readme: ---
  library_name: keras
  ---
  
  ## 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%.