---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# fiction_predictor

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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