trainer

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: 0.6671
• Accuracy: {'accuracy': 0.6136936111747194}

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: 20
• eval_batch_size: 20
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6681	1.0	1020	0.6671	{'accuracy': 0.6136936111747194}
0.667	2.0	2040	0.6671	{'accuracy': 0.6136936111747194}
0.6697	3.0	3060	0.6692	{'accuracy': 0.6136936111747194}
0.6663	4.0	4080	0.6673	{'accuracy': 0.6136936111747194}
0.6655	5.0	5100	0.6671	{'accuracy': 0.6136936111747194}
0.669	6.0	6120	0.6671	{'accuracy': 0.6136936111747194}
0.6667	7.0	7140	0.6671	{'accuracy': 0.6136936111747194}
0.6684	8.0	8160	0.6671	{'accuracy': 0.6136936111747194}
0.6651	9.0	9180	0.6671	{'accuracy': 0.6136936111747194}
0.6669	10.0	10200	0.6671	{'accuracy': 0.6136936111747194}

Framework versions

• Transformers 4.39.3
• Pytorch 2.2.2+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2