mydataset-repo

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

• Loss: 0.0000
• Total-str: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
• Total-val: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
• 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: 16
• eval_batch_size: 16
• 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	Total-str	Total-val	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0674	28.57	200	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0001	57.14	400	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	85.71	600	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	114.29	800	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	142.86	1000	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	171.43	1200	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	200.0	1400	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	228.57	1600	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	257.14	1800	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	285.71	2000	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	314.29	2200	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	342.86	2400	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	371.43	2600	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	400.0	2800	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	428.57	3000	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	457.14	3200	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	485.71	3400	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	514.29	3600	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	542.86	3800	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	571.43	4000	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	600.0	4200	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	628.57	4400	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	657.14	4600	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	685.71	4800	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0
0.0	714.29	5000	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.29.2
• Pytorch 1.13.1
• Datasets 2.12.0
• Tokenizers 0.13.2