lilt-en-test

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the test dataset. It achieves the following results on the evaluation set:

• Loss: 0.0000
• Answer: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}
• Header: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}
• Question: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}
• Overall Precision: 1.0
• Overall Recall: 1.0
• Overall F1: 1.0
• Overall Accuracy: 1.0

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: 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: 2500
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0704	200.0	200	0.0001	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0001	400.0	400	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0001	600.0	600	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0001	800.0	800	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0	1000.0	1000	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0	1200.0	1200	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0	1400.0	1400	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0	1600.0	1600	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0	1800.0	1800	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0	2000.0	2000	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0	2200.0	2200	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0
0.0	2400.0	2400	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.1+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1