metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - drug_bill_layoutv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-vinv2
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: drug_bill_layoutv3
          type: drug_bill_layoutv3
          config: Vin_Drug_Bill
          split: train
          args: Vin_Drug_Bill
        metrics:
          - name: Precision
            type: precision
            value: 1
          - name: Recall
            type: recall
            value: 1
          - name: F1
            type: f1
            value: 1
          - name: Accuracy
            type: accuracy
            value: 1

layoutlmv3-finetuned-vinv2

This model is a fine-tuned version of microsoft/layoutlmv3-base on the drug_bill_layoutv3 dataset. It achieves the following results on the evaluation set:

Loss: 0.0001
Precision: 1.0
Recall: 1.0
F1: 1.0
Accuracy: 1.0

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: 5
eval_batch_size: 5
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 3000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.33	250	0.0025	0.9994	0.9994	0.9994	0.9998
0.0662	2.66	500	0.0004	1.0	1.0	1.0	1.0
0.0662	3.99	750	0.0003	1.0	1.0	1.0	1.0
0.0111	5.32	1000	0.0002	1.0	1.0	1.0	1.0
0.0111	6.65	1250	0.0001	1.0	1.0	1.0	1.0
0.0126	7.98	1500	0.0001	1.0	1.0	1.0	1.0
0.0126	9.31	1750	0.0001	1.0	1.0	1.0	1.0
0.0032	10.64	2000	0.0001	1.0	1.0	1.0	1.0
0.0032	11.97	2250	0.0001	1.0	1.0	1.0	1.0
0.0011	13.3	2500	0.0001	1.0	1.0	1.0	1.0
0.0011	14.63	2750	0.0001	1.0	1.0	1.0	1.0
0.0002	15.96	3000	0.0001	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.2