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lilt-en-funsd
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:
- Loss: 1.1164
- Answer: {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817}
- Header: {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119}
- Question: {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077}
- Overall Precision: 0.8800
- Overall Recall: 0.8892
- Overall F1: 0.8846
- Overall Accuracy: 0.8211
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1500
Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0418 | 1.34 | 200 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 |
| 0.0473 | 2.68 | 400 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 |
| 0.0444 | 4.03 | 600 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 |
| 0.0532 | 5.37 | 800 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 |
| 0.0405 | 6.71 | 1000 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 |
| 0.0383 | 8.05 | 1200 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 |
| 0.0494 | 9.4 | 1400 | 1.1164 | {'precision': 0.8957575757575758, 'recall': 0.9045287637698899, 'f1': 0.900121802679659, 'number': 817} | {'precision': 0.5645161290322581, 'recall': 0.5882352941176471, 'f1': 0.5761316872427984, 'number': 119} | {'precision': 0.904147465437788, 'recall': 0.9108635097493036, 'f1': 0.9074930619796485, 'number': 1077} | 0.8800 | 0.8892 | 0.8846 | 0.8211 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
