metadata

tags:
  - generated_from_trainer
datasets:
  - cord
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cord
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cord
          type: cord
          args: cord
        metrics:
          - name: Precision
            type: precision
            value: 0.9619686800894854
          - name: Recall
            type: recall
            value: 0.9655688622754491
          - name: F1
            type: f1
            value: 0.9637654090399701
          - name: Accuracy
            type: accuracy
            value: 0.9681663837011885

layoutlmv3-finetuned-cord

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

Loss: 0.1845
Precision: 0.9620
Recall: 0.9656
F1: 0.9638
Accuracy: 0.9682

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	100	0.5257	0.8223	0.8555	0.8386	0.8710
No log	4.0	200	0.3200	0.9118	0.9281	0.9199	0.9317
No log	6.0	300	0.2449	0.9298	0.9424	0.9361	0.9465
No log	8.0	400	0.1923	0.9472	0.9536	0.9504	0.9597
0.4328	10.0	500	0.1857	0.9591	0.9656	0.9623	0.9682
0.4328	12.0	600	0.2073	0.9597	0.9618	0.9607	0.9656
0.4328	14.0	700	0.1804	0.9634	0.9663	0.9649	0.9703
0.4328	16.0	800	0.1882	0.9634	0.9648	0.9641	0.9665
0.4328	18.0	900	0.1800	0.9619	0.9648	0.9634	0.9677
0.0318	20.0	1000	0.1845	0.9620	0.9656	0.9638	0.9682

Framework versions

Transformers 4.19.0.dev0
Pytorch 1.11.0+cu113
Datasets 2.0.0
Tokenizers 0.11.6