metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - cne-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cne_nvidia_100
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cne-layoutlmv3
          type: cne-layoutlmv3
          config: cne-dataset
          split: test
          args: cne-dataset
        metrics:
          - name: Precision
            type: precision
            value: 0.9950738916256158
          - name: Recall
            type: recall
            value: 0.9950738916256158
          - name: F1
            type: f1
            value: 0.9950738916256159
          - name: Accuracy
            type: accuracy
            value: 0.9992716678805535

layoutlmv3-finetuned-cne_nvidia_100

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

Loss: 0.0064
Precision: 0.9951
Recall: 0.9951
F1: 0.9951
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: 3
eval_batch_size: 3
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	7.81	250	0.0143	0.9951	0.9951	0.9951	0.9993
0.1596	15.62	500	0.0085	0.9951	0.9951	0.9951	0.9993
0.1596	23.44	750	0.0074	0.9951	0.9951	0.9951	0.9993
0.0195	31.25	1000	0.0068	0.9951	0.9951	0.9951	0.9993
0.0195	39.06	1250	0.0067	0.9951	0.9951	0.9951	0.9993
0.008	46.88	1500	0.0067	0.9951	0.9951	0.9951	0.9993
0.008	54.69	1750	0.0064	0.9951	0.9951	0.9951	0.9993
0.0034	62.5	2000	0.0063	0.9951	0.9951	0.9951	0.9993
0.0034	70.31	2250	0.0063	0.9951	0.9951	0.9951	0.9993
0.0023	78.12	2500	0.0064	0.9951	0.9951	0.9951	0.9993

Framework versions

Transformers 4.31.0
Pytorch 2.0.1
Datasets 2.14.3
Tokenizers 0.13.3