insuff_supported_arguments

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0
• Negative: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0}
• Positive: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0}
• Accuracy: 1.0
• Macro avg: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}
• Weighted avg: {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}

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: 2e-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
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Negative	Positive	Accuracy	Macro avg	Weighted avg
0.0004	1.0	9255	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0}	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0}	1.0	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}
0.0	2.0	18510	0.0000	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0}	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0}	1.0	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}
0.0	3.0	27765	0.0	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 13620.0}	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 6960.0}	1.0	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}	{'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 20580.0}

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2