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our-dataset
This model is a fine-tuned version of microsoft/layoutlmv3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0167
- Precision: 0.7541
- Recall: 0.6479
- F1: 0.6970
- Accuracy: 0.7975
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 | 4.55 | 100 | 1.2849 | 0.5789 | 0.4648 | 0.5156 | 0.6456 |
| No log | 9.09 | 200 | 1.0959 | 0.6724 | 0.5493 | 0.6047 | 0.7215 |
| No log | 13.64 | 300 | 1.1048 | 0.6833 | 0.5775 | 0.6260 | 0.7342 |
| No log | 18.18 | 400 | 1.0442 | 0.7541 | 0.6479 | 0.6970 | 0.7848 |
| 0.488 | 22.73 | 500 | 1.0966 | 0.7333 | 0.6197 | 0.6718 | 0.7722 |
| 0.488 | 27.27 | 600 | 1.0650 | 0.75 | 0.6338 | 0.6870 | 0.7848 |
| 0.488 | 31.82 | 700 | 0.9722 | 0.7742 | 0.6761 | 0.7218 | 0.8101 |
| 0.488 | 36.36 | 800 | 1.0596 | 0.7541 | 0.6479 | 0.6970 | 0.7975 |
| 0.488 | 40.91 | 900 | 0.9996 | 0.7541 | 0.6479 | 0.6970 | 0.7975 |
| 0.0298 | 45.45 | 1000 | 1.0167 | 0.7541 | 0.6479 | 0.6970 | 0.7975 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 1.12.0+cu102
- Datasets 2.13.1
- Tokenizers 0.13.3
