metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - doc_lay_net-small
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Layoutlmv3-finetuned-DocLayNet-test
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: doc_lay_net-small
          type: doc_lay_net-small
          config: DocLayNet_2022.08_processed_on_2023.01
          split: test
          args: DocLayNet_2022.08_processed_on_2023.01
        metrics:
          - name: Precision
            type: precision
            value: 0.580814717477004
          - name: Recall
            type: recall
            value: 0.6415094339622641
          - name: F1
            type: f1
            value: 0.6096551724137931
          - name: Accuracy
            type: accuracy
            value: 0.867559907240402

Layoutlmv3-finetuned-DocLayNet-test

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

Loss: 0.5326
Precision: 0.5808
Recall: 0.6415
F1: 0.6097
Accuracy: 0.8676

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
lr_scheduler_warmup_ratio: 0.1
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.499	0.37	250	0.7771	0.2079	0.2848	0.2403	0.8189
0.8163	0.73	500	0.5990	0.3611	0.5633	0.4400	0.8454
0.5933	1.1	750	0.6424	0.5527	0.6139	0.5817	0.8182
0.3731	1.46	1000	0.7426	0.5923	0.6804	0.6333	0.8282

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0