metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - sroie
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: music_layoutlmv3_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: sroie
          type: sroie
          config: discharge
          split: test
          args: discharge
        metrics:
          - name: Precision
            type: precision
            value: 0.9626865671641791
          - name: Recall
            type: recall
            value: 0.9772727272727273
          - name: F1
            type: f1
            value: 0.9699248120300752
          - name: Accuracy
            type: accuracy
            value: 0.9990407673860912

music_layoutlmv3_model

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

Loss: 0.0083
Precision: 0.9627
Recall: 0.9773
F1: 0.9699
Accuracy: 0.9990

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: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	8.33	100	0.0191	0.9338	0.9621	0.9478	0.9981
No log	16.67	200	0.0120	0.9412	0.9697	0.9552	0.9981
No log	25.0	300	0.0125	0.9412	0.9697	0.9552	0.9981
No log	33.33	400	0.0101	0.9412	0.9697	0.9552	0.9981
0.0527	41.67	500	0.0121	0.9412	0.9697	0.9552	0.9981
0.0527	50.0	600	0.0083	0.9627	0.9773	0.9699	0.9990
0.0527	58.33	700	0.0082	0.9627	0.9773	0.9699	0.9990
0.0527	66.67	800	0.0082	0.9627	0.9773	0.9699	0.9990
0.0527	75.0	900	0.0083	0.9627	0.9773	0.9699	0.9990
0.0006	83.33	1000	0.0083	0.9627	0.9773	0.9699	0.9990

Framework versions

Transformers 4.28.0.dev0
Pytorch 1.13.1+cu116
Datasets 2.2.2
Tokenizers 0.13.2