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# distilroberta-mbfc-bias

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the Proppy dataset, using political bias from mediabiasfactcheck.com as labels.

It achieves the following results on the evaluation set:
- Loss: 1.4130
- Acc: 0.6348

## Training and evaluation data

The training data used is the [proppy corpus](https://zenodo.org/record/3271522). Articles are labeled for political bias using the political bias of the source publication, as scored by mediabiasfactcheck.com. See [Proppy: Organizing the News Based on Their Propagandistic Content](https://propaganda.qcri.org/papers/elsarticle-template.pdf) for details.

To create a more balanced training set, common labels are downsampled to have a maximum of 2000 articles. The resulting label distribution in the training data is as follows:

```
extremeright     689
leastbiased     2000
left             783
leftcenter      2000
right           1260
rightcenter     1418
unknown         2000
```

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9493        | 1.0   | 514  | 1.2765          | 0.4730 |
| 0.7376        | 2.0   | 1028 | 1.0003          | 0.5812 |
| 0.6702        | 3.0   | 1542 | 1.1294          | 0.5631 |
| 0.6161        | 4.0   | 2056 | 1.0439          | 0.6058 |
| 0.4934        | 5.0   | 2570 | 1.1196          | 0.6028 |
| 0.4558        | 6.0   | 3084 | 1.0993          | 0.5977 |
| 0.4717        | 7.0   | 3598 | 1.0308          | 0.6373 |
| 0.3961        | 8.0   | 4112 | 1.1291          | 0.6234 |
| 0.3829        | 9.0   | 4626 | 1.1554          | 0.6316 |
| 0.3442        | 10.0  | 5140 | 1.1548          | 0.6465 |
| 0.2505        | 11.0  | 5654 | 1.3605          | 0.6169 |
| 0.2105        | 12.0  | 6168 | 1.3310          | 0.6297 |
| 0.262         | 13.0  | 6682 | 1.2706          | 0.6383 |
| 0.2031        | 14.0  | 7196 | 1.3658          | 0.6378 |
| 0.2021        | 15.0  | 7710 | 1.4130          | 0.6348 |


### Framework versions

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

{"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-mbfc-bias", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer license:other autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us 

distilroberta-mbfc-bias
=======================


This model is a fine-tuned version of distilroberta-base on the Proppy dataset, using political bias from URL as labels.


It achieves the following results on the evaluation set:


* Loss: 1.4130
* Acc: 0.6348


Training and evaluation data
----------------------------


The training data used is the proppy corpus. Articles are labeled for political bias using the political bias of the source publication, as scored by URL. See Proppy: Organizing the News Based on Their Propagandistic Content for details.


To create a more balanced training set, common labels are downsampled to have a maximum of 2000 articles. The resulting label distribution in the training data is as follows:


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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



### Framework versions


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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 ### Framework versions


* Transformers 4.11.2
* Pytorch 1.7.1
* Datasets 1.11.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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 ### Framework versions


* Transformers 4.11.2
* Pytorch 1.7.1
* Datasets 1.11.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-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### Framework versions


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