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# roberta-base-fake-news-detection

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [fake-news-detection-dataset-english](https://huggingface.co/datasets/mohammadjavadpirhadi/fake-news-detection-dataset-english) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0061
- Accuracy: 0.9992

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0073        | 1.0   | 4490  | 0.0087          | 0.9989   |
| 0.0076        | 2.0   | 8980  | 0.0062          | 0.9992   |
| 0.0094        | 3.0   | 13470 | 0.0061          | 0.9992   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["mohammadjavadpirhadi/fake-news-detection-dataset-english"], "metrics": ["accuracy"], "widget": [{"text": "As U.S. budget fight looms, Republicans flip their fiscal script [SEP] WASHINGTON (Reuters) - The head of a conservative Republican faction in the U.S. Congress, who voted this month for a huge expansion of the national debt to pay for tax cuts, called himself a \u201cfiscal conservative\u201d on Sunday and urged budget restraint in 2018. In keeping with a sharp pivot under way among Republicans, U.S. Representative Mark Meadows, speaking on CBS\u2019 \u201cFace the Nation,\u201d drew a hard line on federal spending, which lawmakers are bracing to do battle over in January. When they return from the holidays on Wednesday, lawmakers will begin trying to pass a federal budget in a fight likely to be linked to other issues, such as immigration policy, even as the November congressional election campaigns approach in which Republicans will seek to keep control of Congress. President Donald Trump and his Republicans want a big budget increase in military spending, while Democrats also want proportional increases for non-defense \u201cdiscretionary\u201d spending on programs that support education, scientific research, infrastructure, public health and environmental protection. \u201cThe (Trump) administration has already been willing to say: \u2018We\u2019re going to increase non-defense discretionary spending ... by about 7 percent,\u2019\u201d Meadows, chairman of the small but influential House Freedom Caucus, said on the program. \u201cNow, Democrats are saying that\u2019s not enough, we need to give the government a pay raise of 10 to 11 percent. For a fiscal conservative, I don\u2019t see where the rationale is. ... Eventually you run out of other people\u2019s money,\u201d he said. Meadows was among Republicans who voted in late December for their party\u2019s debt-financed tax overhaul, which is expected to balloon the federal budget deficit and add about $1.5 trillion over 10 years to the $20 trillion national debt.", "example_title": "Real Example"}, {"text": "Donald Trump Sends Out Embarrassing New Year\u2019s Eve Message; This is Disturbing [SEP] Donald Trump just couldn t wish all Americans a Happy New Year and leave it at that. Instead, he had to give a shout out to his enemies, haters and  the very dishonest fake news media.  The former reality show star had just one job to do and he couldn t do it. As our Country rapidly grows stronger and smarter, I want to wish all of my friends, supporters, enemies, haters, and even the very dishonest Fake News Media, a Happy and Healthy New Year,  President Angry Pants tweeted.  2018 will be a great year for America! As our Country rapidly grows stronger and smarter, I want to wish all of my friends, supporters, enemies, haters, and even the very dishonest Fake News Media, a Happy and Healthy New Year. 2018 will be a great year for America!  Donald J. Trump (@realDonaldTrump) December 31, 2017Trump s tweet went down about as welll as you d expect.What kind of president sends a New Year s greeting like this despicable, petty, infantile gibberish? Only Trump! His lack of decency won t even allow him to rise above the gutter long enough to wish the American citizens a happy new year!  Bishop Talbert Swan (@TalbertSwan) December 31, 2017no one likes you  Calvin (@calvinstowell) December 31, 2017Your impeachment would make 2018 a great year for America, but I ll also accept regaining control of Congress.", "example_title": "Fake Example"}], "pipeline_tag": "text-classification", "model-index": [{"name": "roberta-base-fake-news-detection", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer en dataset:mohammadjavadpirhadi/fake-news-detection-dataset-english license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #en #dataset-mohammadjavadpirhadi/fake-news-detection-dataset-english #license-mit #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-fake-news-detection
================================


This model is a fine-tuned version of roberta-base on the fake-news-detection-dataset-english dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0061
* Accuracy: 0.9992


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #en #dataset-mohammadjavadpirhadi/fake-news-detection-dataset-english #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #en #dataset-mohammadjavadpirhadi/fake-news-detection-dataset-english #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0### Training results### Framework versions


* Transformers 4.27.3
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2