---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: imdb-spoiler-distilbert
  results: []
widget:
- text: This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three.
---

<!-- 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. -->

# imdb-spoiler-distilbert

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [imdb-spoiler](https://huggingface.co/datasets/bhavyagiri/imdb-spoiler) dataset for classification.
[imdb-spoiler](https://huggingface.co/datasets/bhavyagiri/imdb-spoiler) is a subset of a [large-dataset](https://www.kaggle.com/datasets/rmisra/imdb-spoiler-dataset) for classifying whether a movie review is a spoiler or not.

It achieves the following results on the evaluation set:
- Accuracy: 0.7794

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5139        | 0.35  | 500  | 0.4960          | 0.7761   |
| 0.4732        | 0.7   | 1000 | 0.4822          | 0.7794   |


### Framework versions

- Transformers 4.39.2
- Pytorch 2.2.2+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2