<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
LILT_on7
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Able caption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62}
- Ext: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102}
- Mage caption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13}
- Ub heading: {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125}
- Overall Precision: 0.2643
- Overall Recall: 0.4112
- Overall F1: 0.3218
- Overall Accuracy: 0.2643
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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Able caption | Eading | Ext | Mage caption | Ub heading | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.0142 | 0.44 | 500 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643 | 0.4112 | 0.3218 | 0.2643 |
| 1.0228 | 0.89 | 1000 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643 | 0.4112 | 0.3218 | 0.2643 |
| 1.0299 | 1.33 | 1500 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643 | 0.4112 | 0.3218 | 0.2643 |
| 1.0233 | 1.78 | 2000 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643 | 0.4112 | 0.3218 | 0.2643 |
| 0.9924 | 2.22 | 2500 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643 | 0.4112 | 0.3218 | 0.2643 |
| 1.0081 | 2.67 | 3000 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643 | 0.4112 | 0.3218 | 0.2643 |
| 0.9836 | 3.11 | 3500 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643 | 0.4112 | 0.3218 | 0.2643 |
| 0.9997 | 3.56 | 4000 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643 | 0.4112 | 0.3218 | 0.2643 |
| 0.984 | 4.0 | 4500 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643 | 0.4112 | 0.3218 | 0.2643 |
| 0.9889 | 4.44 | 5000 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 62} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 102} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.2642706131078224, 'recall': 1.0, 'f1': 0.41806020066889626, 'number': 125} | 0.2643 | 0.4112 | 0.3218 | 0.2643 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
