metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - funsd
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-funsd
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: funsd
          type: funsd
          config: funsd
          split: test
          args: funsd
        metrics:
          - name: Precision
            type: precision
            value: 0.7998102466793169
          - name: Recall
            type: recall
            value: 0.8375558867362146
          - name: F1
            type: f1
            value: 0.8182479980587235
          - name: Accuracy
            type: accuracy
            value: 0.826102460477832

layoutlmv3-finetuned-funsd

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: 1.0068
Precision: 0.7998
Recall: 0.8376
F1: 0.8182
Accuracy: 0.8261

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: 2
eval_batch_size: 2
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	3.33	250	0.5828	0.7015	0.8033	0.7490	0.8022
0.6702	6.67	500	0.5765	0.7499	0.8073	0.7775	0.8253
0.6702	10.0	750	0.7082	0.7755	0.8236	0.7988	0.8160
0.1797	13.33	1000	0.7819	0.7807	0.8366	0.8077	0.8256
0.1797	16.67	1250	0.8199	0.7997	0.8311	0.8151	0.8227
0.0745	20.0	1500	0.9025	0.7943	0.8286	0.8111	0.8231
0.0745	23.33	1750	0.9159	0.7941	0.8470	0.8197	0.8248
0.041	26.67	2000	1.0012	0.7989	0.8385	0.8182	0.8210
0.041	30.0	2250	0.9852	0.8024	0.8450	0.8231	0.8301
0.0246	33.33	2500	1.0068	0.7998	0.8376	0.8182	0.8261

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3