metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: fiction_predictor
results: []
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