---
license: other
tags:
- generated_from_trainer
model-index:
- name: distilroberta-proppy
  results: []
---


# distilroberta-proppy

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the proppy corpus.
It achieves the following results on the evaluation set:
- Loss: 0.1838
- Acc: 0.9269

## Training and evaluation data

The training data is the [proppy corpus](https://zenodo.org/record/3271522). See [Proppy: Organizing the News
Based on Their Propagandistic Content](https://propaganda.qcri.org/papers/elsarticle-template.pdf) for details.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3179        | 1.0   | 732   | 0.2032          | 0.9146 |
| 0.2933        | 2.0   | 1464  | 0.2026          | 0.9206 |
| 0.2938        | 3.0   | 2196  | 0.1849          | 0.9252 |
| 0.3429        | 4.0   | 2928  | 0.1983          | 0.9221 |
| 0.2608        | 5.0   | 3660  | 0.2310          | 0.9106 |
| 0.2562        | 6.0   | 4392  | 0.1826          | 0.9270 |
| 0.2785        | 7.0   | 5124  | 0.1954          | 0.9228 |
| 0.307         | 8.0   | 5856  | 0.2056          | 0.9200 |
| 0.28          | 9.0   | 6588  | 0.1843          | 0.9259 |
| 0.2794        | 10.0  | 7320  | 0.1782          | 0.9299 |
| 0.2868        | 11.0  | 8052  | 0.1907          | 0.9242 |
| 0.2789        | 12.0  | 8784  | 0.2031          | 0.9216 |
| 0.2827        | 13.0  | 9516  | 0.1976          | 0.9229 |
| 0.2795        | 14.0  | 10248 | 0.1866          | 0.9255 |
| 0.2895        | 15.0  | 10980 | 0.1838          | 0.9269 |


### Framework versions

- Transformers 4.11.2
- Pytorch 1.7.1
- Datasets 1.11.0
- Tokenizers 0.10.3