fiction_predictor

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0011
• Accuracy: 1.0
• F1: 1.0
• Precision: 1.0
• Recall: 1.0

Model description

This model uses data from jennifee/HW1-aug-text-dataset and predicts whether a book is fiction or not based on review.

Intended uses & limitations

This model was constructed as a practice in training for classification of text datasets.

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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0045	1.0	128	0.0228	0.9922	0.9922	0.9923	0.9922
0.0017	2.0	256	0.0012	1.0	1.0	1.0	1.0
0.001	3.0	384	0.0007	1.0	1.0	1.0	1.0
0.0007	4.0	512	0.0005	1.0	1.0	1.0	1.0
0.0006	5.0	640	0.0005	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.56.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.0