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layoutlmv3-base-ner
This model is a fine-tuned version of microsoft/layoutlmv3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1562
- Footer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
- Able: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
- Aption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Ext: {'precision': 0.06153846153846154, 'recall': 0.4, 'f1': 0.10666666666666667, 'number': 10}
- Overall Precision: 0.0310
- Overall Recall: 0.1739
- Overall F1: 0.0526
- Overall Accuracy: 0.8882
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: 3e-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
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Footer | Header | Able | Aption | Ext | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.0796 | 1.0 | 5 | 1.4462 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.05063291139240506, 'recall': 0.4, 'f1': 0.0898876404494382, 'number': 10} | 0.0255 | 0.1739 | 0.0444 | 0.8518 |
| 1.2478 | 2.0 | 10 | 1.1562 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.06153846153846154, 'recall': 0.4, 'f1': 0.10666666666666667, 'number': 10} | 0.0310 | 0.1739 | 0.0526 | 0.8882 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2
