pytorch roberta text-classification transformers generated_from_trainer license:other model-index
---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-clickbait
  results: []
---

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# distilroberta-clickbait

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on a dataset of headlines.
It achieves the following results on the evaluation set:
- Loss: 0.0268
- Acc: 0.9963

## Training and evaluation data

The following data sources were used:
* 32k headlines classified as clickbait/not-clickbait from [kaggle](https://www.kaggle.com/amananandrai/clickbait-dataset)
* A dataset of headlines from https://github.com/MotiBaadror/Clickbait-Detection

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Acc    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0195        | 1.0   | 981  | 0.0192          | 0.9954 |
| 0.0026        | 2.0   | 1962 | 0.0172          | 0.9963 |
| 0.0031        | 3.0   | 2943 | 0.0275          | 0.9945 |
| 0.0003        | 4.0   | 3924 | 0.0268          | 0.9963 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
