results

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: 0.9268
• Accuracy: {'accuracy': 0.897}

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: 0.001
• train_batch_size: 5
• eval_batch_size: 5
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	200	0.3711	{'accuracy': 0.888}
No log	2.0	400	0.3744	{'accuracy': 0.891}
0.3758	3.0	600	0.5101	{'accuracy': 0.885}
0.3758	4.0	800	0.5947	{'accuracy': 0.885}
0.1658	5.0	1000	0.6976	{'accuracy': 0.88}
0.1658	6.0	1200	0.7152	{'accuracy': 0.891}
0.1658	7.0	1400	0.8370	{'accuracy': 0.893}
0.0294	8.0	1600	0.9208	{'accuracy': 0.889}
0.0294	9.0	1800	0.9238	{'accuracy': 0.893}
0.0087	10.0	2000	0.9268	{'accuracy': 0.897}

Framework versions

• PEFT 0.14.0
• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Tokenizers 0.21.0