metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - funsd
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-test
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: funsd
          type: funsd
        metrics:
          - name: Precision
            type: precision
            value: 0.8972868217054264
          - name: Recall
            type: recall
            value: 0.920019870839543
          - name: F1
            type: f1
            value: 0.9085111601667893
          - name: Accuracy
            type: accuracy
            value: 0.8480922382027815

layoutlmv3-test

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

Loss: 0.8036
Precision: 0.8973
Recall: 0.9200
F1: 0.9085
Accuracy: 0.8481

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: 8
eval_batch_size: 8
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	5.26	100	0.5115	0.8071	0.8624	0.8338	0.8407
No log	10.53	200	0.4661	0.8730	0.9086	0.8905	0.8546
No log	15.79	300	0.5613	0.8914	0.9091	0.9001	0.8552
No log	21.05	400	0.6767	0.8937	0.8982	0.8959	0.8507
0.3022	26.32	500	0.7020	0.8935	0.9165	0.9049	0.8626
0.3022	31.58	600	0.7108	0.9040	0.9220	0.9129	0.8591
0.3022	36.84	700	0.7378	0.9049	0.9175	0.9112	0.8517
0.3022	42.11	800	0.7892	0.9026	0.9210	0.9117	0.8537
0.3022	47.37	900	0.8133	0.8995	0.9205	0.9099	0.8490
0.0223	52.63	1000	0.8036	0.8973	0.9200	0.9085	0.8481

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1