Hugging Face's logo

pradyumna0
/
layoutlmv3-finetuned-invoice

Token Classification
Transformers
TensorBoard
Safetensors
layoutlmv3
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
layoutlmv3-finetuned-invoice / README.md
pradyumna0's picture
pradyumna0
End of training
981e145
preview code
|
raw history blame contribute delete
No virus
3.84 kB
metadata
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
  - generated_from_trainer
datasets:
  - generated
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-invoice
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: generated
          type: generated
          config: sroie
          split: test
          args: sroie
        metrics:
          - name: Precision
            type: precision
            value: 0.9959514170040485
          - name: Recall
            type: recall
            value: 0.9979716024340771
          - name: F1
            type: f1
            value: 0.9969604863221885
          - name: Accuracy
            type: accuracy
            value: 0.9995786812723826

layoutlmv3-finetuned-invoice

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

  • Loss: 0.0042
  • Precision: 0.9960
  • Recall: 0.9980
  • F1: 0.9970
  • Accuracy: 0.9996

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: 2000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 2.0 100 0.1164 0.902 0.9148 0.9084 0.9897
No log 4.0 200 0.0262 0.972 0.9858 0.9789 0.9971
No log 6.0 300 0.0157 0.972 0.9858 0.9789 0.9971
No log 8.0 400 0.0097 0.9877 0.9797 0.9837 0.9979
0.1294 10.0 500 0.0065 0.9939 0.9959 0.9949 0.9994
0.1294 12.0 600 0.0042 0.9960 0.9980 0.9970 0.9996
0.1294 14.0 700 0.0048 0.9960 0.9980 0.9970 0.9996
0.1294 16.0 800 0.0047 0.9960 0.9980 0.9970 0.9996
0.1294 18.0 900 0.0045 0.9960 0.9980 0.9970 0.9996
0.0051 20.0 1000 0.0042 0.9960 0.9980 0.9970 0.9996
0.0051 22.0 1100 0.0041 0.9960 0.9980 0.9970 0.9996
0.0051 24.0 1200 0.0041 0.9960 0.9980 0.9970 0.9996
0.0051 26.0 1300 0.0040 0.9960 0.9980 0.9970 0.9996
0.0051 28.0 1400 0.0040 0.9960 0.9980 0.9970 0.9996
0.0024 30.0 1500 0.0040 0.9960 0.9980 0.9970 0.9996
0.0024 32.0 1600 0.0040 0.9960 0.9980 0.9970 0.9996
0.0024 34.0 1700 0.0039 0.9960 0.9980 0.9970 0.9996
0.0024 36.0 1800 0.0039 0.9960 0.9980 0.9970 0.9996
0.0024 38.0 1900 0.0039 0.9960 0.9980 0.9970 0.9996
0.0018 40.0 2000 0.0039 0.9960 0.9980 0.9970 0.9996

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1