
## Model description

BERT-based model for classifying fake news written in Romanian.

## Intended uses & limitations

It predicts one of six types of fake news (in order: "fabricated", "fictional", "plausible", "propaganda", "real", "satire").

It also predicts if the article talks about health or politics.

## How to use the model

Load the model with:

```python
from huggingface_hub import from_pretrained_keras

model = from_pretrained_keras("pandrei7/fakenews-mtl")
```

Use this tokenizer: `readerbench/RoBERT-base`.

The input length should be 512. You can tokenize the input like this:

```python
tokenizer(
    your_text,
    padding="max_length",
    truncation=True,
    max_length=512,
    return_tensors="tf",
)
```

## Training data

The model was trained and evaluated on the [fakerom](https://www.tagtog.com/fakerom/fakerom/) dataset.

## Evaluation results

The accuracy of predicting fake news was roughly 75%.
{"library_name": "keras"}
keras region:us
TAGS
#keras #region-us 


## Model description

BERT-based model for classifying fake news written in Romanian.

## Intended uses & limitations

It predicts one of six types of fake news (in order: "fabricated", "fictional", "plausible", "propaganda", "real", "satire").

It also predicts if the article talks about health or politics.

## How to use the model

Load the model with:



Use this tokenizer: 'readerbench/RoBERT-base'.

The input length should be 512. You can tokenize the input like this:



## Training data

The model was trained and evaluated on the fakerom dataset.

## Evaluation results

The accuracy of predicting fake news was roughly 75%.
## Model description

BERT-based model for classifying fake news written in Romanian. ## Intended uses & limitations

It predicts one of six types of fake news (in order: "fabricated", "fictional", "plausible", "propaganda", "real", "satire").

It also predicts if the article talks about health or politics. ## How to use the model

Load the model with:



Use this tokenizer: 'readerbench/RoBERT-base'.

The input length should be 512. You can tokenize the input like this: ## Training data

The model was trained and evaluated on the fakerom dataset. ## Evaluation results

The accuracy of predicting fake news was roughly 75%.
TAGS
#keras #region-us 
 ## Model description

BERT-based model for classifying fake news written in Romanian. ## Intended uses & limitations

It predicts one of six types of fake news (in order: "fabricated", "fictional", "plausible", "propaganda", "real", "satire").

It also predicts if the article talks about health or politics. ## How to use the model

Load the model with:



Use this tokenizer: 'readerbench/RoBERT-base'.

The input length should be 512. You can tokenize the input like this: ## Training data

The model was trained and evaluated on the fakerom dataset. ## Evaluation results

The accuracy of predicting fake news was roughly 75%.
passage: TAGS
#keras #region-us 
## Model description

BERT-based model for classifying fake news written in Romanian.## Intended uses & limitations

It predicts one of six types of fake news (in order: "fabricated", "fictional", "plausible", "propaganda", "real", "satire").

It also predicts if the article talks about health or politics.## How to use the model

Load the model with:



Use this tokenizer: 'readerbench/RoBERT-base'.

The input length should be 512. You can tokenize the input like this:## Training data

The model was trained and evaluated on the fakerom dataset.## Evaluation results

The accuracy of predicting fake news was roughly 75%.