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: 0.9243884358784284
          - name: Recall
            type: recall
            value: 0.9333832335329342
          - name: F1
            type: f1
            value: 0.9288640595903166
          - name: Accuracy
            type: accuracy
            value: 0.9363327674023769

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: 0.3467
Precision: 0.9244
Recall: 0.9334
F1: 0.9289
Accuracy: 0.9363

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	4.17	250	0.5174	0.8469	0.8735	0.8600	0.8790
0.5511	8.33	500	0.3975	0.8999	0.9147	0.9072	0.9194
0.5511	12.5	750	0.3872	0.9015	0.9184	0.9099	0.9189
0.1802	16.67	1000	0.3416	0.9180	0.9296	0.9238	0.9338
0.1802	20.83	1250	0.3311	0.9159	0.9289	0.9223	0.9359
0.0836	25.0	1500	0.3457	0.9192	0.9281	0.9236	0.9334
0.0836	29.17	1750	0.3347	0.9202	0.9319	0.9260	0.9291
0.0473	33.33	2000	0.3677	0.9194	0.9304	0.9249	0.9253
0.0473	37.5	2250	0.3433	0.9279	0.9341	0.9310	0.9376
0.0342	41.67	2500	0.3467	0.9244	0.9334	0.9289	0.9363

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3