metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - ls-generated4
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-invoice-model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: ls-generated4
          type: ls-generated4
          config: invoice
          split: test
          args: invoice
        metrics:
          - name: Precision
            type: precision
            value: 0.9185733512786003
          - name: Recall
            type: recall
            value: 0.9375
          - name: F1
            type: f1
            value: 0.9279401767505099
          - name: Accuracy
            type: accuracy
            value: 0.9536870503597122

layoutlmv3-invoice-model

This model is a fine-tuned version of microsoft/layoutlmv3-base on the ls-generated4 dataset. It achieves the following results on the evaluation set:

Loss: 0.3694
Precision: 0.9186
Recall: 0.9375
F1: 0.9279
Accuracy: 0.9537

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: 5e-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	0.85	100	0.7836	0.5238	0.5982	0.5585	0.7680
No log	1.69	200	0.4954	0.6888	0.7479	0.7172	0.8422
No log	2.54	300	0.3483	0.7807	0.8462	0.8121	0.9040
No log	3.39	400	0.3200	0.8113	0.8654	0.8375	0.9125
0.5923	4.24	500	0.2775	0.8593	0.8853	0.8721	0.9319
0.5923	5.08	600	0.2674	0.8700	0.9052	0.8872	0.9377
0.5923	5.93	700	0.2766	0.8739	0.9135	0.8932	0.9386
0.5923	6.78	800	0.2641	0.8879	0.9190	0.9031	0.9472
0.5923	7.63	900	0.2893	0.9094	0.9238	0.9165	0.9447
0.0802	8.47	1000	0.3369	0.9145	0.9258	0.9201	0.9465
0.0802	9.32	1100	0.3037	0.9043	0.9341	0.9189	0.9505
0.0802	10.17	1200	0.3510	0.9032	0.9231	0.9130	0.9472
0.0802	11.02	1300	0.3224	0.9138	0.9251	0.9195	0.9501
0.0802	11.86	1400	0.3873	0.9133	0.9265	0.9199	0.9456
0.0198	12.71	1500	0.3786	0.9120	0.9327	0.9222	0.9492
0.0198	13.56	1600	0.3807	0.9050	0.9293	0.9170	0.9469
0.0198	14.41	1700	0.3664	0.9088	0.9313	0.9199	0.9510
0.0198	15.25	1800	0.3582	0.9152	0.9341	0.9245	0.9521
0.0198	16.1	1900	0.3736	0.9198	0.9368	0.9282	0.9528
0.007	16.95	2000	0.3694	0.9186	0.9375	0.9279	0.9537

Framework versions

Transformers 4.35.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1