
# distilroberta-base-finetuned-fake-news-english

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the [fake-and-real news](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0020
- Accuracy: 0.9997
- F1: 0.9997
- Precision: 0.9994
- Recall: 1.0
- Auc: 0.9997

## Intended uses & limitations

The model may not work with the articles over 512 tokens after preprocessing as the model's context is restricted to a maximum of 512 tokens in the sequence.

## Training and evaluation data

The [fake-and-real news](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) dataset contains a total of 44,898 annotated articles with 21,417 real and 23,481 fake. The dataset was stratified split into train, validation, and test subsets with a proportion of 60:20:20 respectively. The model was fine-tuned on the train subset and evaluated on validation and test subsets.

| Split      | # examples |
|:----------:|:----------:|
| train      | 17959      |
| validation | 13469      |
| test       | 13470      |

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 224
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.251         | 0.36  | 200  | 0.0030          | 0.9996   | 0.9995 | 0.9995    | 0.9995 | 0.9996 |
| 0.0022        | 0.71  | 400  | 0.0012          | 0.9998   | 0.9998 | 0.9995    | 1.0    | 0.9998 |
| 0.0013        | 1.07  | 600  | 0.0001          | 1.0      | 1.0    | 1.0       | 1.0    | 1.0    |
| 0.0004        | 1.43  | 800  | 0.0015          | 0.9997   | 0.9997 | 0.9994    | 1.0    | 0.9997 |
| 0.0013        | 1.78  | 1000 | 0.0020          | 0.9997   | 0.9997 | 0.9994    | 1.0    | 0.9997 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.0

{"language": "en", "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "Wisconsin has not counted more votes than it has registered voters. This tweet is comparing the vote count from 2020 with the number of registered voters from 2018. When we take a look at Wisconsin\u2019s current total of registered voters, we see that there is nothing fraudulent about the state\u2019s count.", "example_title": "fake"}, {"text": "Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States.", "example_title": "real"}], "model-index": [{"name": "distilroberta-base-finetuned-fake-news-english", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilroberta-base-finetuned-fake-news-english
==============================================


This model is a fine-tuned version of distilroberta-base on the fake-and-real news dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0020
* Accuracy: 0.9997
* F1: 0.9997
* Precision: 0.9994
* Recall: 1.0
* Auc: 0.9997


Intended uses & limitations
---------------------------


The model may not work with the articles over 512 tokens after preprocessing as the model's context is restricted to a maximum of 512 tokens in the sequence.


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


The fake-and-real news dataset contains a total of 44,898 annotated articles with 21,417 real and 23,481 fake. The dataset was stratified split into train, validation, and test subsets with a proportion of 60:20:20 respectively. The model was fine-tuned on the train subset and evaluated on validation and test subsets.



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
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 224
* num\_epochs: 2


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.12.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 224
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.12.0
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 224
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.12.0
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 224
* num\_epochs: 2### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.12.0