models
This model is a fine-tuned version of microsoft/layoutlmv3-base on the data_loader dataset. It achieves the following results on the evaluation set:
- Loss: 0.1595
- Precision: 0.8940
- Recall: 0.9169
- F1: 0.9053
- Accuracy: 0.9744
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 | 2.5 | 100 | 0.1926 | 0.7730 | 0.8274 | 0.7993 | 0.9452 |
No log | 5.0 | 200 | 0.1342 | 0.8285 | 0.8708 | 0.8491 | 0.9583 |
No log | 7.5 | 300 | 0.1217 | 0.8758 | 0.9015 | 0.8885 | 0.9693 |
No log | 10.0 | 400 | 0.1157 | 0.9082 | 0.9233 | 0.9157 | 0.9769 |
0.15 | 12.5 | 500 | 0.1310 | 0.9011 | 0.9092 | 0.9052 | 0.9744 |
0.15 | 15.0 | 600 | 0.1583 | 0.8682 | 0.9015 | 0.8846 | 0.9693 |
0.15 | 17.5 | 700 | 0.1628 | 0.8867 | 0.9105 | 0.8984 | 0.9724 |
0.15 | 20.0 | 800 | 0.1594 | 0.8945 | 0.9220 | 0.9081 | 0.9749 |
0.15 | 22.5 | 900 | 0.1579 | 0.8940 | 0.9169 | 0.9053 | 0.9744 |
0.0047 | 25.0 | 1000 | 0.1595 | 0.8940 | 0.9169 | 0.9053 | 0.9744 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
microsoft/layoutlmv3-baseEvaluation results
- Precision on data_loadertest set self-reported0.894
- Recall on data_loadertest set self-reported0.917
- F1 on data_loadertest set self-reported0.905
- Accuracy on data_loadertest set self-reported0.974