metadata
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv3-base-ner
results: []
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: 1.4071
- Footer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186}
- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373}
- Able: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100}
- Aption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148}
- Ext: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566}
- Icture: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270}
- Itle: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45}
- Ootnote: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
- Overall Precision: 0.0
- Overall Recall: 0.0
- Overall F1: 0.0
- Overall Accuracy: 0.6399
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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Footer | Header | Able | Aption | Ext | Icture | Itle | Ootnote | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.1724 | 1.0 | 1950 | 1.4537 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | 0.0 | 0.0 | 0.0 | 0.6399 |
| 1.2004 | 2.0 | 3900 | 1.4094 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | 0.0 | 0.0 | 0.0 | 0.6399 |
| 1.2026 | 3.0 | 5850 | 1.4038 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | 0.0 | 0.0 | 0.0 | 0.6399 |
| 1.2107 | 4.0 | 7800 | 1.4217 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | 0.0 | 0.0 | 0.0 | 0.6399 |
| 1.1836 | 5.0 | 9750 | 1.4071 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 186} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 373} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 100} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 148} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 566} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 270} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 45} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | 0.0 | 0.0 | 0.0 | 0.6399 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2