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pbeyens/
kantinewinkel-repo

Name: kantinewinkel-repo
Availability: InStock

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kantinewinkel-repo

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

Loss: 0.0083
Cash: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}
Date: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44}
Subtotal: {'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}
Total: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}
Overall Precision: 0.9886
Overall Recall: 1.0
Overall F1: 0.9943
Overall Accuracy: 0.9991

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: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cash	Date	Subtotal	Total	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0476	8.7	200	0.0083	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44}	{'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	0.9886	1.0	0.9943	0.9991
0.0003	17.39	400	0.0092	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44}	{'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	0.9886	1.0	0.9943	0.9991
0.0005	26.09	600	0.0080	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44}	{'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	0.9886	1.0	0.9943	0.9991
0.0	34.78	800	0.0082	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44}	{'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	0.9886	1.0	0.9943	0.9991
0.0001	43.48	1000	0.0081	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 44}	{'precision': 0.95, 'recall': 1.0, 'f1': 0.9743589743589743, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 19}	0.9886	1.0	0.9943	0.9991

Framework versions

Transformers 4.29.2
Pytorch 1.13.1
Datasets 2.12.0
Tokenizers 0.13.2

pbeyens/kantinewinkel-repo

kantinewinkel-repo

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

pbeyens/
kantinewinkel-repo