	114.29	800	0.2407	0.975	0.975	0.975	0.975
0.1239	128.57	900	0.2428	0.975	0.975	0.975	0.975
0.0007	142.86	1000	0.2449	0.975	0.975	0.975	0.975
0.0007	157.14	1100	0.2465	0.975	0.975	0.975	0.975
0.0007	171.43	1200	0.2488	0.975	0.975	0.975	0.975
0.0007	185.71	1300	0.2515	0.975	0.975	0.975	0.975
0.0007	200.0	1400	0.2525	0.975	0.975	0.975	0.975
0.0004	214.29	1500	0.2540	0.975	0.975	0.975	0.975
0.0004	228.57	1600	0.2557	0.975	0.975	0.975	0.975
0.0004	242.86	1700	0.2564	0.975	0.975	0.975	0.975
0.0004	257.14	1800	0.2570	0.975	0.975	0.975	0.975
0.0004	271.43	1900	0.2573	0.975	0.975	0.975	0.975
0.0003	285.71	2000	0.2574	0.975	0.975	0.975	0.975

Framework versions

Transformers 4.23.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1