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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: 0.0044
- 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 | 0.1244 | 0.964 | 0.9777 | 0.9708 | 0.9958 |
| No log | 4.0 | 200 | 0.0238 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| No log | 6.0 | 300 | 0.0183 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| No log | 8.0 | 400 | 0.0129 | 0.972 | 0.9858 | 0.9789 | 0.9971 |
| 0.1357 | 10.0 | 500 | 0.0075 | 0.9878 | 0.9878 | 0.9878 | 0.9985 |
| 0.1357 | 12.0 | 600 | 0.0058 | 0.9959 | 0.9899 | 0.9929 | 0.9989 |
| 0.1357 | 14.0 | 700 | 0.0039 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.1357 | 16.0 | 800 | 0.0048 | 0.9980 | 0.9959 | 0.9970 | 0.9996 |
| 0.1357 | 18.0 | 900 | 0.0023 | 0.9980 | 0.9980 | 0.9980 | 0.9998 |
| 0.006 | 20.0 | 1000 | 0.0044 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.006 | 22.0 | 1100 | 0.0042 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.006 | 24.0 | 1200 | 0.0038 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.006 | 26.0 | 1300 | 0.0032 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.006 | 28.0 | 1400 | 0.0036 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0026 | 30.0 | 1500 | 0.0036 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0026 | 32.0 | 1600 | 0.0037 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0026 | 34.0 | 1700 | 0.0036 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0026 | 36.0 | 1800 | 0.0037 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0026 | 38.0 | 1900 | 0.0037 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
| 0.0019 | 40.0 | 2000 | 0.0037 | 0.9960 | 0.9980 | 0.9970 | 0.9996 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
