BWayne/LILT_mr

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