README.md

metadata

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

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

The script for training can be found here: https://github.com/huggingface/transformers/tree/main/examples/research_projects/layoutlmv3

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