just-nce

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: 1.0338
• Able: {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 6}
• Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
• Ext: {'precision': 0.75, 'recall': 0.9, 'f1': 0.8181818181818182, 'number': 10}
• Mage: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
• Ub heading: {'precision': 0.9090909090909091, 'recall': 0.625, 'f1': 0.7407407407407406, 'number': 16}
• Overall Precision: 0.6571
• Overall Recall: 0.575
• Overall F1: 0.6133
• Overall Accuracy: 0.68

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Able	Eading	Ext	Mage	Ub heading	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4724	14.29	100	1.0338	{'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}	{'precision': 0.75, 'recall': 0.9, 'f1': 0.8181818181818182, 'number': 10}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}	{'precision': 0.9090909090909091, 'recall': 0.625, 'f1': 0.7407407407407406, 'number': 16}	0.6571	0.575	0.6133	0.68

Framework versions

• Transformers 4.26.1
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.2