metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - format_dataset
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: reciept-model-2500
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: format_dataset
          type: format_dataset
          config: assesment dataset
          split: test
          args: assesment dataset
        metrics:
          - name: Precision
            type: precision
            value: 0.9673366834170855
          - name: Recall
            type: recall
            value: 0.9625
          - name: F1
            type: f1
            value: 0.9649122807017544
          - name: Accuracy
            type: accuracy
            value: 0.9993105033325672

reciept-model-2500

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

Loss: 0.0043
Precision: 0.9673
Recall: 0.9625
F1: 0.9649
Accuracy: 0.9993

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: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.62	100	0.0150	0.8575	0.8725	0.8649	0.9972
No log	1.25	200	0.0075	0.8756	0.9325	0.9031	0.9979
No log	1.88	300	0.0154	0.8744	0.8875	0.8809	0.9973
No log	2.5	400	0.0118	0.8881	0.9525	0.9192	0.9982
0.0029	3.12	500	0.0091	0.9158	0.925	0.9204	0.9983
0.0029	3.75	600	0.0167	0.8720	0.9025	0.8870	0.9975
0.0029	4.38	700	0.0092	0.9183	0.9275	0.9229	0.9983
0.0029	5.0	800	0.0113	0.8843	0.9175	0.9006	0.9979
0.0029	5.62	900	0.0106	0.9349	0.8975	0.9158	0.9982
0.0017	6.25	1000	0.0043	0.9673	0.9625	0.9649	0.9993
0.0017	6.88	1100	0.0044	0.9602	0.965	0.9626	0.9993
0.0017	7.5	1200	0.0118	0.9246	0.92	0.9223	0.9982
0.0017	8.12	1300	0.0067	0.9406	0.95	0.9453	0.9988
0.0017	8.75	1400	0.0083	0.9409	0.955	0.9479	0.9989
0.001	9.38	1500	0.0060	0.9495	0.94	0.9447	0.9988
0.001	10.0	1600	0.0078	0.9369	0.9275	0.9322	0.9985
0.001	10.62	1700	0.0093	0.9248	0.9525	0.9384	0.9986
0.001	11.25	1800	0.0097	0.9062	0.9425	0.9240	0.9983
0.001	11.88	1900	0.0100	0.9098	0.9325	0.9210	0.9982
0.0006	12.5	2000	0.0111	0.9113	0.925	0.9181	0.9981
0.0006	13.12	2100	0.0107	0.9275	0.9275	0.9275	0.9983
0.0006	13.75	2200	0.0105	0.9279	0.9325	0.9302	0.9984
0.0006	14.38	2300	0.0109	0.9325	0.9325	0.9325	0.9985
0.0006	15.0	2400	0.0109	0.9325	0.9325	0.9325	0.9985
0.0003	15.62	2500	0.0109	0.9325	0.9325	0.9325	0.9985

Framework versions

Transformers 4.38.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1