metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - cord-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cord_100
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cord-layoutlmv3
          type: cord-layoutlmv3
          config: cord
          split: test
          args: cord
        metrics:
          - name: Precision
            type: precision
            value: 0.953125
          - name: Recall
            type: recall
            value: 0.9588323353293413
          - name: F1
            type: f1
            value: 0.9559701492537314
          - name: Accuracy
            type: accuracy
            value: 0.965195246179966

layoutlmv3-finetuned-cord_100

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

Loss: 0.1913
Precision: 0.9531
Recall: 0.9588
F1: 0.9560
Accuracy: 0.9652

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: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.56	250	1.0033	0.7434	0.7957	0.7686	0.8060
1.3714	3.12	500	0.5413	0.8534	0.8757	0.8644	0.8769
1.3714	4.69	750	0.3792	0.9013	0.9162	0.9087	0.9219
0.3763	6.25	1000	0.2743	0.9333	0.9431	0.9382	0.9457
0.3763	7.81	1250	0.2404	0.9313	0.9439	0.9375	0.9495
0.2026	9.38	1500	0.2479	0.9325	0.9409	0.9367	0.9431
0.2026	10.94	1750	0.2001	0.9338	0.9499	0.9417	0.9559
0.1349	12.5	2000	0.2102	0.9407	0.9499	0.9453	0.9571
0.1349	14.06	2250	0.1961	0.9560	0.9603	0.9582	0.9648
0.104	15.62	2500	0.1913	0.9531	0.9588	0.9560	0.9652

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.0
Tokenizers 0.13.3