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# Fake-News-Detection-Roberta

This model is a fine-tuned version of [smallbenchnlp/roberta-small](https://huggingface.co/smallbenchnlp/roberta-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0119
- Accuracy: 0.9981
- Precision: 0.9994
- Recall: 0.9966
- F1 score: 0.9980
- Auc score: 0.9980

## Model description

The model is a fake news classifier which has been fined-tuned using the small version of pre-trained RoBERTa where Label 0 indicates its a Fake News but Label 1 indicates its a Real News.

## Intended uses & limitations

To use the Inference API, please give input of word limit=70.

## Training and evaluation data

The model was trained and tested using  this dataset :https://www.kaggle.com/datasets/sadikaljarif/fake-news-detection-dataset-english

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-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: 500
- num_epochs: 0.5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 score | Auc score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:---------:|
| 0.0206        | 0.49  | 500  | 0.0119          | 0.9981   | 0.9994    | 0.9966 | 0.9980   | 0.9980    |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "model-index": [{"name": "Fake-News-Detection-Roberta", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

Fake-News-Detection-Roberta
===========================


This model is a fine-tuned version of smallbenchnlp/roberta-small on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0119
* Accuracy: 0.9981
* Precision: 0.9994
* Recall: 0.9966
* F1 score: 0.9980
* Auc score: 0.9980


Model description
-----------------


The model is a fake news classifier which has been fined-tuned using the small version of pre-trained RoBERTa where Label 0 indicates its a Fake News but Label 1 indicates its a Real News.


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


To use the Inference API, please give input of word limit=70.


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


The model was trained and tested using this dataset :URL


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 500
* num\_epochs: 0.5
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 500
* num\_epochs: 0.5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 500
* num\_epochs: 0.5
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 500
* num\_epochs: 0.5
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Tokenizers 0.13.2